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. It discloses the return structure (entries array with date, customers, customers_percentage_change, plus summary object) and mentions an example ('customers=382 means 382 active customers'), which adds useful context. However, it lacks details on permissions, rate limits, or error handling, leaving behavioral gaps for a metric retrieval tool.

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 front-loaded with the core purpose and return structure, followed by parameter details and an example. It is appropriately sized with no redundant sentences, though the CFL syntax explanation is somewhat dense. Every sentence adds value, making it efficient for an agent to parse.

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's complexity (6 parameters, no annotations, no output schema), the description is fairly complete. It explains the purpose, parameters, return format, and provides an example. However, without an output schema, it could benefit from more detail on the 'summary object' structure, and it lacks error or edge-case handling information.

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, so the description must compensate. It adds significant semantics: it lists required parameters (start_date, end_date, interval with format/options), optional parameters (geo, plans, filters), explains the CFL syntax for filters, and references 'get_cfl_fields' for field lists. This covers most parameters meaningfully, though some details like geo/plans formats are not elaborated.

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 explicitly states the tool's purpose: 'Retrieve customer count metrics (total active customers over time).' It specifies the exact resource (customer count metrics) and verb (retrieve), and distinguishes it from sibling tools like 'customer_churn_rate_metrics' or 'mrr_metrics' by focusing on active customer counts over time.

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 for retrieving time-series customer metrics but does not explicitly state when to use this tool versus alternatives like 'all_metrics' or other metric-specific tools. It mentions an optional 'filters' parameter and references 'get_cfl_fields' for field lists, providing some contextual guidance but no clear when/when-not directives or named alternatives.

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