Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It states it extracts signals and suggests questions, implying a read-only analysis. However, it does not explicitly confirm it is non-destructive or describe any side effects, leaving some ambiguity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise with two sentences. The first sentence states the action and outputs, the second provides usage guidance. Every sentence earns its place with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no output schema, the description gives a concrete list of extracted items (budget signals, timeline urgency, etc.) and suggests clarifying questions, giving a clear picture of what the output will contain. This is sufficient for understanding the tool's capabilities.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with a single parameter 'brief' described as 'The client brief, job post, or email thread to analyze'. The description adds context about what the analysis extracts but does not add new parameter-specific meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool analyzes a client brief before drafting, listing specific outputs like budget signals, timeline urgency, red flags, and clarifying questions. It distinguishes from sibling tools which are mostly email drafting and sending tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly advises using this tool first when a brief is vague or budget unclear, providing clear context for when to use. It does not explicitly mention alternatives or when not to use, but the guidance is helpful.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses that the email does not count against the monthly draft limit and positions as a strategic partner without hard-selling. However, it does not address other behavioral aspects like destructive actions, authentication needs, or whether the email is sent automatically or returned as text.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (few sentences) and well-structured: it states the purpose, lists content components, explains the strategic benefit, and notes a constraint (draft limit). Every sentence contributes valuable information without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given there is no output schema, the description provides a good overview of the email's structure and content. It covers the key elements a user needs to know. However, it could be slightly more complete by mentioning that the tool generates draft text (implying the output is the email body) or clarifying whether it saves the draft or returns it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, so the baseline is 3. The description adds little extra meaning beyond the schema; it provides usage examples (e.g., comma-separated deliverables) that are already in the schema descriptions. No significant new semantic value is added by the description text.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool writes an end-of-year review email to long-term clients, listing specific content sections (deliverables, result, reflection, suggestion). It distinguishes itself from transactional vendor emails, and among siblings like 'client_anniversary_email' or 'retainer_check_in_email', this tool's focus on annual review and strategic positioning is unique.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implicitly indicates use for end-of-year or end-of-engagement periods with long-term clients, and mentions it opens renewal discussions. However, it does not explicitly state when not to use this tool or provide direct alternatives among the many sibling email tools, leading to some ambiguity.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Since no annotations are provided, the description carries the burden. It discloses the email's tone ('warm, non-desperate') and that it doesn't count against draft limits, but lacks details on potential constraints like rate limits or dependencies.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences with no redundancy: first sentence defines the action, second provides rationale, third specifies constraints. Front-loaded and efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description fully covers the tool's purpose, audience, tone, word limit, nature of the ask, and billing exclusion. No output schema is needed given the straightforward email generation task.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with parameter descriptions. The tool description adds value by providing context on tone, word limit, and audience, which goes beyond the schema's parameter descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool generates an email to past clients about available capacity. It uses specific verbs ('Write') and resources ('email to past clients'), and is distinct from sibling tools like 'cold_pitch' or 'reactivation_email'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description advises use for past clients rather than cold pitching, providing context on when to use it. It mentions constraints like 'Under 120 words, one soft ask' and 'Does not count against your monthly draft limit', but does not explicitly list alternatives or when not to use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must disclose all behavioral traits. It describes the tone (confident, not desperate) and the fact it doesn't count against draft limits. However, it lacks details on output (draft vs send), authentication requirements, or rate limits. The behavioral disclosure is partial.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: it leads with purpose, contextualizes the value, gives tone guidance, then lists parameters. Every sentence is necessary and adds value. No redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 7-param tool without output schema or annotations, the description covers purpose, tone, parameter details, and a limit note. It is missing explicit description of the output (e.g., the generated email text). This is a minor gap but overall complete enough for an AI agent to use correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds value by grouping parameters as required/optional and providing examples (e.g., availability_window examples). However, it mostly reiterates the schema descriptions without adding significant new semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool writes a short, warm email to past clients/contacts announcing availability. It distinguishes from siblings like annual_review_email or bid_lost_follow_up by focusing on availability announcement. The high-ROI context further clarifies its specific use case.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides context on when to use (when you have upcoming availability) and notes it's high-ROI. It mentions it does not count against monthly draft limit. However, it does not explicitly exclude scenarios or mention alternatives among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses the tool's behavioral traits: it does not count against the monthly draft limit, imposes a 100-word body limit, and requires a gracious and brief tone. It does not mention any sending mechanism or permissions, but for a draft email tool, the provided transparency is sufficient beyond no annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a dense paragraph but well-structured with clear sections: purpose, tone/length, sibling distinctions, and parameter details. It could be slightly more concise (e.g., bullet points) but is efficient and front-loaded with the main purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, the description fully addresses the tool's complexity: it explains the scenario, required parameters, optional parameters with usage notes, constraints (100 words, tone), and relationship to siblings. No critical information is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description adds meaningful context for each parameter (e.g., 'reason_if_known: what they told you — used to tailor tone', 'future_work_angle: a specific type of future work you'd like to be considered for'). It clarifies optional usage beyond the schema's basic descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool creates a follow-up email after losing a bid, with a clear verb ('write'), resource ('professional follow-up email'), and context ('after you didn't win a competitive bid or pitch'). It also distinguishes this from sibling tools like cold_pitch_follow_up, client_followup, and no_response_closure_email.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use guidance ('after you didn't win a competitive bid or pitch') and when-not-to-use guidance by contrasting with specific sibling tools, stating their distinct contexts (e.g., 'no response to a cold pitch' vs. 'actively told you they went with someone else').

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses that the tool sends an email, includes a scope summary, and asks for confirmation. It also notes it does not count against a monthly draft limit. However, it does not clarify whether the email is actually sent or just drafted, nor does it mention side effects like saving drafts or authentication requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is detailed and well-structured, starting with purpose, then parameters, then workflow, then a practical note. However, it is somewhat verbose and could be trimmed for conciseness without losing essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description thoroughly covers the inputs and their usage, but it lacks information about the output (e.g., whether a draft or sent confirmation is returned). Given no output schema, this gap reduces completeness. It also does not mention error handling or what happens on success.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema covers all 9 parameters (100% coverage), and the description adds significant value by explaining the purpose of each parameter, providing examples, and specifying which are required versus optional, including how to omit irrelevant ones.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool writes and sends a confirmation email before work begins, using specific verbs and resource. It distinguishes from sibling tools by placing it in a workflow sequence: after discovery_call_follow_up_email and before project_kickoff_email.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context on when to use the tool (before work begins to prevent scope disputes) and includes a workflow diagram that implicitly indicates alternatives. However, it lacks explicit exclusions or 'when not to use' guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses that the tool does not count against the monthly draft limit and that it keeps the relationship warm, but does not clarify if the email is automatically sent or just drafted. The behavioral traits of the output (draft vs. send) are not fully specified.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two main sentences, front-loading the purpose and routes, then covering key details (distinctions, draft limit, required/optional params) efficiently. No superfluous text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of 8 parameters (4 required), no output schema, and no annotations, the description provides comprehensive context: explains all parameters' purpose, gives examples, names sibling alternatives, and clarifies a behavioral trait (draft limit). It is complete for the agent to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, but the description adds significant value by explaining the role of each parameter in context of the three routes (e.g., what_can_be_cut for scope_reduction, middle_ground_offer for middle_ground) and providing examples of acceptable inputs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool writes a professional email for a specific scenario (client budget shortfall) and outlines three strategic routes. It distinguishes itself from sibling tools by naming them (discount_request_response, competitor_response_email, change_order_email), making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells when to use the tool (when client budget falls short), the three response routes with brief explanations, and explicitly distinguishes from sibling tools, providing clear alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description bears full burden. It discloses the behavioral trait 'Counts against your monthly draft limit', which is useful. However, it does not mention whether the tool modifies an existing proposal, sends the proposal, or requires specific authorization. More detail on side effects would improve transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise at three sentences. It front-loads the condition and action, then adds value with strategy and policy info. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the use case and trigger well. However, it does not explain what the output is (e.g., a draft proposal text, an email), nor whether the tool sends or saves the proposal. Given no output schema, this gap limits completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds no additional parameter meaning beyond the schema; it only restates the general action. It does not clarify parameter formats or edge cases.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly specifies the tool writes a revised proposal with reduced scope when a client says the quote is too high. It distinguishes itself from siblings like 'discount_request_response' by explicitly stating 'not a rate cut', and from general proposal tools by the specific trigger scenario.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives a clear trigger condition: 'when a client says your quote is too high'. It implicitly advises against using for rate cuts. However, it does not explicitly mention when not to use or list alternative tools (e.g., 'discount_request_response' for discount requests).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It describes the email's purpose, structure, and tone, and notes that it does not count against the monthly draft limit. However, it is somewhat ambiguous whether the tool actually sends the email or just generates the text, which could be clarified.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph that is well-organized and informative, but slightly verbose. It efficiently conveys all necessary information without significant redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the 7 parameters, 3 required, and no output schema, the description comprehensively covers the tool's behavior, usage context, and output expectations. It includes comparison to siblings, structure guidance, tone, and a note about draft limits, making it fully adequate for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description adds value by explaining the context for parameters like 'reason' (one sentence max) and 'approval_needed' (default true). This goes beyond the schema definitions to guide the AI on proper usage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool writes an email about a budget increase due to underestimation. It explicitly distinguishes from sibling tools like scope_change_email and budget_proposal by specifying the scenario of an honest estimate correction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use guidance (honest update when estimate is off) and when-not-to-use (scope changes or budget proposals). It also outlines the recommended structure and tone, giving clear instructions for appropriate use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description bears the full burden. It discloses behavioral traits: parks the prospect warmly, acknowledges inquiry, explains capacity, gives availability if known, offers priority slot. Also mentions it doesn't count against monthly draft limit. Missing: whether it sends or just generates the email text.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two paragraphs with front-loaded purpose. Every sentence provides useful information, no fluff. Could be slightly more concise but well-organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers purpose, usage, parameters, and behavioral notes. Lacks explicit mention of output format (e.g., what the email text looks like) or error handling, but given no output schema, the description is fairly complete for a simple email generation tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and description adds valuable context beyond schema descriptions, e.g., for project_description it clarifies 'makes the email feel specific rather than a generic I'm busy note.' Provides usage nuance for optional fields like available_from and offer_priority_slot.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool writes a professional email to a prospect when fully booked, with a specific verb 'Write' and resource 'email', and distinguishes from sibling tools like client_decline_email and reactivation_email.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells when to use (fully booked but don't want to lose prospect), when not to use (permanent no), and names two alternatives (client_decline_email, reactivation_email). Also explains that it creates a warm pipeline rather than declining or going silent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It mentions the draft limit exemption but fails to disclose whether the tool is read-only or has other side effects, authentication needs, or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two concise sentences plus a single note. It is front-loaded with the core action and adds valuable context about output format and draft limits without any wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 7 parameters, no output schema, and moderate complexity, the description provides sufficient context about the output (challenge, approach, results, learnings) and the target user (freelancers). It is complete enough for the intended use case.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description lists outline sections like 'learnings' that are not parameters, adding slight context but not enhancing understanding of the actual parameters beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool transforms a completed project into a structured portfolio case study with specific outlined sections. It distinctly differentiates itself from the sibling tools, which are primarily email templates and proposal-related, by focusing on portfolio documentation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states the tool is for turning completed projects into case studies, noting it 'does not count against your monthly draft limit.' While it doesn't provide negative guidance or alternatives, the sibling context makes the use case clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description carries full burden. It explains the tool creates a professional document, requires sign-off, and protects from scope creep. However, it does not disclose what happens after creation (e.g., download, send, store) or any side effects. The behavior is generally safe but lacks full transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with four sentences, front-loading the main action. Every sentence adds value: purpose, content of document, benefit, and note about draft limit. Could be slightly more streamlined, but overall well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description should explain what the tool returns. It states it writes a document but doesn't specify format (e.g., text, PDF, download link). Given 6 parameters and the tool's nature, the description covers the input well but leaves output unclear.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds minimal extra meaning beyond schema: it mentions 'additional cost and timeline impact' which map directly to parameters already well-described in the schema. No significant additional semantics provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: writing a professional change order document when a client requests work outside the original project scope. It uses specific verbs and resource (change order document) and distinguishes from sibling tools like scope_change_email or scope_clarification_email by focusing on document generation rather than email communication.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says when to use this tool (when client requests work outside original scope) and mentions it requires client sign-off. However, it does not mention when not to use it or provide alternatives among siblings, such as scope_change_email for simpler notifications. Still, the context is clear enough for most agents.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full transparency burden. It notes the tool does not count against monthly draft limit and that it generates an approval request. However, it does not clarify whether the email is drafted or sent, leaving minor ambiguity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Highly concise: 5 sentences covering purpose, benefit, sibling distinction, behavioral trait, and parameter listing. No wasted words, well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Provides sufficient context for a simple email generation tool: purpose, usage, key parameters, and an important behavioral note. Lacks explicit mention of output format (draft vs. sent), but overall adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with all parameters described. Description adds value by listing required/optional groups but does not significantly augment schema descriptions. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool writes a change order email to confirm additional work with scope, cost, and timeline impact, and asks for written approval. It explicitly distinguishes from sibling tool scope_creep_email, providing a specific verb and resource.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use: for agreed extra work, and identifies alternative tool (scope_creep_email) for unwanted additions. Provides clear guidance on context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It discloses the three routes with different tones, that it does not count against monthly draft limit, and explains the purpose of each parameter. However, it does not explicitly state that it only generates the email text and does not send it, which is a minor gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured: starts with main purpose, then lists routes, differentiation, and parameter details. It is informative but not overly verbose. Could be slightly more concise, but front-loads key information effectively.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 8 parameters, 2 required, no output schema, and no annotations, the description covers all necessary aspects: explains the three routes, gives examples, differentiates from siblings, and lists required and optional parameters with explanations. It is complete for an email generation tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds value by grouping parameters, explaining the route enum with examples, and providing context for each parameter (e.g., why you need access_reason). This extra semantic richness justifies a 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool writes an email to request access to client systems, tools, etc. It specifies the verb 'Write', the resource, and distinguishes between three routes. It also differentiates from sibling tools like client_material_chase_email and project_restart_email, making its purpose highly specific and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'For when you need logins, permissions, API keys, or repository invitations from the client side' and provides three distinct routes (initial_request, follow_up, partial_workaround) with clear contexts. It also contrasts with sibling tools, giving clear when-to-use and when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so description carries full burden. It discloses that the tool generates a non-upsell, human-toned email under 100 words with optional personalization fields. States it does not count against monthly limit. No mention of side effects or authentication, but for a generation tool this is sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is roughly 150 words, well-structured with front-loaded purpose and differentiation. Every sentence adds information without redundancy. Minor length could be trimmed, but overall efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, description adequately explains the nature of the output (email under 100 words, warm, no pitch). It covers usage, parameter nuance, and differentiation. Could include edge cases like milestone format flexibility, but not essential for basic usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers 100% of parameters with descriptions. The description adds value by explaining how optional parameters (project_or_relationship, standout_moment, forward_line) contribute to specificity and warmth, going beyond basic schema definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool writes a short, warm anniversary email for long-term clients, distinguishes from sibling annual_review_email, and specifies constraints (no pitch, no ask, under 100 words). The verb 'Write' plus resource 'email marking anniversary' makes it unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly contrasts with annual_review_email, stating this is relationship-first with no deliverables or pitch. Provides when-to-use (anniversary acknowledgment) and when-not-to (structured retrospectives). Also mentions it doesn't count against monthly draft limit, adding practical context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. Mentions it doesn't count against draft limit, but does not disclose whether it saves or stores data, or if it has side effects beyond generating text.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with main purpose and is reasonably concise at ~160 words, though some phrases like 'prevents scope creep and 'bad brief' problems' could be trimmed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers use case, timing, and parameters well. However, lacks description of output structure (e.g., format of the questionnaire) despite no output schema, which would help an agent understand what to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for each parameter. The overall description adds context like defaults and format options, but does not provide significant additional meaning beyond what's in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Generate a structured client brief questionnaire' with a specific verb and resource, and distinguishes it from sibling tools by clarifying its use at the start of an engagement before proposals.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit guidance: 'Use this at the very start of an engagement — before drafting a proposal or setting a price.' Lacks explicit alternatives but context implies it's for new client onboarding.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. Describes email content and tone but does not explicitly state whether the tool sends the email or just generates draft. Ambiguous about saving vs outputting.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences, front-loaded with main purpose. Efficiently conveys core info without fluff. Slightly verbose in some phrases but overall well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Provides good usage context and distinction from sibling, but lacks output format details (plain text, subject line, etc.) since no output schema. Does not clarify save/send behavior, leaving gaps for agent invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% with detailed param descriptions. The tool description adds overall context (tone, word limit) but does not significantly enhance parameter understanding. Baseline 3 due to high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states it writes a short proactive check-in email during long projects, distinguishes from project_status_update (light vs full report), and specifies word limit and purpose. Clear verb+resource with sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells when to use (mid-phase, silent execution periods), when not to (not a full weekly report), and mentions draft limit policy. Directs to project_status_update for alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must cover behavioral traits. It mentions the tool generates an email, explains the routes, and notes it does not count against a draft limit. However, it doesn't disclose that the output is a draft requiring user action, or describe any side effects, which would be helpful for a generation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but well-structured: purpose first, then routes, then parameters. Every sentence adds value. Minor redundancy exists in repeating the three routes in both the overview and parameter section, but overall it is appropriately sized for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 9 parameters, multiple routes, and no output schema, the description covers purpose, usage guidelines, parameter semantics, and a behavioral note (draft limit). It lacks an explicit statement about the output format (a draft email), but the tool's nature is well implied. It is sufficiently complete for an AI agent to use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining how each parameter maps to the three routes (e.g., what_went_wrong for acknowledge, your_response for dispute), integrating them into the usage narrative beyond the schema's individual descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses specific verbs ('write a professional email responding') and clearly identifies the resource (client complaint email). It distinguishes from creative feedback, which is a sibling tool, and outlines three distinct routes (acknowledge, dispute, resolve), making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use (client complaint/serious dissatisfaction) and distinguishes from creative feedback. Provides context on the three routes with default recommendation. However, it could be more explicit about specific scenarios where other sibling tools are preferred over this one.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description adds valuable behavioral context: the email is warm and respectful, preserves relationships, and does not count against monthly draft limit. It implies the tool generates a draft rather than sending it.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is moderately concise, with the core purpose front-loaded. Each sentence adds value, though some minor redundancy (e.g., 'warm and respectful' repeated) could be trimmed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Without an output schema, the description covers essential aspects: purpose, scenarios, tone, optional referral, and draft limit. It does not explicitly state the output format (e.g., plain text), but the overall context is adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, but the description enriches parameter meaning by explaining the four decline reasons (capacity, not_fit, timing, budget) and the optional referral, adding context beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool writes a professional email declining a client project inquiry, covering four specific situations (capacity, not_fit, timing, budget). It distinguishes from sibling tools that handle other email types (e.g., bid_lost_follow_up, client_followup).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains when to use the tool: when you can't or shouldn't take work, with scenarios listed. It implicitly indicates not to use for other purposes, but lacks explicit exclusions or alternative tool names.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden. It details behavioral traits: three response modes with clear definitions, no destructive actions, and the fact that it does not count against monthly draft limit. No contradictions with annotations (none provided).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized, front-loading the purpose, then explaining modes, distinguishing from sibling, and adding rationale and a bonus behavioral trait (no draft limit). Every sentence adds value without waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (7 parameters, 3 modes, no output schema), the description covers purpose, usage context, sibling differentiation, mode details, and a behavioral bonus. It is complete enough for an agent to understand when and how to invoke the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and includes detailed descriptions for each parameter, including enum values. The tool description explains the three modes, which is already covered in the schema's enum description. While it adds context about when to use each mode, it does not add significantly new semantic meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: writing professional replies to critical/negative mid-project feedback. It specifies three response modes and explicitly distinguishes it from the sibling tool 'revision_response_email' for specific change requests.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly explains when to use this tool versus the sibling 'revision_response_email' by contrasting feedback types (qualitative/directional vs. specific change requests). It also advises against common pitfalls (defensiveness, over-apologizing) and promotes the intended professional path.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Description goes beyond annotations (none provided) by stating the output is short, non-pushy, and designed to reopen conversation. Does not mention side effects or limitations, but tone and intent are clear.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, no wasted words. Front-loaded with purpose, then behavioral promise, then input requirements. Efficient and easy to grasp.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

While the description covers purpose and inputs, it does not specify the output format or how the generated message is returned. For a generating tool, this is a missing detail.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and description repeats the need to provide summary and days. No additional meaning beyond schema, so baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool writes a follow-up message for a proposal with no response, using specific verb-resource combo. It stands out from siblings like `proposal_to_email` or `contract_unsigned_follow_up` which handle different scenarios.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says to provide proposal summary and days since sent, giving clear context for use. However, no guidance on when not to use or comparison to alternative tools like `bid_lost_follow_up`.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Despite no annotations, the description fully discloses key behaviors: tone calibration (friendly/firm/escalation), making impact clear without blame, and that it does not count against monthly draft limit. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Five well-structured sentences, each adding value: purpose, usage, tone, sibling distinctions, and behavioral note. Front-loaded with main action. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 8 parameters (3 required), no output schema, and no annotations, the description thoroughly covers purpose, usage, behavioral nuance, and parameter explanations. It provides sufficient context for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with detailed parameter descriptions. The overall description adds context on tone calibration and usage, but the individual parameter descriptions already provide semantics. Slightly redundant but still valuable.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the tool writes a professional email for chasing overdue materials/content/approvals. Distinguishes from siblings like project_delay_warning, payment_reminder_email, and third_party_delay_email, providing specific verb+resource and context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly describes when to use: when client hasn't delivered committed items and delay blocks work. Mentions tone calibration based on overdue days and names alternative tools for different scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It discloses that the tool does not count against monthly draft limit and implies it creates an email draft. Additional behavioral details like permissions or side effects are not needed for a write tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Compact and front-loaded: purpose, usage context, sibling differentiation, and a behavioral note in three sentences. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description clearly implies the tool returns an email draft. It covers purpose, scope, and differentiation from siblings. Slight gap: does not explicitly state the output format, but it's well-understood as an email generation tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds context about the overall structure but does not enhance individual parameter definitions beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool writes a structured end-of-engagement email with a handover checklist. It distinguishes from two sibling tools (client_offboarding_email and project_closure_email) by specifying this is an operational handover with action items and checklist format.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Sent at project close or retainer end' and distinguishes from sibling tools, providing clear when-to-use and when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must convey behavior. It adds value by noting 'Does not count against your monthly draft limit', but it does not disclose whether the email is sent or just drafted, or any other side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with key information front-loaded: purpose, sibling distinction, tone guidance, and draft limit detail. It is efficient but slightly verbose in the middle.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 7 parameters and no output schema, the description covers purpose, usage, tone, and draft limit context. It lacks explicit mention of output format but is otherwise complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description does not elaborate on parameters beyond what the schema already provides, adding no extra meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool writes an email ending a client relationship, specifies the relationship type (retainer, long-term engagement), and explicitly distinguishes from project_closure_email, leaving no ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear usage context ('for when you are choosing to end the relationship') and names an alternative (project_closure_email). However, it omits exclusions and does not reference other relevant sibling tools like client_decline_email.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It discloses an important cost behavior: 'Does not count against your monthly draft limit.' This is a valuable behavioral trait beyond the standard generative function. No contradictions with annotations since none exist.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences, each serving a distinct purpose: what it does, how it adapts, when to use, and a behavioral note. No wasted words, front-loaded with core function. Excellent structure.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers main function, adaptation, and timing, but lacks details on output format (e.g., bulleted list, plain text) and edge cases. Given no output schema, a brief format hint would improve completeness for an agent to expect the response type.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for all 4 parameters. The description reinforces that the checklist adapts by project_type and is tailored, but adds no new constraints or format details beyond the schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the verb 'Generate' and the resource 'tailored list of everything you need from a client', specifying it adapts by project type. This distinguishes it from sibling tools that send emails or proposals, as it focuses on creating an actionable checklist for client onboarding.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit instruction: 'Send this after the kickoff email, before starting work.' This provides clear context for when to use the tool, but does not mention specific alternatives or when not to use it, which would elevate it to a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must convey behavior. It explains that the email 'makes the ask easy: it gives context on the prospect, sets expectations on time commitment, and makes it simple to say yes or no'. It also notes the draft limit exemption. However, it does not disclose any authentication requirements, whether the email is sent automatically, or other side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph that efficiently front-loads the core purpose, then adds differentiation, context, and a key behavioral note (draft limit). It is concise without unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description explains what the email does and its context. It sets expectations for what the tool produces (an email that includes context, time commitment, and an easy yes/no). It is sufficient for an agent to understand the tool's output.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description does not add parameter-specific meaning beyond the schema, but the schema descriptions themselves are detailed. The overall description provides context but not parameter-level detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool's verb and resource: 'Ask a trusted past client to be a named reference for a specific prospect'. It immediately distinguishes from three sibling tools (testimonial_request, recommendation_request_email, referral_request) by contrasting their purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly compares to alternative tools and provides context on when to use each. Also notes that the tool 'does not count against your monthly draft limit', providing a practical rule for usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description fully covers behavioral traits: tone (warm, brief, non-pushy), handling of happy vs. unsatisfied clients, and optional parameters' effects. No contradictions with missing annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured: first sentence states core purpose, then details tone, sibling distinctions, and parameter descriptions. No wasted words, all sentences add value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, description fully covers tool's purpose, usage, parameters, and behavior. It's complete for correct selection and invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All 6 parameters are described in the schema, and the description adds meaning: explains survey_link omission leads to direct reply, testimonial_ask adds a sentence, outcome_note personalizes email, etc.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it's for writing a post-completion feedback email, distinguishing it from similar tools like bid_lost_follow_up, referral_thank_you, and cold_pitch_follow_up. Uses specific verb+resource structure.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly describes when to use (after completing a project) and names three sibling tools it's distinct from. Also notes it does not count against monthly draft limit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the tone (factual, non-accusatory) and a benefit (no draft count), but does not describe the output format, whether it sends the email, or any side effects. The behavior is mostly clear as a text generator, but details are missing.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences with no wasted words. It front-loads the purpose, then tone, then benefit. Efficient and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the use case and tone but does not explain the return value (presumably the email text). With no output schema, the agent might need explicit indication of what the tool returns. While the complexity is low, the lack of return specification is a minor gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and schema descriptions are already clear. The description adds useful context, e.g., that 'days_waiting' calibrates tone, which is not in the schema. This extra meaning justifies a score above the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it writes a professional email to a client who hasn't delivered promised items, blocking the project. It distinguishes from many siblings by specifying the client's delay as the cause, but it doesn't explicitly differentiate from other client-facing emails like 'client_check_in_email' or 'client_followup', so a 4 is appropriate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use (project blocked by client delay) and sets tone expectations, but it lacks explicit guidance on when not to use or alternatives. For a tool among many similar email templates, this is a gap.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Without annotations, the description carries full burden. It discloses that the tool does not count against the monthly draft limit, a useful billing behavior. However, it does not describe other behavioral traits such as AI generation, storage, or read/write nature, which limits full transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is four sentences, each contributing distinct information: clear purpose, differentiation from inbound, stylistic guidance, and billing note. No redundancy or filler, achieving high conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple text generation tool with 6 parameters and no output schema, the description covers the essential usage context. It could mention the output format (a draft email) but the name and description make that obvious. It is adequately complete for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by noting that the 'ask' parameter defaults to 'a 15-minute call' and provides stylistic guidance for the overall email. This extra context lifts the score above baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses the specific verb 'Write a cold outbound pitch email' and identifies the resource as a potential client who hasn't contacted you. It explicitly distinguishes from 'inbound proposal work' and sibling tools like 'cold_pitch_follow_up', making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly states when to use the tool (for proactive business development to non-contact clients) and contrasts it with inbound proposals. It also provides stylistic guidance (short, specific, single easy ask) and a billing hint (doesn't count against draft limit). It lacks explicit 'when not to use' but is sufficient for clear usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description covers behavioral aspects: it generates a concise follow-up, resurfaces key hook, gives an easy out, asks yes/no, and mentions the draft limit. Lacks mention of any potential side effects or constraints beyond that.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is front-loaded with purpose and key guidelines. Every sentence adds value; no redundancy. Efficiently communicates usage, tone, and sibling distinction.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description still explains enough about output (short follow-up). Parameters are well-explained, and sibling tools are clearly differentiated. Completeness is high for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, baseline 3. The description adds context for days_since_pitch (calibrates tone) and new_angle (optional addition). Also clarifies that company_name personalizes subject line and original_pitch_summary is a one-sentence summary.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it writes a short professional follow-up for unanswered cold pitches. Distinguishes from client_followup and win_back_email by specifying the exact scenario (genuine cold silence).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly defines when to use (cold pitch unanswered) and when not (post-proposal follow-up, re-engaging lapsed clients). Additionally provides a behavioral hint about not counting against draft limits.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Without annotations, the description carries full burden. It details the three modes, their appropriate contexts, and behaviors to avoid. It also notes that the tool does not count against the monthly draft limit. Missing info on authentication or side effects, but these are less critical for a drafting tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph but front-loads the purpose and key modes. Every sentence adds value, though it could be more structured with bullet points. It's not overly verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (three modes, 8 parameters), the description covers behavioral guidance, parameter semantics, and a note on draft limits. No output schema exists, so the description adequately implies the output (a drafted reply). It addresses likely agent confusion about which mode to use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the purpose and nuance of each mode, giving examples for differentiator, and clarifying how optional parameters affect output (e.g., omitting competitor_name keeps reply vague). This goes beyond the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states it writes a professional reply when a client compares prices. This clearly differentiates it from sibling tools like discount_request_response or bid_lost_follow_up by targeting a specific scenario.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use each of the three response modes and when not to (e.g., 'do not use just to close a deal' for match_with_context). It also clarifies what behaviors are avoided (caving, defensiveness). However, it does not explicitly mention alternative tools for related scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It describes the high-stakes sales moment, relationship risk, and three response routes. However, it does not explicitly state whether the tool sends the email or just drafts it, though the phrase 'does not count against your monthly draft limit' implies a draft is generated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is thorough but not wasteful; each sentence adds value. It front-loads the purpose and then provides detailed guidance. Could be slightly more concise, but the complexity of the tool (three routes) justifies the length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description should clarify what the tool returns (e.g., an email draft). It hints at 'does not count against your monthly draft limit' but does not explicitly state the output format or how to use the result. For a tool with multiple routes and high-stakes context, this is a gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 100% coverage with descriptions for each parameter, but the tool description adds rich context with concrete examples for condition_requested and counter_offer, and explains the route enum options with usage scenarios. This goes beyond the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: generating responses when a client says 'yes, but...' and asks for changes. It distinguishes from three sibling tools (discount_request_response, budget_negotiation_email, price_objection_response_email) by explaining the specific scenario where this tool applies.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly specifies when to use (client accepted but requests changes) and when not to (alternatives listed with clear differentiators). Also provides guidance on the three routes (accept, counter, hold) with conditions for each.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses one behavioral trait (no impact on draft limit) but does not detail output format, whether it actually submits the pitch, or any side effects. The description adds moderate value beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (three sentences) with the main purpose front-loaded. The marketing sentence about public speaking adds context but is not strictly necessary; overall efficient without being verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 7 parameters and no output schema, the description adequately explains what the tool generates and the required inputs. However, it lacks details on the return format or any post-generation steps (e.g., copying to clipboard). It is minimally complete for a generation tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the description adds minimal extra meaning beyond the parameter descriptions. It reinforces that key_takeaways should be comma-separated but does not provide new semantic context. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool writes a speaker submission for conferences, meetups, or podcasts, specifying the output (abstract, takeaways, bio) and format (CFP). This distinguishes it from sibling tools like cold_pitch or linkedin_post, which serve different content generation purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides context on when to use it—for CFP submissions as a high-authority marketing move—and includes a note about not counting against monthly draft limits. However, it lacks explicit guidance on when not to use it or alternatives (e.g., for non-talk pitches).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It discloses a key behavioral trait: 'Does not count against your monthly draft limit', which is valuable for resource management. It also clarifies the tone ('without being heavy or legalistic'). However, it does not explicitly state whether the tool returns the email text or sends it, nor does it discuss authentication or rate limits, but the context (cover email) implies text generation, which is reasonable.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core action: 'Write the short covering email...'. The description is moderately concise, with each sentence providing distinct value. It could be slightly tighter (e.g., combining some optional parameter notes), but overall well-structured and efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description acknowledges the output implicitly (the email). It covers purpose, audience, content, and key parameters. With 5 parameters (1 required) and no nested objects, the description is sufficiently complete for an agent to select and use the tool correctly, though it could explicitly state the return format.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, baseline 3. The description adds meaning by explaining each optional parameter in context: e.g., `project_description` is 'brief note on the project the NDA covers', `relationship` defaults to 'client', `signing_method` gives examples. This adds practical guidance beyond the schema descriptions, enhancing usability for an AI agent.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it writes a covering email to accompany an NDA, using specific verbs ('write') and resource ('covering email'). It distinguishes from sibling tools `nda_template` (generates the NDA) and `subcontractor_brief` (briefs a subcontractor), giving a unique identity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says when to use: sending NDA to client or subcontractor. Provides when-not-to-use by naming alternatives (`nda_template` for the NDA itself, `subcontractor_brief` for briefing). Also explains what the email covers (why sending, what it covers, what to do) without being legalistic, guiding the agent on appropriate context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses that the email opens the renewal conversation, references work done, and does not count against the draft limit. It explains the two routes and optional parameter behavior, offering good transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two paragraphs, front-loaded with purpose. It includes some narrative detail ('Most freelancers...') which adds context but could be trimmed. Overall, every sentence contributes value, and it is well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 8 parameters, 2 required, and no output schema, the description is thorough. It covers all parameter roles, default behavior, optionality, and route implications. The agent has sufficient information to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% (baseline 3). The description adds valuable context beyond schema, such as explaining optional fields like end_date ('makes the ask feel timely'), work_summary ('reminds the client of value'), and new_rate behavior when omitted ('proposes discussing updated terms'). This enhances understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool writes a professional contract or retainer renewal email. It specifies the verb 'write' and the resource 'renewal email,' and distinguishes itself from siblings by focusing on renewals. The two routes (same_terms/revised) provide specificity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains when to use this tool ('before an engagement ends') and provides context about common freelancer mistakes. It describes the two routes but does not explicitly exclude alternatives or state when not to use it. However, given the large sibling set, the differentiation is implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description adds good behavioral context: it explains the email's content structure and optional parameters' effects (e.g., signing_deadline changes sign-off). It does not detail any side effects, but the tool is a content generator with no destructive actions implied.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences, front-loaded with purpose, no fluff. Every sentence adds essential information about the tool's function and distinctiveness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a content generation tool with no output schema or annotations, the description provides a solid understanding of what the email contains and the optional inputs' behavior. It could be improved by explicitly stating the output (e.g., 'returns the email text').

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining how optional parameters affect the email content (e.g., signing_deadline omission changes sign-off, signing_link presence changes CTA), which goes beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool's action ('Write the short covering email') and resource ('contract or agreement'), and distinguishes it from the sibling tool 'contract_template' by clarifying that this is the email wrapping the contract, not the document itself.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly explains what the email does and differentiates it from 'contract_template', providing clear context. However, it does not explicitly mention when not to use it or provide alternatives among other sibling tools like follow-up emails.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It discloses that the tool generates a full working contract in plain English and specifies it does not count against a draft limit. It does not detail any destructive effects or authentication needs, but the generation behavior is sufficiently clear.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, front-loaded with the primary action, followed by differentiation and suitability. Every sentence adds value without redundancy. It is highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, the description adequately explains what the tool produces (a complete contract) and covers its main features. Given the parameter richness and sibling context, the description is complete enough for an agent to understand and use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage for all 8 parameters, with clear descriptions for each. The tool's description does not add additional meaning beyond listing some contract areas, which are already implied by the parameter descriptions. Thus, baseline score is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool generates a 'Freelance Services Agreement' and lists the areas it covers (services, payment, IP, revisions, termination, liability). It distinguishes itself from sibling tools by noting it is more comprehensive than an NDA and more legally framed than a SOW.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides context for when to use this tool (standard freelance/consulting engagements) and contrasts it with related tools (NDA, SOW). It also mentions it does not count against a monthly draft limit. However, it does not explicitly state when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully discloses the tool's behavior: it generates a short professional email with specific tone constraints. It reveals that the tone is calibrated by days_since_sent parameter, that it avoids guilt, and that it includes an easy out. Also discloses that it does not consume monthly draft limit. No contradictions or omissions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is detailed but efficient, front-loading the core purpose and then differentiating with siblings. It uses clear, direct language. A few extra phrases ('the hardest follow-up to write because it feels pushy') add empathy but don't waste space. Could be slightly shorter, but well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 5 parameters, no output schema, and many sibling tools, the description provides enough context to use correctly: it explains when to use, tone, length constraint, and parameter usage. It does not describe the return format, but that's acceptable as output is presumably the generated email text. A minor gap: no mention of how the email is returned (draft, copy, etc.), but still complete enough.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% with each parameter already well-described (e.g., client_name, days_since_sent for tone calibration). The tool description adds no additional parameter-level meaning beyond what the schema provides. Baseline of 3 is appropriate per guidelines.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool writes a professional follow-up email when a client has agreed but not signed. It explicitly distinguishes itself from three sibling tools by naming their specific contexts (never replied, proposal not approved, closing out ghost). The verb 'write' and resource 'follow-up for unsigned contract' are precise and unique.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use: 'after the handshake: they said yes, you sent the contract, now it's sitting unsigned.' Also tells when-not-to-use by contrasting with sibling tools. Includes tone guidance (matter-of-fact, no guilt, under 100 words, gives easy out) and a system behavior note (doesn't count against draft limit). Comprehensive and actionable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description indicates a destructive action ('remove') but lacks details on irreversibility, permissions, or side effects. With no annotations, more transparency is needed for a deletion tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with no unnecessary words, efficiently conveying the tool's purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is adequate for a simple tool but lacks behavioral context (e.g., permanence of deletion). The abundance of sibling tools suggests more contextual guidance could be beneficial.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, and the description adds no additional meaning beyond what the schema already provides for the 'name' parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Remove'), the resource ('saved proposal'), and the context ('reference library'), distinguishing it from sibling tools like 'save_proposal' and 'list_proposals'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit when-to-use or when-not-to-use guidance is provided. The usage is implied by the tool's name and description, but alternatives or exclusions are not mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It describes the email's content, tone, and required/optional fields. However, it doesn't clarify whether the tool drafts or sends the email, which is a minor gap. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise yet comprehensive; every sentence adds value. Front-loaded with purpose and usage, then parameter details. No fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Complete for a text-generation tool with no output schema. Covers why, when, what params, and alternatives. No gaps in explaining the tool's role.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All 7 parameters are described in the schema with 100% coverage. The description adds value by listing required params, providing examples and defaults for each optional param, and giving practical usage tips beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool writes an email asking for sign-off on completed deliverables, and explicitly distinguishes it from three sibling tools (milestone_delivered_email, project_closure_email, project_completion_email) with concise differences.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use context ('before you close the project or send the final invoice'), contrasts with siblings, and includes practical notes like 'Does not count against your monthly draft limit.'

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must carry the full behavioral burden. It discloses the email's tone ('confident and clear, not apologetic') and that it doesn't count against draft limits, but does not explain whether the email is sent directly or returned as text, nor any side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is four sentences, each adding essential information: purpose, tone, timing, flexibility, and a benefit. No wasted words, and the most important info is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite 8 parameters and no output schema, the description does not explain the return value (presumably email text) or how parameters interact. The detailed parameter schema compensates, but the tool lacks a complete picture for the agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all 8 parameters thoroughly. The tool description adds no additional meaning or usage guidance beyond the schema definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool writes a deposit request email, specifies the timing (before work begins, after contract/SOW), and distinguishes it from other email templates with its unique focus on deposits.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says when to use the tool ('fills the gap between signing the contract/SOW and starting work') and notes it works for any deposit amount. However, it does not mention when not to use it or compare to sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations, so description carries full burden. It adds the note about not counting against monthly draft limit, plus explains the three modes' behaviors. Could mention return format, but that's minor.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences, each earning its place: purpose, stakes, how it works, benefit, and side-effect. Front-loaded and zero fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given complexity (6 params, 3 modes) and no output schema, description covers usage, parameter guidance, and behavioral note. It's complete enough for an agent to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers 100% of parameters with descriptions, so baseline is 3. Description adds value by contextualizing the response_mode enum and explaining when to use each mode, which complements schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool writes a response to discount requests, with three specific modes. It distinguishes from siblings by focusing on this precise use case, unlike general decline or negotiation emails.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'when a client asks for a lower price', giving clear context. Lacks explicit alternatives or when-not-to-use, but the context is strong and sibling list suggests suitable cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description fully discloses behavioral traits: it produces an under-150-word email, does not count against monthly draft limits, and follows a specific structure. No contradictions with missing annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is informative but slightly lengthy; it could be tightened without losing clarity. However, it is well-structured and front-loads the core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and moderate complexity, the description fully explains the tool's output (email), its workflow position, and behavioral constraints, making it complete for an agent to use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All 5 parameters are described in the schema (100% coverage), and the description adds significant meaning beyond the schema, such as auto-formatting for what_discussed and fallback behavior for confirmed_next_step.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool writes a follow-up email after a discovery call, providing a specific verb and resource. It distinguishes itself from siblings by explicitly differentiating from meeting_request_email and project_kickoff_email.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use guidance (within 24 hours of a discovery call) and identifies alternative tools for different contexts, including the workflow sequence: meeting_request_email → call → this email → draft_proposal.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description explains the behavioral logic: assumes good faith on first no-show offers reschedule, and politely closes on second no-show. However, it does not explicitly state whether the tool sends the email directly or outputs draft text, leaving a minor ambiguity about its exact action.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: starts with core purpose, adds context, differentiates from siblings, and lists parameters with defaults. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and few sibling complexities, the description covers purpose, usage, parameters, and behavioral nuances comprehensively. It even notes the draft limit exemption, which adds helpful context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, the baseline is 3, but the description adds substantial meaning with examples (e.g., call_time: 'today at 2pm', no_show_count: defaults and scenario outcomes, reschedule_link: default behavior). This goes well beyond the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool writes an email to a prospect who missed a discovery call. It explicitly distinguishes from siblings like meeting_cancellation_email, meeting_postponement_email, and cold_pitch_follow_up, and outlines the two scenarios (first no-show vs second no-show).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool vs alternatives by naming siblings and their contexts. It also notes that it does not count against a monthly draft limit, providing additional usage guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided. The description reveals that the tool is a non-destructive generation tool (does not count against draft limit) and produces structured output. It does not disclose any side effects, permissions, or rate limits, but the generative nature is conveyed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is exceptionally concise: two sentences that cover purpose, inputs, outputs, workflow position, and a key behavioral note. Every word adds value, making it easy for an AI agent to quickly understand the tool's role.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (3 parameters, no output schema), the description provides sufficient context: what it generates, its place in a workflow, and a non-standard feature (no draft limit). It could be more complete by specifying the output format, but the listed outputs (questions, agenda, confirmation items) are clear enough for an agent to expect appropriate results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All three parameters are described in the input schema with clear descriptions (brief, analysis, your_service). The description adds a usage hint for analysis and your_service but does not add significant new semantic detail beyond what the schema provides. With 100% schema coverage, a score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: preparing for a discovery call. It specifies the input (brief) and the generated outputs (sharp questions, call agenda, confirmation items). It also positions the tool in the workflow between analyze_brief and draft_proposal, distinguishing it from siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool ('Use between analyze_brief and draft_proposal') and notes that it does not count against the monthly draft limit, which is helpful for resource planning. It lacks explicit 'when not to use' guidance, but the context provides a clear usage scenario.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains that the tool produces an invoice (not a cover email or reminder), outputs Markdown, and includes a professional header, line items, subtotal, optional tax, and total. It does not mention any destructive actions or side effects, which is appropriate for a document generation tool. A minor gap is the lack of explicit statement about whether it modifies any records, but the context implies it is a pure generation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is approximately 6 sentences, each conveying essential information without redundancy. It is front-loaded with the primary action, specifies the output format, distinguishes from siblings, and summarizes required/optional parameters. No fluff – every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (11 parameters, nested array for line_items) and lack of output schema, the description is remarkably complete. It explains the output format (Markdown) and content (header, line items, subtotal, tax, total). It also covers usage context and parameter guidelines. No gaps are apparent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. However, the description adds significant value beyond the schema: it explains the purpose of each optional parameter (e.g., 'Omit to leave a placeholder' for invoice_number, 'Omit for fixed-fee items' for quantity/rate), provides examples for date formats, and clarifies the line_items structure. This makes parameter semantics very clear.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it generates a complete, ready-to-send professional invoice document in Markdown format, and distinguishes itself from three sibling tools (invoice_cover_email, invoice_reminder, payment_plan_proposal). The verb 'Generate' and resource 'invoice document' are specific and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides context for when to use the tool ('Ideal for converting an accepted proposal into a billable document') and lists required and optional parameters. It explicitly distinguishes from sibling tools, though it does not explicitly state when not to use it (e.g., avoid for sending reminders). Still, the guidance is clear and helpful.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries burden. It discloses that it uses saved winning proposals as references and mentions rate limits (free plan: 5 drafts/month). However, it doesn't specify if the proposal is automatically saved or how output is delivered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, no fluff. First sentence states purpose, second adds context, third mentions limits. Efficient and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, but description says 'Returns a ready-to-send proposal', which is sufficient for an agent to understand the result. Lacks details on output format or how to access the proposal, but overall context is adequate given the simple parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so parameters are already well-documented. Description adds no additional meaning beyond the schema; it mentions 'brief' but doesn't elaborate on budget, deadline, your_rate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool drafts a proposal from a brief using saved winning proposals as style references, distinguishing it from siblings like analyze_brief (analysis only) and budget_proposal (budget-specific).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Description implies when to use (when you have a brief and want a proposal) and mentions style references, but lacks explicit exclusions or alternatives like 'use analyze_brief if you only need analysis'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses that the tool does not count against monthly draft limit and explains the two routes and their outputs. While it could state whether it drafts or sends the email, the behavioral details are sufficient for an email generation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose and scenario. Every sentence adds informative value, from the common mistake warning to the route explanation. It is efficiently structured without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 7 parameters (2 required) and no output schema, the description covers all parameters with examples and context. It differentiates from a large set of sibling tools effectively. The email output is intuitive, so no further completeness is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the description adds value beyond the schema by explaining the routes in detail ('full_delivery... confident announcement...', 'early_preview... inviting feedback...'), providing concrete examples for deliverables, and clarifying optional parameters like original_deadline and feedback_window with usage context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with a clear verb+resource: 'Write the email when your work is ready ahead of the agreed deadline.' It explicitly distinguishes this tool from siblings like project_completion_email, project_handover_email, and milestone_approval_request_email, providing precise differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool (work ready ahead of deadline) and when not to, contrasting with siblings. It also describes two routes (full_delivery and early_preview) with specific use cases, giving clear guidance on scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses the tool's behavior: it writes an email, has three modes, and does not count against a monthly draft limit. It does not mention auth requirements or rate limits, but those are unlikely for a text generation tool. The behavioral context is adequate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured, starting with the main purpose, then detailing modes, required/optional params, and tone advice. It is slightly lengthy but every sentence adds value. A 5 would require more conciseness without losing clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 6 parameters, 3 modes, and no output schema, the description is quite complete: it covers all parameters with examples and defaults, explains the three modes, and provides professional best practices. It does not describe the return format, but that is implicitly the email text. A 5 would require more explicit handling of edge cases or output specification.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining the reason parameter's enum values, giving examples for engagement_description and handover_note, and clarifying defaults (reason defaults to natural_end). This extra context justifies a 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool writes a professional email to end a client relationship or retainer, and distinguishes three specific modes (natural_end, capacity, fit_mismatch) with distinct use cases. This makes the purpose highly specific and differentiates it from sibling tools like client_check_in_email or project_pause_email.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.