Finance Tools MCP

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 states the tool performs calculations but does not cover important traits like error handling (e.g., for invalid expressions), performance considerations (e.g., complexity or rate limits), or security aspects (e.g., safe evaluation). The examples imply it returns a dictionary with a 'result' key, but this is not explicitly stated in the description text.

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 a clear purpose statement followed by illustrative examples. Each example earns its place by demonstrating usage, but the formatting with code blocks and quotes could be slightly more streamlined for readability.

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 (single parameter, no output schema, no annotations), the description is partially complete. It covers the purpose and parameter semantics well but lacks usage guidelines and behavioral details. For a computation tool, more context on limitations or error cases would enhance 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 schema description coverage is 0%, so the description must compensate. It adds significant meaning by explaining that the 'expression' parameter accepts mathematical expressions using Python math syntax and numpy, with examples illustrating valid inputs. This goes beyond the schema's basic string type, though it could be more explicit about constraints or formats.

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 with a specific verb ('calculate') and resource ('result of a mathematical expression'), distinguishing it from sibling tools that focus on financial data, news, or time retrieval. It explicitly mentions support for Python math syntax and numpy, which further clarifies its mathematical computation role.

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 does not mention any prerequisites, exclusions, or comparisons with sibling tools, leaving the agent to infer usage based on the purpose alone. This lack of explicit context reduces its effectiveness in tool selection.

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