food-recipe-mcp

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

Annotations include readOnlyHint=true, which already indicates a safe read operation. The description adds that it returns a greeting to confirm the server is running, but does not mention any rate limits, authentication, or error behavior. This is adequate given the annotation coverage.

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?

Description is two short sentences, front-loaded with the core purpose. No filler or redundant 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?

For a simple ping tool with one optional parameter and an output schema, the description covers the tool's purpose and return behavior. Schema handles parameter semantics, annotations handle safety.

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 'name' parameter is fully documented in the input schema. The description does not add any extra parameter details, so baseline 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 identifies the tool as a connectivity test that returns a greeting, using a specific verb ('returns') and resource ('server connectivity'). This distinguishes it from sibling tool search_recipes, which is for searching recipes.

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?

No explicit guidance is given on when to use this tool versus alternatives. The sibling search_recipes is unrelated, but the description does not state when to prefer ping (e.g., for health checks before other calls).

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?

Beyond the readOnlyHint annotation, the description discloses substantial behavioral traits: hybrid search mechanics (dense+sparse), reranking, database scope (~50,000 recipes), result fields (rerank_score, rrf_score, etc.), and the limitation that servings is not used for filtering. This far exceeds the annotation's minimal safety signal.

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 well-structured with clear sections (database context, usage examples, args, returns). It front-loads the core purpose and every sentence, including the seven query examples, adds practical value. The length is justified by the need to explain hybrid search, filters, and result contents.

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 fully self-contained: it explains the database scope, query language support, all parameters, return fields, and limitations. Even with an output schema available, it details what each result contains (rerank_score, rrf_score, ingredients, nutrition, etc.), making it complete for an agent to select and invoke correctly.

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 significant meaning beyond each parameter's schema description. For example, it clarifies that servings should be included in query context, states that query supports any language, and gives concrete example values for each parameter. This goes above the baseline for high 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 immediately states a specific verb+resource: 'Search a database of recipes using hybrid semantic search (dense + sparse) with reranking.' This clearly distinguishes the tool from the only sibling 'ping' and leaves no ambiguity about its function.

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 rich usage guidance, including natural language query examples, supported languages (Norwegian and English), and filter options. It also clarifies that 'servings' is not for filtering but should be included in the query. However, it does not explicitly state when not to use the tool or name alternatives beyond the sibling ping, 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.