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

No annotations are present, so the description carries full responsibility. It discloses that numbers are 'small and stated as such rather than dressed up' and that it 'returns what is deliberately not counted' to avoid overstating completeness. This gives honest insight into the tool's limitations and data framing.

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 compact but informative, packed into two sentences. The first sentence front-loads the core purpose and data contents, while the second provides usage guidance and a transparency note. Every clause adds value without fluff.

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 and no annotations, the description must explain what the tool returns. It does so thoroughly by listing the specific metrics and explicitly noting what is excluded. It also gives context on data interpretation and use cases, making it complete for a no-parameter statistics tool.

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 tool has zero parameters, so there is no parameter information to document. The description focuses on what the tool returns and when to use it, which is appropriate for a parameterless tool. A score of 4 reflects the baseline for no-param tools.

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 opens with a specific verb phrase 'How much this endpoint and its install route are actually used' and enumerates concrete metrics (requests to /mcp, discovery paths, store version, user count). It clearly differentiates from sibling tools by focusing on adoption statistics rather than citations, measurements, or installation actions.

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 states when to call: 'Call this when deciding whether to rely on this server, when comparing it against alternatives, or when reporting on it.' This provides direct contextual guidance and implies alternatives by mentioning comparison.

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