cronometer-mcp

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 adds value by specifying the return format ('matching foods with their IDs and source information needed to add a serving'), which helps understand the tool's output. However, it does not cover other behavioral aspects like rate limits, authentication needs, or error handling, leaving gaps in transparency.

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 appropriately sized and front-loaded, with the core purpose stated first, followed by return details and parameter explanation. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI 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?

Given the tool's moderate complexity (single parameter, search operation), no annotations, and the presence of an output schema, the description is largely complete. It covers purpose, return format, and parameter semantics adequately. However, it could improve by addressing behavioral aspects like error cases or usage constraints, slightly reducing 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?

The schema description coverage is 0%, so the description must compensate. It effectively explains the single parameter 'query' with its purpose ('Food name or keyword to search for') and provides examples ('e.g., "eggs", "chicken breast"'), adding meaningful semantics beyond the basic schema. This compensates well for the lack of schema descriptions.

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 ('Search Cronometer's food database by name') and the resource ('food database'), distinguishing it from sibling tools like 'get_food_details' or 'add_food_entry'. It precisely communicates what the tool does without being vague or tautological.

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 searching foods by name, but does not explicitly state when to use this tool versus alternatives like 'get_food_details' or 'get_food_log'. It provides basic context (searching by name) but lacks explicit guidance on exclusions or comparisons with sibling tools.

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