nyuchi-docs

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 provides a synthesized answer with source citations. Though not explicitly read-only, the semantics of 'ask' and 'grounded in docs' imply a safe query operation. No behavioral traits beyond what is stated are needed for this simple tool.

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

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

The description is a single sentence of 15 words, perfectly front-loaded and devoid of fluff. Every word contributes meaning.

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 required parameter, no output schema), the description covers the essential purpose and output characteristics. It does not mention limitations or result format details, but for a basic Q&A tool the information 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 coverage is 100%, so baseline is 3. The description merely restates the parameter purpose ('ask a question') without adding any new constraints, format, or examples. It adds no value beyond the schema.

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

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

The description clearly states the verb 'ask', the resource 'docs.nyuchi.com', and the outcome 'synthesized answer with source citations'. It distinguishes from siblings like search_docs (which likely returns raw results) and read_page (for reading a specific page).

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 the tool is for asking questions to get synthesized answers, but it does not explicitly guide when to use this tool versus alternatives like search_docs or read_page. No exclusions or prerequisites are mentioned.

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

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

With no annotations provided, the description carries the full burden. It discloses that the tool files a GitHub issue when configured, otherwise queues it for the docs team. This gives useful behavioral context. However, it does not mention authentication requirements, rate limits, or whether the operation is idempotent.

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, concise and front-loaded with the purpose. Every sentence adds value without redundancy.

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

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

Given the tool's simplicity (3 parameters, no output schema), the description adequately explains what the tool does and what happens with the issue. It could mention the expected return value or confirmation, but overall it's 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%, so the baseline is 3. The description adds no extra meaning beyond the schema for parameters; it only refers to 'actionable issue' in general. No further detail is provided about parameter usage 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 explicitly states the tool's purpose: raising actionable documentation issues. It specifies the types of issues (wrong instructions, broken example, outdated page) and the action taken (GitHub issue or queue for docs team). This clearly distinguishes it from sibling tools like ask_docs, read_page, etc.

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 the tool (when finding actionable issues in docs) and mentions behavior based on configuration. However, it does not explicitly state when not to use it or compare to alternative sibling tools, which could improve 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 states it fetches a page and returns text, but omits details like required authentication, potential errors, caching behavior, or any side effects. For a simple fetch, this is minimal but insufficient.

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 key action and resource. Every word is necessary, and it is appropriately concise for the tool's simplicity.

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 low complexity, the description is too vague about the output format ('readable text content' is ambiguous). With no output schema, it should specify whether it returns plain text, markdown, or structured data. This gap limits the agent's ability to use the response.

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

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

Schema description coverage is 100%, so baseline is 3. The description reiterates the schema's description (path or URL) without adding new semantics or format details, thus no value added.

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 verb 'Fetch' and the resource 'docs.nyuchi.com page', and specifies the output 'readable text content'. The purpose is specific and distinct from siblings like search_docs or ask_docs.

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 fails to provide when-to-use or when-not-to-use guidance relative to sibling tools. It does not mention alternatives or exclusions, leaving the agent guessing about the best tool for a given 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?

With no annotations, the description carries the full burden. It discloses that the search is both keyword and semantic, and the return format includes titles, URLs, and snippets. However, it does not mention ordering, authentication needs, rate limits, or any side effects. Basic transparency is provided.

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

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

The description is extremely concise—two sentences with no wasted words. It front-loads the core functionality and output format, making it 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?

Given the tool's simplicity (2 parameters, no output schema, no nested objects), the description covers the essential behavior: the search domain and the items returned. It is complete enough for an agent to understand what the tool does and what it returns.

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

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

Schema coverage is 100%, so the baseline is 3. The description does not add any additional meaning to the parameters beyond what the schema already provides (e.g., 'What to search for' for query, max results for top_k). The description focuses on output rather than parameter 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 the action ('Keyword/semantic search'), the resource ('docs.nyuchi.com'), and the output ('matching pages with titles, URLs and snippets'). It effectively distinguishes from sibling tools like 'read_page' (which retrieves a specific page) and 'ask_docs' (which is likely Q&A).

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 explicitly state when to use this tool versus alternatives, nor does it provide when-not-to-use guidance or mention any prerequisites. Usage is implied but not clarified.

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

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

No annotations are provided, so the description carries full burden. It discloses that feedback is stored for the docs team, indicating a non-destructive write operation. However, it does not mention any other behavioral traits like authentication or rate limits, leaving gaps.

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

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

The description is extremely concise with no wasted words. Two short sentences convey purpose and outcome efficiently.

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 4-parameter tool with no output schema and no nested objects, the description and schema together provide sufficient information. The description clarifies the purpose and the schema covers parameter details.

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

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

Schema description coverage is 100%, so parameters are already documented. The description adds no additional meaning beyond the schema, meeting the baseline for high 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 action ('Send feedback'), the resource ('documentation'), and examples of feedback types (correction, confusing, missing). It implicitly differentiates from siblings like ask_docs and raise_issue, as it focuses on documentation feedback.

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

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

The description implies usage for documentation feedback but provides no explicit guidance on when to use this tool versus alternatives (ask_docs, raise_issue). No exclusions or context are given.

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