Pythia 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's function but does not cover critical aspects like whether it's read-only, requires authentication, has rate limits, or what the output format entails (e.g., numerical values, plots, or errors). This leaves significant gaps in understanding how the tool behaves beyond its basic purpose.

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 without unnecessary details. Every word contributes directly to explaining what the tool does, making it highly concise and well-structured for quick understanding.

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 (2 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose and parameters indirectly via the schema, but lacks details on output behavior, error handling, or integration with sibling tools. This results in a minimal viable description that meets basic needs but leaves room for improvement in contextual richness.

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, clearly documenting both parameters ('mass' and 'sqrts') with units and enum values. The description adds minimal value beyond the schema by mentioning 'specified mass and energy', but does not provide additional context like typical mass ranges or energy implications. With high schema coverage, a baseline score of 3 is appropriate.

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 action ('Get') and resource ('Standard Model predictions for Higgs cross sections and branching ratios'), specifying the scientific context. However, it does not explicitly differentiate from sibling tools like 'get_latest_higgs_data' or 'compute_sm_likelihood', which might offer related Higgs data or computations, leaving some ambiguity in tool selection.

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, such as 'get_latest_higgs_data' for experimental data or 'compute_sm_likelihood' for likelihood calculations. It lacks explicit context, prerequisites, or exclusions, relying solely on the implied need for Standard Model predictions at specific parameters.

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