MCP Analytics

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It successfully clarifies what content is returned (use cases, assumptions, data requirements) beyond a generic 'information' label, implying this is a metadata retrieval operation. However, it lacks safety indicators (read-only status), error handling details (what if tool_name is invalid), or rate limit warnings.

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 a single, efficient sentence with high information density. The critical differentiator ('use cases, assumptions, data requirements') is placed at the end via em-dash for emphasis. No words are wasted; every clause earns its place by either stating the operation or clarifying the scope.

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 the tool has only one required parameter (fully documented in schema) and no output schema, the description adequately compensates by specifying the categories of information returned. For a simple read-only metadata tool, this is sufficient, though it could be improved by mentioning error cases or return format (structured vs text).

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 100% with the single 'tool_name' parameter fully documented as 'Name of the tool'. The description mentions 'a specific analysis tool' which implicitly reinforces the parameter purpose, but adds no additional semantic value (examples, format constraints, or lookup behavior) beyond what the schema already provides. Baseline 3 is appropriate for high schema coverage.

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 the verb (get detailed information) and resource (analysis tool), and the em-dash clarification ('use cases, assumptions, data requirements') effectively distinguishes this from siblings like tools_schema (technical specs) or discover_tools (listing). However, it lacks explicit comparison to these siblings to make the differentiation foolproof.

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 implies usage context through the specific information types returned (use cases, assumptions, data requirements), suggesting when an agent should use this tool. However, it lacks explicit 'when to use vs alternatives' guidance or mention of prerequisites like needing the exact tool_name from discover_tools first.

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