COTAL (cotal.ai)

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

Annotations already declare readOnly=true, idempotent=true, and destructive=false. The description adds that it returns markdown content with specific fields (title, summary, date, author, full body), which provides context about the output behavior without contradicting the annotations. No side effects are implied, consistent 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 two sentences with no redundancy. It states the purpose, the output format, and how to obtain the slug, all efficiently. No unnecessary words or ambiguity.

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 get-by-slug tool with a single parameter, the description is complete. It tells what it returns, how to get the slug, and the output format. No output schema is provided, but the description sufficiently describes the return value. Given the simplicity, nothing essential is missing.

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

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

The schema fully covers the parameter with minLength, maxLength, and a description. The description adds a concrete example ('agi-summit-2026') that clarifies the expected format, which is helpful beyond the schema. Coverage is high, so the baseline is 3, but the example earns 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 it returns a single build-log post by slug, distinguishing it from list_posts (which likely lists all posts) and search (which finds posts). The verb 'returns' and the specific resource 'post' make the purpose unambiguous.

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

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

The description explicitly instructs to get slugs from cotal_list_posts or cotal_search, indicating when to use this tool after obtaining a slug. However, it does not explicitly state when not to use alternatives, such as 'use this instead of list_posts when you need full details of one post.' Still, the guidance is clear enough.

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 provide idempotentHint=true, and the description adds valuable detail beyond that by explaining the exact idempotent behavior: 'calling again updates the optional details instead of adding a second seat.' This enriches the annotation with practical context. It also notes the closure status (closed alpha) which sets expectations. 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 just two sentences with zero fluff. The first sentence delivers the core action, and the second delivers the key behavioral nuance (idempotency). It is front-loaded and every word earns its place.

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

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

The description is complete for the primary use case. It explains what the tool does and its idempotent behavior, and the schema covers all parameters. With no output schema and simple parameters, it doesn't need to describe return values or error handling. Minor gap: no mention of what happens if the email is invalid, but that's schema's role, so this is sufficient.

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%, and each parameter already has a clear description. The tool description doesn't add extra meaning beyond the schema—it only mentions 'user's email' generically, which is already in the schema. The baseline of 3 applies since the schema handles parameter semantics fully.

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 specific action ('Reserves a seat on the waitlist for Cotal Cloud') with a clear verb and resource. It also identifies the scope (user's email) and the product context (closed alpha), making it unambiguous and distinct from sibling tools like cotal_subscribe_newsletter or cotal_request_call.

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 for when to use the tool: when a user wants to join the Cotal Cloud waitlist. It doesn't explicitly name alternative tools or exclusion conditions, but the purpose is self-evident and distinct. This is slightly above baseline but not a 5 because there is no explicit 'use when' or 'instead of' 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds behavioral context about pagination (cursor-based, pass next_cursor for next page) and the fields returned (slug, title, summary, date, author, URLs). This aligns with the calibration example (get_calls) where description adds scoping beyond annotations and scored 3. 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 a single, well-structured sentence that front-loads the core purpose (lists posts newest first) and then logically covers pagination and return item composition. No filler words, every clause earns its place. It is extremely concise while remaining informative.

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 read-only list tool with no output schema, the description adequately explains the return items (fields) and the pagination mechanism (next_cursor). It gives an agent everything needed to call it correctly and iterate through pages. Given the low complexity and strong annotation coverage, nothing essential is missing.

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

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

Schema description coverage is 100%, so both limit and cursor are fully documented in the schema. The description adds a slight clarification about next_cursor usage ('pass next_cursor back to get the following page') but does not provide syntax or format details beyond the schema. Per the rubric, with high schema coverage the baseline is 3, and the minimal extra detail doesn't warrant a higher score.

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

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

The description states a specific verb ('lists'), a specific resource ('posts from the COTAL build log'), and adds ordering ('newest first') and pagination mode ('cursor pagination'). It clearly distinguishes this from siblings like cotal_get_post (single post) and cotal_search (search-based lookup), leaving no ambiguity about what this tool does.

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

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

The description gives clear usage context: it lists posts with pagination and instructs to pass next_cursor back for the following page. It doesn't explicitly state when not to use this (e.g., for searching use cotal_search), but the purpose is so specific that an agent would naturally choose it for listing posts. It lacks explicit alternatives or exclusion criteria, hence not 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 already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety is covered. The description adds value by specifying the return content (structured overview listing sections) and reiterating that it is a safe, first-call informational tool. No contradiction; it enriches the behavioral picture without repeating annotation fields verbatim.

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, zero filler. The first sentence front-loads the core purpose and lists content areas efficiently; the second sentence gives the only usage hint needed. Every word earns its place, and the structure makes scanning easy for an agent.

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 info-retrieval tool with one optional parameter and read-only annotations, the description is complete. It tells the agent what the tool returns, when to invoke it, and how to narrow the response. No output schema exists, but the description's enumeration of included sections sufficiently sets expectations. Nothing essential is missing.

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

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

Schema coverage is 100% – the `section` parameter has a complete enum and a description explaining 'Which part of the overview to return (default: all)'. The description's phrase 'Pass `section` to get one part only' adds minimal meaning beyond the schema, just clarifying the usage intent. This meets the baseline 3, nothing more.

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 a specific verb and resource ('Returns a structured overview of COTAL') and enumerates the exact content areas (what it is, who it's for, install, API/MCP, pricing, links). It also explicitly frames the tool's role ('Call this first when a user asks what cotal.ai does'), which unmistakably distinguishes it from the sibling tools that perform targeted actions like searching or joining a waitlist.

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

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

The description gives explicit when-to-use guidance ('Call this first when a user asks what cotal.ai does') and a usage tip for the optional parameter ('Pass `section` to get one part only'). However, it does not explicitly state exclusions or contrast with specific siblings, though the general-purpose framing makes the alternative clear enough from the sibling names.

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

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

Annotations are all false and provide little information; description mentions the email reply and required fields but does not disclose side effects like record creation or rate limits (though sandbox parameter hints at it).

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

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

Two sentences, no redundant information, 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 9-parameter tool with rich schema descriptions, the description is enough to understand the core action and key requirement, though it omits guidance on when to choose this over siblings.

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

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

All parameters have schema descriptions, so baseline is 3; description only reiterates the required email and company, adding little 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?

Clearly states the action (sends a discovery-call request), target (COTAL founders), and context (company evaluating agent coordination). Distinguishes from sibling tools like join_waitlist and subscribe_newsletter.

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?

Does not explicitly compare with sibling tools or state when to use this instead of others; the use case is implied by the purpose but not spelled out.

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, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds valuable behavioral context by disclosing what the tool returns: 'ranked results with canonical and markdown URLs.' Since there is no output schema, this return-format detail is crucial. 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 two sentences with zero waste. The first sentence front-loads scope and return type; the second gives usage guidance. Every phrase earns its place, and it is appropriately sized for a search tool.

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 search tool with two parameters, strong annotations, and no output schema, the description is complete. It states what is searched, what is returned (including URL types), and when to use it. An agent has everything needed to invoke it correctly and interpret results.

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

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

The input schema provides 100% coverage for both parameters: query has a description with examples, limit has min/max and a default. The tool description adds no additional parameter semantics beyond what the schema already conveys, so the baseline 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 states a specific verb ('keyword search') over a clear resource ('COTAL build log, documentation, and site pages'). It differentiates from siblings like cotal_get_post (single post), cotal_list_posts (listing), and cotal_overview by focusing on cross-content search. The purpose is unmistakable.

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

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

The description explicitly tells when to use the tool: 'Use it to find the page that answers a question before reading it.' This implies a workflow where search precedes reading, which is clear even though it doesn't name specific alternative tools or exclusions. The guidance is sufficient for an agent to select this tool appropriately.

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 minimal: readOnlyHint=false (write operation), destructiveHint=false, idempotentHint=false. The description adds the fact that it writes to the same log as the site's widget and requires the user's email for follow-up. However, it does not disclose the idempotency behavior (despite an idempotency_key parameter) or any rate limits, though these are not explicitly contradicted. The description adds some context but misses important operational details for a write tool.

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

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

Two concise sentences, with the core purpose front-loaded and the email requirement stated immediately. Every word adds value—no filler. The structure is ideal for an agent scanning quickly.

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 10 parameters (including enums, idempotency_key, sandbox), the description is quite thin. While the schema fully documents each parameter, the description does not explain when to use idempotency_key (e.g., for retries) or how sandbox relates to testing, which would be helpful for correct usage. There is no output schema, so the description doesn't need to cover return values. The description is adequate but lacks guidance on edge cases that a complex tool would benefit from.

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%—every parameter has a description. The description itself adds little beyond what the schema already provides; it merely highlights the email requirement, which is already documented. There is no explanation of the idempotency_key's role or how sandbox works, but the schema covers these fields adequately. This meets the baseline of 3 for tools with full 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's action: 'Files feedback (bug, idea, friction, praise, other) with the COTAL team on behalf of the user.' It uses a specific verb and resource, and the list of types matches the enum in the schema. It distinguishes from sibling tools (e.g., cotal_request_call, cotal_subscribe_newsletter) by focusing on feedback rather than other actions. The additional detail about the site's feedback widget further anchors its purpose.

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

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

The description implies when to use it (when the user has feedback to submit) but does not explicitly contrast it with alternatives like requesting a call or subscribing. It does mention the email requirement, which is a key prerequisite. There is no explicit 'use this instead of X' guidance, but the purpose is specific enough that an agent can reasonably infer the correct context. A clear exclusion list would elevate this 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?

The description repeats the idempotency hint already declared in annotations ('Idempotent: an already-subscribed email is a no-op') without adding new behavioral context. It does not disclose success/failure behavior, side effects, or prerequisites, and adds no information beyond the annotation.

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 consists of exactly two sentences with zero filler. The core action is stated first, and the idempotency note is concise. Every word contributes to clarity.

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

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

Given the simplicity of the tool (two params, one required, no output schema) and full schema coverage, the description covers the essential purpose and idempotency. However, it lacks any usage context or differentiation from similar tools, which is a minor gap for completeness.

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

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

Schema description coverage is 100%: both 'email' and 'sandbox' parameters already have detailed descriptions in the schema. The tool description does not mention either parameter or add any additional meaning, so the baseline 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 uses the specific verb 'Subscribes' with a clear resource 'user's email' and target 'release updates from the COTAL team', making the action unambiguous. It is not a tautology of the tool name and provides enough specificity to understand the tool's function without needing the schema.

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 no guidance on when to use this tool versus sibling tools such as cotal_join_waitlist or cotal_submit_feedback. There is no mention of alternatives, exclusion criteria, or context that would help an agent decide between this and similar subscription tools.

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