Pythia MCP

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool lists datasets but doesn't describe output format (e.g., list, table, JSON), pagination, rate limits, or authentication requirements. For a tool with no annotations, this leaves significant gaps in understanding how the tool behaves beyond its basic function.

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 ('List available experimental datasets') and adds organizational context ('organized by experiment and run period'). There is no wasted text, and it's appropriately sized for a simple listing tool with two parameters.

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 low complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose but lacks behavioral details like output format or usage context. Without annotations or output schema, the description should do more to compensate, but it falls short, resulting in a mediocre completeness score.

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%, with both parameters ('experiment' and 'runPeriod') fully documented in the schema, including enums and descriptions. The description adds no additional parameter semantics beyond implying filtering by experiment and run period, which is already covered in the schema. This meets the baseline of 3 for high schema coverage without extra value from the description.

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 verb ('List') and resource ('available experimental datasets in the Lilith database'), specifying organization by experiment and run period. It distinguishes from siblings like 'get_dataset_info' or 'search_hepdata' by focusing on listing datasets rather than retrieving detailed info or searching. However, it doesn't explicitly differentiate from 'list_cern_opendata_files', which might overlap in purpose but targets different data sources.

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, such as needing database access, or compare to siblings like 'get_dataset_info' for detailed metadata or 'search_ern_opendata' for broader searches. The lack of explicit when/when-not statements leaves the agent to infer usage from the tool name alone.

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