ChartMogul MCP Server

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 effectively adds context beyond basic functionality: it warns about LTV values being in integer cents (critical for correct interpretation), explains the LTV formula (LTV = Average Revenue Per User / Customer Churn Rate), describes the return structure (entries array with date, ltv, ltv_percentage_change, plus summary object), and provides an example for clarity. However, it does not cover aspects like rate limits, authentication needs, or error handling.

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 appropriately sized and front-loaded, starting with the core purpose and critical warning about integer cents. Each sentence adds value: formula explanation, return structure, parameter requirements, and an example. There is minimal waste, though it could be slightly more structured (e.g., bullet points for parameters). Overall, it efficiently conveys necessary information without redundancy.

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 complexity (6 parameters, no annotations, no output schema), the description does a good job of being complete. It explains the tool's purpose, critical data interpretation (integer cents), return format, and parameter semantics. However, it lacks details on error cases, pagination, or authentication requirements, which would enhance completeness for a metric retrieval tool in an API context.

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%, so the description must compensate. It adds significant meaning beyond the schema: it specifies that start_date, end_date, and interval are required and provides format details (YYYY-MM-DD for dates, 'any valid value' for interval). For optional parameters, it explains that filters use 'CFL syntax' and references 'get_cfl_fields' for field lists, adding practical usage context that the schema lacks. The description covers all parameters but could provide more detail on geo and plans.

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 tool retrieves Customer Lifetime Value metrics from the ChartMogul API, specifying both the verb ('Retrieve') and resource ('Customer Lifetime Value metrics'). It distinguishes itself from sibling tools like 'arpa_metrics' or 'mrr_metrics' by focusing specifically on LTV calculations, making the purpose highly specific and differentiated.

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 by specifying required parameters (start_date, end_date, interval) and optional ones (geo, plans, filters), but it does not explicitly state when to use this tool versus alternatives like 'all_metrics' or other metric-specific tools. It provides some context through parameter requirements but lacks explicit guidance on tool selection scenarios.

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