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multivon-mcp

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by multivon-ai

eval_discover

Retrieve a complete catalog of available evaluators, trap families, and test suites to plan your evaluation strategy without guessing tool names.

Instructions

Return the full machine-readable capability catalog.

Useful as a first call at session start — an agent can plan its evaluation strategy against the actual available evaluators rather than guessing or hallucinating tool names.

Returns: A dict with three top-level keys:

- ``evaluators``: every available multivon-eval evaluator,
  with its tier, what inputs it needs, and (when shipped)
  calibrated default thresholds per judge model.
- ``traps``: every pdfhell trap family, the failure mode each
  elicits, and the expected_failure_mode metadata.
- ``suites``: every named pdfhell suite, the (trap_family,
  seed_count) breakdown, and the suite_hash for the canonical
  version.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses the return structure in detail (three keys: evaluators, traps, suites) and mentions it is machine-readable. It does not mention side effects or permissions, but the tool is read-only and safe.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is fairly concise with a clear structure: a lead sentence, usage note, and a bullet-like list of return keys. It could be slightly shorter, but it is well-organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (zero parameters), the description is complete. It explains the purpose, usage context, and output structure in sufficient detail. Even with an output schema present, the description provides all necessary information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, and schema coverage is 100% (no props). The description adds value by detailing the output structure, which is not captured in the input schema. This compensates for the lack of parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the purpose: 'Return the full machine-readable capability catalog.' It distinguishes itself from sibling evaluation tools by positioning itself as the initial discovery call, unlike the other eval tools that perform specific evaluations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly advises using this tool as a first call at session start to plan evaluation strategy, providing clear context. It does not explicitly say when not to use it, but the sibling names imply it is for initial discovery before other tools.

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

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