PM-Skills MCP

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

No annotations are provided, so the description carries full burden. It details the return format ('Markdown formatted cache statistics') and the metrics included (hit rate, cache size, TTL settings). It could confirm the operation is read-only, but this is strongly 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?

The description is three short sentences, each earning its place: purpose, metrics, usage context, and return format. No fluff, 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?

For a simple read-only stats tool with no parameters, the description covers purpose, usage, and return format. It could mention whether the data is real-time, but overall it is sufficient 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?

The input schema has zero parameters, so schema coverage is 100% trivially. The description adds value by explaining the return format, which compensates for a lack of output schema. No parameter information 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 opens with 'Get PM-Skills cache statistics', which is a specific verb+resource combination. It clearly distinguishes from sibling tools, none of which are cache-related.

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 states the tool is 'Useful for monitoring server performance and debugging cache behavior', providing clear context. However, it does not explicitly mention when not to use it or alternatives, though none exist 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?

No annotations are provided, so the description carries the full burden. It only mentions the return format (Markdown) and resource URIs, but does not disclose side effects, authentication needs, rate limits, or other behavioral traits expected for a read operation.

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: two sentences plus a returns line. Every sentence adds value without waste, and the core purpose 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?

For a simple list-all tool with no parameters and no output schema, the description adequately states what is returned and the format. However, it lacks details on error conditions or pagination, but these are less critical for a basic list operation.

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 is empty with 0 parameters, and schema coverage is already 100%. The description adds no parameter information, which is acceptable given no parameters exist. Baseline score of 4 applies.

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 'list all available persona library entries', uses a specific verb and resource, and distinguishes from sibling tools like 'pm_list_skills' by mentioning 'persona library entries' and the resource URI pattern.

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 does not provide any guidance on when to use this tool versus alternatives, nor does it mention when not to use it. It simply states what it does without context relative to other 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?

Without annotations, the description carries the burden. It discloses the return format (Markdown list) and states it lists 'all' prompts, but does not discuss side effects, prerequisites, or pagination. Adequate for a simple read-only listing.

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 efficiently cover purpose, context, usage, and output. No fluff; 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 the tool's simplicity (no parameters, no output schema), the description is mostly complete, though it could mention whether the list is exhaustive or has any ordering convention.

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 no parameters and 100% schema coverage, the description adds no parameter info, which is acceptable. Baseline 4 for zero-parameter tools.

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 all available PM-Skills MCP prompts' with a specific verb and resource, distinguishing it from siblings that list skills, personas, resources, or workflows.

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 ('discover available prompts') and directs users to invoke prompts via 'prompts/get', providing clear context though not explicitly ruling out 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 carries the full burden. It explains the return format (categorized Markdown list with URIs) and mentions how resources can be accessed via resources/read. No side effects or destructive actions are expected for a list operation.

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 sized with a clear structure: statement of purpose, URI patterns in bullet points, and expected return. Each sentence adds value, though it could be slightly more concise.

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 adequately explains the return value (categorized list of resources with URIs). It covers usage context and expected output, making it complete for an agent to understand the tool's behavior.

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 no parameters, and schema description coverage is 100%. The description adds value by documenting the output format and URI patterns, which is beyond what the schema provides. Baseline for zero params is 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 lists all available PM-Skills MCP resources, specifying the verb 'List' and resource 'all available... resources'. It distinguishes from siblings like pm_list_skills and pm_list_personas by being all-encompassing.

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 does not provide guidance on when to use this tool versus alternatives like pm_list_skills or pm_list_personas. It lacks explicit when-to-use or when-not-to-use instructions, relying on implicit differentiation.

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 states that the tool returns a 'Markdown formatted list of all skills with their tool names and descriptions.' This is transparent about the output format and behavior, with no side effects 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?

The description is exceptionally concise with only three sentences. Each sentence adds value: purpose, usage guidance, and return format. 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 and no output schema, the description fully covers what the tool does, why to use it, and what it returns. The context of sibling tools (e.g., pm_search_skills) is implicit, but still 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?

There are zero parameters, so the baseline is 4. The description adds no parameter information because none exist, which 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 lists all available PM-Skills tools, categorized by workflow phase and classification. It uses specific verbs ('List all available') and identifies the resource. However, it does not explicitly differentiate from the sibling tool pm_search_skills, which could cause confusion.

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 includes a direct usage guideline: 'Use this tool to discover which skills are available before invoking specific skill tools.' This provides clear context on when to use it. No exclusion criteria are given, but for a listing tool, this is adequate.

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, but the description discloses the return format (Markdown formatted list) and implies a read-only operation. For a zero-parameter list tool, this is adequate 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—three short sections—with the core purpose first, then explanation, usage guidance, and return format. Every sentence serves a 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?

For a simple list tool with no parameters and no output schema, the description is complete: it covers purpose, usage context, and output format. It doesn't discuss edge cases, but that's acceptable given the tool's simplicity.

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, so schema coverage is 100%. The description adds value by explaining what workflows are (pre-defined sequences) and giving examples, beyond the empty 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 'List all available PM-Skills workflows' with a specific verb and resource. It distinguishes from sibling tools like pm_list_skills (lists skills) and pm_workflow_* tools (specific invocations).

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 to use this tool before invoking a specific workflow tool, providing clear guidance on when to use it and implying when not to (if you already know the workflow).

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 takes full responsibility for behavioral disclosure. It explains the search scope (names, descriptions, content), the effect of the searchContent parameter, and the return format (Markdown list with relevance). This is sufficient for a read-like operation.

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 three focused sections: main purpose, Args list, and Returns. Every sentence contributes value without redundancy or filler.

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 tool's purpose, parameters, and return format adequately. However, it could be more complete by mentioning result limits or pagination, though these are minor omissions for a search 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 for both parameters is 100%, so the description adds minimal value beyond restating the schema. The 'Returns' line provides output format information but does not enhance parameter 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 it searches PM-Skills by keyword across skill names, descriptions, and content. This specific verb and resource effectively distinguish it from sibling tool pm_list_skills, which likely lists all skills.

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 usage for finding specific skills via keyword search, but it does not explicitly state when to use this tool over alternatives like pm_list_skills or other search tools. No when-not guidance is provided.

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 implicitly indicates this is a read-only validation operation by stating it returns a result. Without annotations, it carries the behavioral transparency burden well, though it could be more explicit about having no 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 a clear purpose sentence, followed by parameter and return value descriptions. It is concise without extraneous information, though the parameter section is somewhat redundant with the schema.

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 tool with two parameters and no output schema, the description fully covers what the tool does, its inputs, and its output format. No additional information is needed for correct 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 coverage is 100% with clear descriptions for both parameters. The tool description adds minimal additional meaning beyond repeating parameter purposes, meeting the baseline for 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 clearly states the tool validates a PM artifact against its skill template, specifying the verb and resource. It distinguishes from sibling tools like pm_list_skills and pm_search_skills, which have different 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?

No guidance is provided on when to use this tool versus alternatives, such as when to validate versus simply listing or searching skills. The description lacks explicit usage context or 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?

No annotations are provided, so the description must carry the full burden. It mentions 'Effort Level: standard' and describes the output format, but does not disclose side effects, idempotency, rate limits, or error conditions, leaving significant behavioral aspects unaddressed.

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 a clear purpose, effort level, steps list, and usage instruction. It is slightly verbose but every section adds value, earning a high score for conciseness and front-loading.

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 tool returns a workflow plan, and the description covers input parameters, steps, and execution instructions. It lacks details on interpreting the plan or error handling, but given the absence of an output schema, the description is fairly complete for its purpose.

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 both parameters already described. The description repeats the schema ('topic (string, required)', 'context (string, optional)') without adding new semantics, achieving a baseline score 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 the tool's purpose: 'Customer Discovery workflow - Transform raw research into a clear, validated problem worth solving.' It lists the specific steps and differentiates from sibling workflows like pm_workflow_feature_kickoff by its unique name and step sequence.

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 includes 'Use this tool to get a complete workflow plan' which indicates a clear context for use. However, it lacks explicit guidance on when not to use it or alternatives among the many sibling workflows, leaving differentiation largely 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?

The description explains the tool returns a Markdown workflow plan with steps and guidance, and outlines the steps. No annotations are provided, but the description covers the main behavior. It lacks mention of authorization or side effects, but for a plan-generating tool, this is acceptable.

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, well-structured with bullet points for steps, and front-loaded with the 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?

For a moderate-complexity workflow plan tool, the description covers the workflow steps, parameters, and return value. No output schema exists, but the description adequately explains what is returned. Complete given 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?

Parameter schema coverage is 100% and both parameters are described in the schema. The description repeats the parameter info but adds no extra semantics beyond the schema. 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?

The description clearly states it is a complete experimentation workflow from hypothesis through learning capture, and explicitly mentions use to get a workflow plan. It lists specific steps, distinguishing it from sibling workflow 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 says 'Use this tool to get a complete workflow plan' and that the AI orchestrates execution, providing clear usage context. However, it does not explicitly state when not to use or compare to 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 carries the full burden. It transparently states the output is a 'Markdown workflow plan with steps, guidance, and execution instructions' and lists the five steps. No destructive behavior is implied, and the read-only nature is 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 well-structured with sections (Effort Level, Steps, Args, Returns) and is front-loaded with purpose. It is concise enough but could be slightly shorter; however, 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?

For a multi-step workflow tool, the description is complete: it explains the workflow purpose, steps, effort level, execution pattern (AI orchestrates), parameters, and return type. No output schema exists, but the description adequately describes the return format. It lacks details on error handling or step dependencies, but these are beyond typical scope.

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 parameters are already well-documented. The description repeats the parameter definitions ('topic' and 'context') but adds only minimal context beyond the schema, such as grouping them under 'Args.' No additional syntax or format details are 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: 'Complete workflow for kicking off a new feature, from problem definition through implementation-ready requirements.' It lists specific steps and distinguishes from sibling workflows by focusing on a standard feature kickoff, unlike other specialized workflows like lean startup or customer discovery.

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 states when to use: 'Use this tool to get a complete workflow plan' and notes that the AI should orchestrate execution. However, it does not explicitly compare to or exclude other workflow tools, leaving the agent to infer when to choose this one over siblings like pm_workflow_lean_startup or pm_workflow_triple_diamond.

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 the burden. It states it returns a Markdown plan without side effects. While adequate, it could be more explicit about being a non-destructive read-only plan generation.

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 with clear sections: purpose, effort level, steps, args, returns. Front-loaded with purpose. Could be slightly more concise but no wasted 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?

For a workflow plan generator with 2 parameters and no output schema, the description adequately explains the output (Markdown plan) and the steps involved. Complete enough for an agent to use appropriately.

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 3 applies. Description adds minimal new meaning beyond the schema: 'subject or feature' for topic and 'additional context' for context. No extra detail on format or constraints.

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 names the workflow as 'Lean Startup Validation' and explains it as a 'Build-Measure-Learn cycle', providing specific steps. This distinguishes it from other workflow siblings which have different 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 states 'Use this tool to get a complete workflow plan' and describes orchestration by calling step tools in sequence. Lacks explicit when-not-to-use but provides sufficient context 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?

No annotations are provided, so the description carries the full burden. It discloses that the tool returns a Markdown workflow plan and describes the effort level as 'comprehensive.' It also lists the steps and what the plan includes. There is no mention of destructive actions or auth needs, which is acceptable for a workflow planning 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 well-structured with a bolded title, bulleted steps, and clear sections for arguments and returns. It is front-loaded with the purpose. While it could be slightly more concise (e.g., the 'Steps' list could be inline), it remains easy to scan.

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 tool has 2 parameters (one required), no nested objects, and no output schema. The description explains the return value (Markdown workflow plan) and provides execution guidance (AI orchestrates steps). This is sufficient for a workflow 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% (both parameters have descriptions). The description adds context beyond the schema by explaining 'topic' as 'The subject or feature for this workflow' and 'context' as 'Additional context, constraints, or requirements.' This reinforces the schema but does not introduce new syntax 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 it is a 'Post-Launch Learning workflow' for setting up measurement, evaluating results, and capturing learnings after a feature ships. It lists specific steps (pm_instrumentation_spec, pm_dashboard_requirements, etc.), distinguishing it from sibling workflows like pm_workflow_feature_kickoff or pm_workflow_lean_startup.

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 says 'Use this tool to get a complete workflow plan' and implies post-launch context. It notes that the AI orchestrates execution by calling each step's tool. However, it does not explicitly state when not to use this tool or mention alternative workflows for different phases.

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 behavior. It describes the tool as returning a Markdown workflow plan and lists steps. It mentions 'Effort Level: comprehensive' and that the AI client orchestrates execution, but does not discuss side effects, permissions, or if any data is modified. Given the tool is a planner, the description is adequate but not thorough.

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 a header, effort level, steps list, args, and returns. It is front-loaded and each section is concise. Minor redundancy in the args section (repeats schema) 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?

For a workflow tool, the description lists all steps, arguments, and the return format (Markdown plan). It provides enough context for an agent to understand what the workflow entails. Without an output schema, the description compensates by describing the output. However, it does not specify what each step returns or how they combine.

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% for both parameters. The description repeats the parameter names and descriptions from the schema, adding that 'context' is optional and the return format. This adds minimal value beyond the schema, so a 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?

The description clearly states it is a 'Product Strategy workflow' to 'frame a major strategic initiative with competitive context and documented decisions'. The steps (e.g., pm_competitive_analysis) distinguish it from sibling workflow tools like pm_workflow_feature_kickoff or pm_workflow_lean_startup.

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 says 'Use this tool to get a complete workflow plan' and that 'The AI client orchestrates execution'. It implies usage for product strategy initiatives but lacks explicit guidance on when to use this vs. sibling workflows 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?

No annotations provided, so description carries full burden. It discloses that the tool returns a workflow plan and that steps are executed sequentially by the AI client. However, it does not mention side effects, authentication needs, or potential limitations beyond the plan generation.

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 a header, effort level, steps, usage note, arguments, and return format. It is clear and front-loaded, though it could be slightly more concise by avoiding repetition of the schema.

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 explains the return type (Markdown workflow plan) and the orchestration model. For a workflow tool with no output schema, it provides sufficient context for an AI agent to use it correctly, especially given the sibling tools for different workflows.

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 both parameters described. The description repeats the schema info in an Args block without adding significant new meaning. Baseline of 3 is appropriate as schema already documents the 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?

The description clearly states the tool's purpose: 'Quick PRD workflow - Streamlined workflow for rapidly creating a PRD when the problem and solution are already well-understood.' It distinguishes from sibling workflow tools by specifying the quick, well-understood 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?

The description explicitly states when to use: 'when the problem and solution are already well-understood.' The sibling list provides context for alternatives, but no explicit when-not-to-use or alternative names are given.

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 discloses that the tool returns a workflow plan (not executing steps), includes steps, effort level, and execution instructions ('The AI client orchestrates execution by calling each step's tool in sequence'). 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 highly structured: title, effort level, numbered steps, usage instruction, args, returns. Each sentence serves a purpose without fluff. Information is front-loaded with 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 the tool's complexity (workflow orchestrator with multiple steps) and lack of annotations and output schema, the description fully covers: what it does, what it returns (Markdown plan), how it works (orchestration), and parameter details. No 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 coverage is 100%, so baseline is 3. The description restates the two parameters with nearly identical descriptions from the schema, adding minimal extra meaning (e.g., 'from a backlog or PRD'). Does not provide format or syntax details beyond 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 'Sprint Planning workflow - Prepare sprint-ready stories with edge case coverage from a backlog or PRD.' It specifies the verb (prepare) and resource (sprint-ready stories), clearly distinguishing it from sibling workflow tools like pm_workflow_feature_kickoff or pm_workflow_quick_prd.

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 says 'Use this tool to get a complete workflow plan' and explains the AI orchestrates execution by calling tools in sequence. It provides clear context but does not explicitly state when not to use it or name alternatives. However, the context is sufficient for a workflow orchestrator.

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 fully convey behavioral traits. It describes the return format but does not disclose side effects (e.g., read-only, no resource creation) or any constraints beyond parameters. This is inadequate for a tool that returns a plan.

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 reasonably concise and well-structured with effort level and steps. Some redundancy exists (e.g., 'Use this tool to get a complete workflow plan' overlaps with the first sentence), but it remains clear and scannable.

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 moderate complexity and no output schema, the description explains the return format and steps. However, it lacks usage context relative to sibling workflows and does not detail the structure of the returned Markdown plan, leaving gaps for an 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% for both parameters (topic and context). The description repeats the schema's parameter descriptions without adding new meaning, so it meets the baseline but provides no extra value.

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 provides a Stakeholder Alignment workflow plan and builds a case for leadership buy-in. However, it does not distinguish this workflow from sibling workflow tools like pm_workflow_feature_kickoff or pm_workflow_lean_startup, which serve different 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 offers minimal guidance on when to use this tool. It says 'Build a compelling case for leadership buy-in before committing resources,' but no explicit criteria for selection over alternatives or exclusions are given, which is insufficient given many sibling workflow 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 provided, so description carries full burden. It discloses the tool returns a plan and the AI client orchestrates execution, not performing actions directly. It also notes effort level and steps, making behavior 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?

Description is well-structured with sections, effort level, and numbered steps. Every sentence is informative with no redundancy or wordiness. Front-loaded with 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?

Two parameters are sufficiently described. The return value is specified as a Markdown workflow plan. The description explains the execution model and step sequence. Minor gap: no details on output structure, but it's adequate for a workflow 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?

Input schema coverage is 100%, and description repeats parameter explanations exactly as in schema. It does not add new meaning or constraints beyond the schema's own descriptions, so baseline score of 3 applies.

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 evaluates technical feasibility and documents architecture decisions. It provides a specific verb and resource. However, it does not differentiate from sibling workflow tools, so it lacks explicit distinction.

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 says 'Use this tool to get a complete workflow plan' and implies technical discovery context, but it does not provide explicit when-to-use vs alternatives or when-not-to-use. Sibling workflow tools exist without comparative 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?

With no annotations, the description carries full burden. It explains that the tool returns a Markdown workflow plan and that the AI orchestrates steps, but does not disclose behavioral traits like side effects or prerequisites. As a read-only plan generation tool, this is adequate but could be more explicit.

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 clear sections: title, effort level, steps, usage instruction, args, returns. It is front-loaded and concise, though slightly verbose in listing steps.

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 explains the return format (Markdown workflow plan). It also lists steps and their optionality, providing sufficient context for a workflow planning 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 parameters are well-documented in the schema. The description repeats the parameter names and brief descriptions but adds no significant new 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 identifies the tool as a comprehensive end-to-end workflow following the Triple Diamond framework, listing its steps. This distinguishes it from sibling workflow tools that use different frameworks.

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 states to use it to get a complete workflow plan and explains that the AI client orchestrates execution. It provides context on when to use it but does not explicitly mention when not to use it or compare to alternatives.

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