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 minimal behavioral disclosure. It mentions the tool computes '-2 log L' but doesn't describe output format, error conditions, computational requirements, or data sources beyond 'LHC experimental data'. For a complex scientific computation tool, this is inadequate.

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?

Two sentences efficiently convey the core purpose without redundancy. The first sentence states what the tool does, the second provides context about its role. However, it could be more front-loaded with key behavioral information given the tool's complexity.

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 complex tool with 17 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what '-2 log L' means in practical terms, how results should be interpreted, what experimental data is used, or any limitations/assumptions. The context signals indicate high complexity that isn't addressed.

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 100%, so the baseline is 3. The description adds marginal value by mentioning 'reduced couplings or signal strengths' which aligns with the mode parameter, but doesn't provide additional semantics beyond what's already documented in the comprehensive schema descriptions.

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 specific verbs ('compute', 'compare') and resources ('Higgs likelihood', 'theoretical predictions', 'LHC experimental data'). It distinguishes from siblings by focusing on likelihood computation rather than analysis of specific models (e.g., analyze_2hdm) or auxiliary functions like data fetching.

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 implies usage for comparing theoretical predictions against experimental data but doesn't explicitly state when to use this tool versus alternatives like compute_sm_likelihood or convert_to_signal_strength. It mentions the primary analysis function but lacks explicit guidance on tool selection criteria.

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