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

Despite no annotations, the description reveals internal scoring mechanism (100-200 for real match, filtered by min_score) and output structure (missing_in). It does not mention any side effects or prerequisites.

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 concise, well-structured, and free of extraneous information. It starts with the core purpose, then provides necessary detail in subsequent sentences.

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 adequately covers behavior for a 4-parameter tool without output schema, though limit_per_language could be more explicitly defined. Overall, it gives sufficient context for proper invocation.

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?

With 0% schema description coverage, the description fully compensates by explaining the meaning and defaults of all parameters: name, languages (defaults to all), min_score (default 90), and limit_per_language (default 1).

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 the specific verb 'Find' and resource 'the same algorithm across languages', clearly distinguishing it from sibling tools like search_algorithms (which searches for algorithms) and list_languages (which lists languages).

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 explains the intended use case: comparing an algorithm across languages. It details the scoring filter and default behavior, but lacks explicit when-not-to-use or alternative tool references.

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