mcp-sympy

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

No annotations are provided, and the description offers no behavioral information. It doesn't disclose whether this is a read-only computation, what the output format is, or any constraints like input validation or performance characteristics.

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?

While concise with a single phrase, it's under-specified rather than efficiently informative. The description lacks structure and fails to front-load essential information, making it ineffective despite its brevity.

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 no annotations and a simple tool with one parameter and an output schema, the description is incomplete. It doesn't explain the tool's purpose or usage, leaving gaps that could confuse an agent, even though the output schema might cover return values.

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 input schema has 100% description coverage, with 'n' documented as a 'Non-negative integer'. The description adds no parameter semantics beyond this, so it meets the baseline of 3 where the schema handles all documentation.

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 'Lucas number.' is a tautology that merely restates the tool name without explaining what it does. It doesn't specify the action (e.g., 'compute' or 'generate') or clarify that it calculates the nth Lucas number, which would distinguish it from siblings like 'sympy_fibonacci'.

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?

No guidance is provided on when to use this tool versus alternatives. It doesn't mention sibling tools (e.g., 'sympy_fibonacci' for Fibonacci numbers) or specify use cases, leaving the agent with no context for selection.

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