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 lacks behavioral details. It doesn't disclose whether this is a read-only calculation or has side effects, what the output format is (e.g., numerical values, structured data), error handling, or computational constraints (e.g., rate limits for physics simulations). The description only states the conversion action without operational context.

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 action ('convert') and scope. Every word earns its place—'reduced couplings', 'signal strength values', and 'all production and decay modes' are precise physics terms without redundancy. No extraneous details or fluff are present.

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 complexity (9 parameters, physics domain), no annotations, and no output schema, the description is incomplete. It doesn't cover parameter meanings, output format, error conditions, or usage context. For a tool with significant input requirements and no structured support, more explanation is needed to guide effective use.

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 0%, so the description must compensate but adds no parameter information. It doesn't explain what 'mass', 'CV', 'CF', etc., represent (e.g., particle mass, coupling constants), their units, valid ranges, or interdependencies. With 9 undocumented parameters, the description fails to provide essential semantics beyond the schema's bare types.

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 ('convert') and the transformation ('reduced couplings to signal strength values'), specifying the scope ('for all production and decay modes'). It distinguishes from siblings like 'compute_likelihood' or 'scan_1d' by focusing on unit conversion rather than analysis or scanning. However, it doesn't explicitly differentiate from tools like 'get_sm_predictions' which might involve similar physics concepts.

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 doesn't mention prerequisites (e.g., needing coupling values first), exclusions, or comparisons to siblings like 'compute_sm_likelihood' for related calculations. Usage is implied only by the conversion purpose, with no explicit context for selection.

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