capmus-mcp

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

No annotations are provided, so the description fully discloses behavior: creates a draft, returns continue_url, payment_required implies posting plan, publish sends a one-click link. It also warns about security of continue_url.

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

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

The description is thorough and front-loaded, but slightly verbose. Every sentence adds value, though could be tightened without losing 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 7 parameters, no output schema, and no annotations, the description covers the entire workflow: draft creation, payment handling, publication options, and security concerns. It is complete and actionable.

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%, but the description adds value: e.g., 'Never invent or guess [email] - ask the user.', explains when price is required, and clarifies publish parameter behavior.

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 creates a DRAFT listing on Capmus on behalf of the poster, specifying the verb 'create' and resource 'draft listing'. It distinguishes from siblings like search_listings or send_message, which perform different 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?

The description provides explicit guidance: 'Any email is accepted — never ask the user to qualify first.' It explains when to use publish: true vs. the wizard, handles payment_required scenarios, and instructs not to share the continue_url.

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 data is from the public page and includes no personal information. However, it omits error behavior, auth requirements, or rate limits, leaving gaps for a mutationless read 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?

Two concise sentences plus a short instructional sentence about surfacing the trust signal. Every sentence adds value; no fluff.

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

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

Given the tool's simplicity (one param, no output schema), the description covers the key points: what it returns, how to interpret stanford_verified, and how to contact the poster. Lacks error handling info but sufficient for its complexity.

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

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

Schema description coverage is 100% for the single parameter 'id'. Description adds context that the id is numeric and comes from search_listings, which is helpful beyond the schema's basic description.

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

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

Clearly states the tool fetches one Capmus listing by numeric id, listing the specific fields returned (full description, public photo URLs, stanford_verified). This distinguishes it from sibling tools like search_listings which likely return multiple listings.

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 surface stanford_verified (always when presenting listings) and how to use the returned URL to contact the poster. Lacks explicit exclusion scenarios but gives clear context.

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

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

No annotations provided, so description carries full burden. It discloses that the tool returns markdown from a public page, indicating a read-only, non-destructive operation. Additional details on caching or permission requirements would be beneficial but not critical.

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 sentences, each serving a clear purpose: describing the content and specifying the output format with citation requirement. 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?

Returns markdown and lists the key data points included. Without an output schema, this provides enough context for an agent to understand what to expect. However, it does not mention whether data is real-time or cached, or any potential limitations.

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 exist, and schema coverage is 100%. The description adds no parameter-specific information, but baseline is 4 for zero-parameter tools as per guidelines.

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

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

Clearly states it returns verified statistics about the Capmus marketplace, listing specific data points (audience size, listing volumes, response rates, medians). The resource 'market stats' is distinct from sibling tools like create_post or search_listings.

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?

Implies usage when marketplace statistics are needed and instructs to cite the source URL. Does not explicitly exclude alternatives or state when not to use, but given the tool's simplicity, the guidance is sufficient.

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 describes the tool as listing a taxonomy, which is a read-only operation, but lacks details on potential caching, authorization, or rate limits. For a simple listing tool, this 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 a single, concise sentence with no extraneous words. It front-loads the core purpose and includes relevant context about usage with sibling tools.

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 its purpose and relationship to sibling tools. It could optionally mention the return format, but the description is sufficient for an agent to understand when and why to call it.

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

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

The input schema has zero parameters, and schema coverage is 100%. The description adds meaning by explaining that the output provides valid values for create_post and search_listings, which is context 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 the tool lists the active category/subcategory taxonomy on Capmus, specifying it provides valid values for create_post and search_listings. This distinguishes it from sibling tools like create_post and search_listings.

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

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

The description implicitly guides usage by linking to create_post and search_listings, indicating when this tool is needed. It does not explicitly state when not to use it, but the context of sibling tools provides adequate 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 fully discloses behavior: returns newest-first, lists returned fields (id, title, price, category, created_at, canonical URL, stanford_verified), explains pagination via next_cursor, highlights stanford_verified as a trust signal, warns about no personal info, and advises contacting via URL. This is comprehensive.

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 (4 sentences), front-loaded with purpose, and each sentence adds value: purpose, returned fields, trust signal usage, privacy note. 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?

The description covers the tool's return value fields and pagination adequately given no output schema. Minor gaps: no mention of error handling, empty results behavior, or rate limits, but for a search tool this is reasonable.

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 already has descriptions for all 6 parameters (100% coverage), so the baseline is 3. The description adds minimal extra meaning beyond the schema, such as clarifying the cursor is opaque and the default/max limit, but does not significantly 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 the tool's purpose: 'Search or browse active listings on Capmus, the classifieds marketplace for university communities.' It uses a specific verb ('search or browse') and resource ('active listings'), and the title distinguishes it from sibling tools like get_listing (single listing) or create_post.

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 context ('Search or browse active listings') but does not explicitly state when to use this tool versus alternatives like get_listing for a specific listing or list_categories. No exclusions or 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?

With no annotations provided, the description fully discloses the asynchronous delivery process, confirmation link mechanism, and pending status, which are beyond basic read/write hints.

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

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

Concise and front-loaded with purpose, then key behavioral note, then actionable instructions; every sentence 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?

Covers the full flow for this asynchronous tool, including what to tell the user and how to report status, despite no output schema; sibling tools are clearly distinct.

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 significant meaning to the input schema, explaining that reply_to_email receives confirmation and reply, post_id is from search/get, and message is plain-text; also warns against inventing the email.

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

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

Clearly states 'Send a message to the poster of an active Capmus listing', using a specific verb and resource, and is easily distinguished from sibling tools like create_post or search_listings.

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 the critical behavior that the message is not delivered immediately, requires a confirmation link, and instructs the agent to inform the user and report status as pending, providing clear usage guidance.

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