search_qa_cases

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

No annotations are provided, but the description fully discloses behavior: it is a search tool that returns a dict with count and results, each result containing specific fields. No side effects or destructive actions are implied, and the return format is fully specified.

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 fairly long but well-organized into sections (usage, args, returns). It front-loads the purpose and provides concrete examples. Some repetition could be trimmed, but it remains clear and informative.

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 no output schema, the description fully describes the return structure including fields like slug, title, problem preview, etc. It covers input parameters, usage context, and output format, leaving no critical gaps for an agent to invoke the tool 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 description coverage is 0%, but the description provides detailed guidance for all 5 parameters: query examples, server slug example, category with allowed values, lang with allowed values, and limit with range. This adds substantial meaning beyond the bare schema.

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 it searches Q&A cases, which are curated solutions to real GitHub Issues. It specifies the structured format (problem, cause, solution) and distinguishes from sibling tools that search for tools, servers, tutorials, etc.

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?

Explicitly says 'Use this FIRST when the user reports an error or unexpected behavior with an MCP server' and lists common error examples. Does not explicitly state when not to use, but the context is clear and alternatives are implied by the sibling tool names.

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