suggest_mcps_for_query

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

With no annotations provided, the description carries the full burden for behavioral disclosure. It adequately reveals that the tool analyzes the query and returns JSON with suggestions, rationale, workflows, and cost/time estimates. While it doesn't explicitly state 'does not modify state,' the analytical nature and return specification make the behavior clear.

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: summary line, Args, Returns, and Example. Every sentence contributes meaningful information, and the example is illustrative without being verbose. This is appropriately sized for a tool with three parameters.

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?

Despite the absence of annotations, the description is comprehensive. It covers the tool's purpose, parameter semantics, return value structure, and a realistic use case. The presence of an output schema reduces the need to detail return values, but the description still provides a useful example of what the output looks like.

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 has 0% description coverage, but the description's Args section fully compensates by adding meaning beyond the raw schema. It explains the context parameter with options (SECURITY, COST, etc.), describes user_mcps as a filter, and provides a complete example with actual parameter values.

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 uses a specific verb ('suggest') and resource ('MCP servers'), clearly distinguishing this from sibling tools like get_mcp_workflow_template or consult_skill. It states the exact function: analyzing a query and recommending appropriate MCP servers.

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 clear context for when to use this tool ('for a given query') and includes a concrete example for a security review scenario. However, it doesn't explicitly mention when not to use it or name alternative tools, so it falls short of full usage-guideline coverage.

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