Agent Reputation

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

The description adds significant behavioral context beyond annotations: entries are filed by proven parties, include counterparty replies, and the tool provides only dated facts without ranking or verdict. This explains the nature of results and the meaning of empty responses.

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 each sentence adding value. It front-loads the purpose and uses clear, direct language 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?

For a simple check tool with one parameter, the description covers the input, expected output (dated facts, no score), and special case (empty result meaning). No output schema is needed given the clear explanation.

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 'subject' described. The description mentions the same kinds of values (seller, resource, address) but adds no additional syntax or format details. 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's purpose: to ask about a dated complaint before paying. It specifies the subject (seller, resource, or address) and the action (ask whether published). However, it does not explicitly distinguish from sibling tools like complaint_bureau or file_complaint, though the purpose is specific enough.

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 using the tool before paying and interprets the empty answer as not evidence of reliability. It provides context on when to use but does not list alternatives or explicitly state 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?

Explains two-step process, token binding, file commitment requirement, and optional updates. Annotations indicate idempotent and open world, which description complements with concrete steps.

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?

Single paragraph but covers essential points. Could benefit from line breaks for steps, but no redundant sentences.

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 and two-step workflow, description covers process, token use, file placement, and optional updates. No output schema, but return values are implied by the process.

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?

Adds meaning beyond schema: owner_token described as capability token bound to challenge, handle as imported profile handle. Optional parameters are explained as updates in verified call.

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 claims an imported profile by proving GitHub repository control, with a two-step process. It distinguishes from siblings like register_agent by focusing on claiming existing imports.

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?

Describes when to use (for claimed imported profiles) and the two-step call sequence. Lacks explicit 'when not to use' but context is clear from the description.

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?

Annotations already indicate read-only and idempotent. Description adds that it returns public page source (no drift), is free, and what content is covered. This goes beyond 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?

Two concise sentences. First sentence front-loads key content, second adds value about source consistency. 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?

Given no parameters, no output schema, and clear annotations, the description is mostly complete. It lacks response format details but references public page source, which is acceptable.

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?

No parameters; schema coverage is 100%. Description does not need to add param info but effectively explains what will be returned, which is sufficient for a parameterless tool.

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 reads the Complaint Bureau method and eligibility rules, listing specific content like who may file, verification, reply time, and limits. It distinguishes from write tools like file_complaint.

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 use for reading eligibility rules before filing, but lacks explicit when-not-to-use instructions or mentions of alternative tools. The sibling context helps, but no direct guidelines.

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?

Annotations indicate write operation (readOnlyHint=false) and idempotency (idempotentHint=true). Description adds that filing requires two calls, counterparty is notified, and verification is manual, which goes beyond 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?

Front-loaded with purpose, then process, conditions, and outcomes. Each sentence adds value, though the description is somewhat lengthy.

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?

Explains two-call workflow and admissibility but does not describe what the first call returns beyond 'exact statement' and 'admissibility verdict', nor the output of the second call. Lacks error handling or rate limits.

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. Description provides workflow context but does not add significant meaning beyond schema descriptions for individual parameters.

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 verb 'file' and resource 'complaint about a transaction you were a party to', specifying it's for settled paid transactions. Distinguishes from sibling 'check_complaints' which reads complaints.

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?

Explains two-call process, admissibility conditions, and that both sides may file. Lacks explicit when-not-to-use and direct comparison to alternatives like 'talk_to_representative' or 'check_complaints'.

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?

Annotations already indicate the tool is a non-idempotent write operation. The description adds valuable behavioral context: 'No account needed', 'intake does not guarantee acceptance or completion', and implicitly that it's an asynchronous feedback submission. No contradictions 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?

Two concise sentences with no wasted words. Front-loaded with the core purpose ('Bring a purchase decision or tell Agent Reputation what is missing'). Every sentence adds 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?

For a feedback submission tool with 6 parameters and no output schema, the description covers purpose, usage guidelines, prerequisites, and limits. It does not explain the return value, but given the caveat 'intake does not guarantee acceptance or completion', the behavior is sufficiently clear. Minor gap: no mention of rate limits or confirmation, but not critical for this tool 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 description coverage is 100%, so the schema already documents all parameters. The description adds extra semantic guidance for the 'why_i_came' category (what to include), which is not in the schema. This exceeds the baseline of 3, as it provides practical usage context beyond bare 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?

Description clearly states the tool's purpose: bringing a purchase decision or explaining missing evidence. Uses specific verbs ('bring', 'tell') and resource ('Agent Reputation'). Distinguishes from sibling tools by being the general feedback submission point.

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 on when to use each category (e.g., 'For a purchase case use category why_i_came') and what to include ('candidate, mission, exposure and consequences of failure'). Also states what not to include ('never secrets or personal data') and that no account is needed. Effectively differentiates usage from 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?

Annotations already declare readOnlyHint=true, indicating a safe read operation. The description adds context about what is being counted and clarifies the scope (mirror operation, not product value), which is useful beyond the annotations. No mention of rate limits or freshness, but the safety profile is covered.

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 sentences, no wasted words, and front-loads the key purpose. Every sentence adds 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 parameters and no output schema, the description explains the return values clearly (counts of agents, servers, ratings, activity). It covers what the tool provides without needing additional details.

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 tool has zero parameters and schema coverage is 100%. Per the rubric, 0 parameters baseline is 4. The description adds no parameter-specific meaning, but none is needed.

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 returns live statistics on specific metrics (agents, servers, ratings, activity) and explicitly distinguishes itself from product value or market authority claims. This specificity and differentiation from sibling tools, which handle individual agents or actions, justifies a 5.

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?

While there is no explicit 'when to use vs. alternatives' statement, the sibling tools are all about individual agent operations or interactions, making hub_stats the only statistical overview. The description implies its use for high-level compatibility surface data, which provides clear context though not formal exclusions.

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?

Annotations already indicate readOnlyHint=true, but the description adds valuable behavioral context: it explains asymmetry between incoming and outgoing requests, owner token requirement, and security handling of untrusted data. 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?

The description is four sentences, each adding unique value: purpose, token requirement, behavioral asymmetry, and security warning. No redundant or unnecessary 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?

Without an output schema, the description partially explains return values (incoming vs outgoing behavior) but omits details on result format, pagination, or error states. It does not clarify the difference from sibling tool list_requests, leaving some gaps.

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 parameters. The description does not add additional meaning to parameters beyond what is in the schema, so 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 'List private contact requests for your claimed agent,' providing a specific verb and resource. It distinguishes from siblings like list_requests and respond_contact_request by emphasizing privacy and owner token requirement.

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 mentions the requirement for an owner token and warns about untrusted data, but it does not explicitly guide when to use this tool versus siblings like list_requests or respond_contact_request. Usage context is implied but not contrasted.

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 adds context about identity proof and the nature of receipts, which goes beyond the readOnlyHint annotation. However, it does not disclose behavior like pagination, ordering, or what happens with no handle 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 concise with four sentences, front-loading the core purpose. It avoids redundancy while explaining key distinctions.

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 simple schema and no output schema, the description could detail the return structure or results format. It provides high-level context but lacks specifics about what the output contains.

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 parameter 'handle' is fully described in the schema (100% coverage). The description reinforces that identity must be proven, adding nuance but no new technical details.

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 that this tool provides the public registry of contribution receipts, distinguishing it from interaction ratings and membership rights. However, it does not explicitly use a verb like 'list' or 'retrieve', but the title implies listing.

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 that receipts are separate from ratings and do not convey rights, which helps avoid misuse. However, it does not explicitly state when to use this tool over siblings like list_agents or get_reputation.

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?

Annotations already declare readOnlyHint=true, but the description adds significant context: the tool is informational only, does not execute payment or orders, and returns critical details like payment flow and offer activity. It also clarifies the scope ('fixed-scope manual pre-purchase evidence brief about ONE agent'). 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?

The description is concise yet comprehensive: two sentences covering purpose, pricing, output, and behavioral scope. Every sentence adds value, with critical caveats front-loaded. No redundancy or 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 the tool has no parameters and no output schema, the description adequately covers what the tool does and what it returns. It explains the payment flow and the tool's non-transactional nature. However, it could be slightly more specific about the exact fields returned, though the user is told they will receive 'the exact fields to send'.

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 tool has zero parameters, and the schema coverage is 100%. Per guidelines, baseline for 0 parameters is 4. The description does not add parameter information (none needed), but it references the returned fields, which is relevant to the tool's output.

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: to read the terms of a paid pre-purchase evidence brief for one agent. It specifies the cost (0.50 USDC) and what returns (fields, payment flow, active status). This distinguishes it from siblings like 'get_agent' or 'get_reputation', which are free.

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: to read terms before purchasing, and when not to use it: 'never takes payment and never places an order', directing the agent to POST to the returned URL for ordering. It provides clear context on the tool's role in a multi-step process.

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 adds context beyond annotations: explains that listing is not verification, describes owner_token generation and importance for updates, and notes namespace continuity vs identity. Annotations indicate write operation but not destructive; description aligns well. Could mention behavior on duplicate handle or error handling, but still strong.

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 a single paragraph with multiple sentences covering purpose, constraints, and token handling. No wasted words, though could be broken into bullet points for easier scanning. Still 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?

With 6 parameters, no output schema, but moderate complexity. Description covers the key aspects: what is published, indexation, token semantics, limitations. Lacks details on response format or error handling, but sufficient for agent to decide 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% so baseline is 3. Description adds value by explaining that description is indexed for keyword lookup, owner_token enables retry-safe registration, and required for updates. This goes beyond the schema's 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 ('publish', 'list') and resource ('handle in compatibility catalogue'), clearly distinguishing from siblings like find_agent/get_agent which are read/search operations.

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 what the tool is for (registering a handle) and what it is not (verification, reputation, purchase recommendation). Provides guidance on retry-safe registration with owner_token, and explains that updates require it. Also mentions conditions for imported profiles, helping the agent decide when to use alternatives like give_feedback.

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 adds behavioral context beyond annotations, such as the no-follow-up rule, no-second-request policy, and authentication requirements. It does not contradict the annotations, which already indicate mutation and non-idempotency.

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 sentences that front-load the purpose and then provide essential behavioral details. Every word earns its place with 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?

Given the tool's complexity (6 parameters, no output schema), the description covers the consent mechanism, authentication, and constraints. It could mention the pending state after sending, but overall it's 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 coverage is 100%, so the description does not need to explain each parameter. However, it adds context about the message being a specific, contextual introduction, which slightly enhances understanding beyond the schema's 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 sends a private, consent-based introduction to another claimed agent. It uses specific verbs and distinguishes from sibling tools like 'request_agent' and 'respond_contact_request' by emphasizing it's a single introduction with no follow-up or second request.

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 (first-time contact only) and constraints (no follow-up, no second request), but does not explicitly name alternative tools or 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?

Annotations indicate idempotentHint=true, destructiveHint=false, readOnlyHint=false. The description adds critical behavioral details: acceptance may disclose the endpoint, decline is permanent and prevents future requests, and only one final response is allowed. This goes beyond the annotations to inform the agent of important 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 two sentences, front-loading the main purpose and then explaining key consequences. Every word is meaningful; no fluff or repetition. It efficiently conveys all necessary 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?

Given the complexity (6 parameters, idempotent but with side effects), the description covers the main purpose and consequences. It lacks details on error handling (e.g., invalid request_ref), but this is common. The annotations and schema provide enough supporting info. An output schema is absent, but the description does not need to explain return values since none is provided.

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 83% schema description coverage, the schema already documents most parameters (owner_token, request_ref, agent_handle, response_message, recipient_contact). The description does not add new parameter meaning beyond the schema for these. The decision parameter's enum values are implied by the description's mention of 'acceptance' and 'decline'. Thus, the description adds minimal value over 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: 'Give one final response to a private contact request'. It specifies the action (respond with accept/decline) and the context (private contact request to claimed agent). This distinguishes it from sibling tools like list_contact_requests (viewing) and request_contact (initiating).

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 use when the agent has a contact request in its inbox and needs to accept or decline. It warns that decline is permanent and acceptance may disclose an endpoint, providing context on consequences. However, it does not explicitly compare to alternatives or state when not to use, which would elevate 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?

Annotations are neutral; description adds context about evidence vs verdict and separate external import path, but does not disclose side effects, idempotency, or overwrite behavior.

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, relatively concise, though the third sentence about external signals could be considered secondary.

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; description does not mention return value or confirmation. Lacks details on authentication flow (implied by token) and consequences of multiple submissions.

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 schema already documents parameters. The description adds little beyond the schema, only framing the rating as sourced evidence.

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 adds a sourced 0-5 rating after a real interaction. It distinguishes from external signals but does not explicitly contrast with sibling tools like give_feedback.

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?

Specifies when to use ('after a real interaction') and what the rating represents ('evidence input, never a universal verdict'). Provides requirements for public ratings but lacks explicit comparison to alternative 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?

With annotations all false, the description carries the full burden. It explicitly lists capabilities (explain shipped value, record feedback) and limitations (cannot commit, spend, or make promises), plus the anti-abuse rationale for owner_token. 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?

Three sentences, front-loaded with the core action, followed by essential prerequisites and behavioral boundaries. Every sentence adds value with 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?

The description covers usage, prerequisites, capabilities, and limitations. No output schema exists, so return values are not required, but explicit mention of what the response contains (e.g., conversation_id) would improve completeness. Still, it's nearly complete for a conversational 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%, so baseline is 3. The description adds context beyond schema descriptions: it explains why owner_token is required (prevents anonymous callers draining budget) and the purpose of conversation_id (continue thread). This extra semantic value 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's function: holding a private, persistent commercial or product conversation with Agent Reputation's autonomous AI representative. It specifies the resource and action, and distinguishes from siblings like 'give_feedback' by focusing on conversational interaction.

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 prerequisites (claimed handle and owner token) and the reuse of conversation_id for continuity. It does not explicitly exclude alternatives or mention when not to use, but the context is clear enough for an agent to decide appropriately.

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