Elenchus MCP Server

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 mentions the action ('Submit') but does not clarify what happens after submission—such as whether it stores data, triggers processes, or has side effects like updates to sessions or evaluations. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior and impact.

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 concise and front-loaded in a single sentence, clearly stating the purpose without unnecessary details. However, it could be slightly more structured by explicitly mentioning the tool's role in the evaluation workflow, but overall, it is efficient with minimal waste.

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 submitting evaluations, no annotations, and no output schema, the description is incomplete. It lacks details on what the tool returns, error handling, or how it integrates with other tools like 'elenchus_submit_round'. This makes it inadequate for guiding an agent in a full context, especially with multiple sibling tools involved.

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 input schema has 100% description coverage, so the schema already documents all parameters (sessionId, evaluationType, llmResponse, targetId) with descriptions and enums. The description does not add any additional meaning beyond the schema, such as explaining parameter interactions or usage examples, resulting in a baseline score of 3.

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 ('Submit LLM evaluation response') and resource ('LLM evaluation'), specifying it should be called after receiving an LLM response to an evaluation prompt. However, it does not explicitly differentiate from sibling tools like 'elenchus_evaluate_convergence' or 'elenchus_evaluate_severity', which might handle similar evaluation contexts, leaving some ambiguity about its unique role.

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 implied usage by stating 'Call this after receiving an LLM response to an evaluation prompt,' which gives a timing context. However, it lacks explicit guidance on when to use this tool versus alternatives like the evaluation-specific sibling tools (e.g., 'elenchus_evaluate_convergence'), and does not mention any exclusions or prerequisites, making it somewhat vague.

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