Financial Analysis MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool calculates growth rates and returns a dict with specific keys, but lacks details on edge cases (e.g., handling of empty series, negative values, or single data points), computational methods (e.g., formula used), or error handling. This is a significant gap for a calculation tool with no annotation coverage.

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, with the core purpose stated first, followed by parameter and return details. Each sentence adds value, and there's no redundant information. It could be slightly more structured (e.g., bullet points), but overall it's efficient and clear.

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 moderate complexity (calculation-based, no annotations, no output schema), the description is minimally adequate. It covers the purpose, parameter semantics, and return structure, but lacks behavioral details like error handling or method specifics. Without an output schema, it partially compensates by describing the return dict, but more context would improve completeness.

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 description adds meaningful semantics beyond the input schema. The schema only indicates 'series' is an array with 0% description coverage. The description clarifies it's a 'List of numbers ordered oldest to newest,' providing crucial ordering and data type context. However, it doesn't specify minimum length or validation rules, leaving some gaps.

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's purpose: 'Calculates growth rates for a time series (e.g., revenue, earnings).' It specifies the verb ('calculates'), resource ('growth rates'), and domain context ('time series'). However, it doesn't explicitly differentiate from sibling tools like 'mcp_calculate_financial_metrics' or 'mcp_calculate_technical_indicators', which might also involve calculations on financial data.

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 provides no guidance on when to use this tool versus alternatives. It mentions examples ('e.g., revenue, earnings') but doesn't specify contexts, prerequisites, or exclusions. With sibling tools like 'mcp_calculate_financial_metrics' available, there's no indication of how this tool differs or when it's preferred, leaving usage ambiguous.

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