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 p-values but doesn't explain what statistical method is used (e.g., chi-squared distribution), whether it handles edge cases (e.g., invalid likelihood/ndf values), what the output format is, or any error conditions. For a statistical computation tool with zero annotation coverage, this is insufficient.

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 directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly. Every part of the sentence contributes to understanding the core functionality.

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 of statistical computation, lack of annotations, and no output schema, the description is incomplete. It doesn't explain the statistical methodology, output format, error handling, or how it integrates with sibling tools (especially compute_likelihood). For a tool that performs mathematical calculations with multiple parameters, more context is needed for 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 100%, so the schema already documents all three parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't clarify the relationship between likelihood and ndf, or how reference affects computation). With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate with extra insights.

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 p-value for a given model compared to the Standard Model or best-fit point.' It specifies the action (compute) and resource (p-value) with context (comparison to SM or best-fit). However, it doesn't explicitly differentiate from sibling tools like compute_likelihood or compute_sm_likelihood, which prevents a perfect score.

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 mentions the tool computes p-values for model comparisons but doesn't indicate prerequisites (e.g., needing likelihood values from compute_likelihood), when to choose SM vs. bestfit reference, or how it differs from sibling statistical tools. This leaves usage context unclear.

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