mcp

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

With no annotations, the description carries full burden. It states the output format (JSON with anchor text) and that only http(s) links are returned, but does not disclose potential behaviors like handling of unreachable pages, redirects, or rate limits. This is adequate but not rich.

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

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

The description is two sentences, front-loaded with the core action and output, and every word serves a purpose. It is concise and 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 fetch-and-extract tool with fully documented parameters, the description adequately explains return values (JSON of links with anchor text). It doesn't detail limit behavior or edge cases, but given the tool's simplicity, this is acceptable.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds minimal parameter-specific meaning beyond the schema, except clarifying that the output includes anchor text, which relates to the link extraction rather than 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 function: 'Fetch a web page and return all of its outbound http(s) links (with anchor text) as JSON.' This uses a specific verb (fetch, return) and resource (outbound links), distinguishing it from siblings like fetch_markdown or fetch_metadata.

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 notes it is 'Useful for crawling or finding related pages,' providing clear usage context. However, it does not explicitly mention alternatives or when not to use it, which is a minor gap.

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 burden of disclosure and does well by stating that nav, ads, and boilerplate are removed, and that raw endpoints are returned as-is. It does not discuss error handling or rate limits, but for a read-only fetch tool the disclosed behavior is substantial.

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 concise, front-loaded sentences: the main function, an important exception for raw endpoints, and a clear use case. Every sentence adds value with no redundancy.

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

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

The tool is simple (2 params, no output schema) and the description explains what it returns (clean Markdown) and how raw formats are handled. It provides enough context for correct invocation and result interpretation without needing additional detail.

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% and both parameters are already described in the input schema. The description adds general context but no additional syntax or meaning beyond what the schema provides, so it meets the baseline without exceeding it.

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 a specific verb ('Fetch a web page') and resource ('return its main content as clean Markdown'), clearly distinguishing it from sibling tools like extract_links or fetch_metadata. It also specifies the transformation (boilerplate removed) and the 'as-is' behavior for raw endpoints.

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 usage guidance: 'Use this to read an article, docs page or raw file.' It does not explicitly name sibling tools as alternatives or state when not to use it, but the context is clear enough for an agent to select it 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses the return format (JSON) and the scope (metadata only, no full body), but does not mention error handling, redirects, network timeouts, or authentication requirements. This is adequate for a simple fetch tool but lacks depth.

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 that efficiently communicates the tool's purpose, output format, and a key limitation (no full body). Every word earns its place, with no redundancy.

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

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

For a simple one-parameter tool with no annotations or output schema, the description covers the essential aspects: what it fetches, what it returns, and what it omits. It could be slightly more complete by mentioning error behavior or non-page inputs, but those are minor gaps for a tool of this 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?

The input schema fully documents the single url parameter with a clear description ('The absolute http(s) URL of the page'), and the tool description does not add further parameter-specific semantics. With 100% schema coverage, 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 clearly specifies the tool's function: fetching a web page and returning metadata as JSON, listing the exact fields (title, byline, site name, excerpt, word count). It also distinguishes from sibling tools like fetch_markdown by explicitly stating 'no full body' is returned.

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 metadata is needed) and hints at alternatives via 'no full body', which suggests using a different tool for full content. However, it does not explicitly name sibling tools or provide explicit when-not-to-use guidance, so it falls short of 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?

With no annotations, the description carries the burden and adds meaningful behavioral context: it returns only passages, includes heading breadcrumbs, ranks by relevance, and notes token efficiency. However, it does not cover edge cases like no matches, error handling, or pagination, so it is not fully transparent.

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 compact sentences, each earning its place: what the tool does, the result format, and when to use it with a named alternative. No filler or 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?

Without an output schema, the description explains the return nature (passages, breadcrumb, ranking) and provides a practical use case with token savings. It doesn't fully specify the return structure or edge-case behavior, but it is complete for a tool of this 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 already provides 100% coverage with detailed descriptions for all four parameters, so the description adds little beyond the schema. It offers general context about query usage and token savings but no additional parameter-level semantics.

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 fetches a web page and returns only matching passages, with the result format (heading breadcrumb, ranked by relevance). It explicitly contrasts with fetching the whole page and references fetch_markdown, distinguishing it from sibling 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 provides explicit usage guidance: 'Use this to find a specific detail' and mentions the token advantage over fetch_markdown, naming a clear alternative. The 'instead of the whole page' phrase implies when not to use it, satisfying the when/when-not/alternatives criterion.

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