search-mcp

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses the output format (plain text) and that it extracts 'main text content' rather than the full page. However, it does not mention error behavior, redirects, size limits, or timeout handling. It is not contradictory but lacks depth for a tool with no annotation safety net.

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, clear sentence that is front-loaded with the action and outcome. It contains no fluff or redundant details. Every word earns its place, and the key constraint ('public') is included without extra verbiage.

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?

This is a simple tool with one parameter and an output schema that defines the return type. The description sufficiently covers what the tool does, the input format, and the output format. For a tool of this complexity, nothing essential is missing – an agent can correctly invoke it with just this description.

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 description coverage is 100%, and the schema already defines the url parameter including the 'private/loopback hosts are rejected' constraint. The description adds 'public' which is redundant with the schema. No additional parameter semantics are provided beyond what the schema already communicates.

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 ('Fetch'), a specific resource ('a public http(s) URL'), and a clear outcome ('return its main text content as plain text'). It clearly distinguishes from the sibling search_web, which is about searching rather than fetching a known URL.

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 a clear condition: the URL must be public. However, it does not explicitly mention when to use this tool versus the alternative (search_web). An agent can infer that if you have a specific URL, use fetch_url, but the guidance is not explicit. No exclusion criteria or alternative routing 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 to carry the safety or behavioral profile, the description is the only source for behavioral information. It only states the output shape (titles, URLs, snippets), but doesn't disclose rate limits, authentication requirements, pagination behavior, or error conditions. This is a significant gap for a search 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?

A single, compact sentence with zero fluff. The action and output are stated upfront, making it easy to parse. Very efficient for agents.

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, and the output schema likely covers return types. However, the description does not mention any edge cases, limitations (e.g., result count restrictions are in schema), or behavioral details that could affect invocation. It is adequate for a basic search but lacks depth for agents needing to handle unexpected outcomes.

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 documents both parameters ('query' and 'count') with their types and defaults, and schema description coverage is 100%. The description adds no parameter-specific information beyond what the schema provides, so the 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 states a specific verb ('Search'), a resource ('the web'), and the return content ('titles, URLs, and snippets'). It clearly distinguishes from the sibling 'fetch_url', which likely fetches a specific URL rather than performing a query. The purpose is clear, though it doesn't explicitly name the alternative.

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 alternatives. It does not mention when not to use it, when to prefer 'fetch_url', or any context for selecting between them. Usage must be inferred from the tool name and purpose.

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