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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds genuine behavioral context by disclosing data provenance (public GitHub repos set up for AI coding agents), which tells the agent the results are static observations rather than live queries. It does not address freshness, caching, or result size.

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?

A single dense sentence that front-loads the source and then enumerates content dimensions; nothing is wasted. It is slightly run-on, packing agent-level, combination, and per-domain scope into one clause chain, but it stays readable.

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?

With no output schema, the description carries the return-value burden and does so reasonably, enumerating the result categories (servers, combinations, per-domain APIs/SDKs/packages). Combined with 100% schema coverage on inputs and the readOnly annotation, an agent has enough to call it correctly; only freshness and output shape remain unstated.

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 both optional parameters (agent, domain) are already documented with their allowed values. The description adds domain-usage framing ('what those projects use') and examples that roughly mirror the schema, but no format or interaction details beyond what the schema provides. Baseline 3 applies.

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 concretely states the dataset: MCP servers developers connect, common combinations, and per-domain APIs/SDKs/packages, sourced from public GitHub repos. It distinguishes itself from data-catalog siblings like find_apis or find_tools by framing the content as observed real-world usage rather than a directory. The verb is implicit (a retrieval), so it falls short of a crisp verb+resource.

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?

There is no explicit when-to-use guidance, no conditions, and no named alternative among the many overlapping siblings (find_apis, find_tools, find_skills, list_recipes). The phrase 'overall or per agent' hints at how to slice the data but does not tell the agent when this tool is the right choice versus a catalog tool.

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