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 computes and returns '-2 log L for SM couplings (all C = 1)', which implies a read-only calculation, but doesn't cover critical aspects like performance characteristics, error handling, or whether it's idempotent. For a computational tool with zero annotation coverage, this leaves significant behavioral gaps.

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 extremely concise and front-loaded: two sentences that directly state the purpose and return value with zero wasted words. Every sentence earns its place by providing essential information about what the tool does and what it returns.

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 computational nature, single parameter, and lack of output schema, the description is minimally complete. It explains what's computed and the return format (-2 log L), but doesn't provide context about the significance of the result or how it fits with other tools. With no annotations and no output schema, it should ideally explain more about the computation's scope or limitations.

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 schema description coverage is 100%, with the single parameter 'expInput' documented as 'Path to experimental input list'. The description adds no additional parameter information beyond what the schema provides. According to the rules, with high schema coverage (>80%), the baseline is 3 even with no param info in the description, which applies here.

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: 'Compute the Standard Model likelihood as a reference point.' It specifies the verb ('compute') and resource ('Standard Model likelihood'), and distinguishes it from siblings like 'compute_likelihood' by specifying it's for SM couplings with C=1. However, it doesn't explicitly differentiate from 'get_sm_predictions' which might be a related sibling.

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 when to choose this over 'compute_likelihood' (a sibling tool) or other analysis tools like 'analyze_2hdm'. There's no context about prerequisites, timing, or exclusions, leaving the agent without usage direction.

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