Pythia MCP

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

With no annotations provided, the description carries full burden but offers limited behavioral insight. It mentions the tool returns likelihood values but doesn't disclose computational characteristics (e.g., execution time, resource intensity), error handling, or whether it's read-only versus mutative. The example 'CV-CF plane' hints at physics applications but lacks operational details.

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 a single, efficient sentence that front-loads the core purpose. It avoids redundancy and wastes no words, though it could be slightly more informative without losing conciseness.

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?

For a tool with 3 parameters, low schema coverage (33%), no annotations, no output schema, and nested objects, the description is insufficient. It doesn't explain the output format (e.g., array structure for contour data), error conditions, or how the scan relates to sibling tools, leaving the agent with significant uncertainty.

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 low at 33%, with only 'param1.name' and 'fixedParams' having descriptions. The description adds minimal value beyond the schema, mentioning 'CV-CF plane' as an example for parameter names but not explaining the semantics of min/max/steps or how 'fixedParams' should be structured. It partially compensates but leaves significant 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 performs a '2D parameter scan' and returns 'likelihood values for contour plotting', which is specific about the action and output. It distinguishes from sibling tools like 'scan_1d' by specifying 2D scanning, but doesn't explicitly differentiate from other likelihood-related tools like 'compute_likelihood'.

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 minimal guidance, only implying usage for contour plotting. It doesn't specify when to use this tool versus alternatives like 'scan_1d' for 1D scans or 'compute_likelihood' for single-point calculations, nor does it mention prerequisites or exclusions.

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