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universal-research-mcp

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memory_check_evidence_eligibility

Read-onlyIdempotent

Check exact evidence integrity and count to determine if it meets eligibility for a claim. Classify the claim type to activate the evidence gate.

Instructions

Check exact evidence integrity and count, never semantic claim support.

claim_type is deliberately required: the gate activates from claim consequence, so a caller must classify the claim instead of inheriting a silent inactive default.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
claimYes
evidenceNo
claim_typeYes
materialityNoauto

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.9.3
    • changedOutput schema / (root)
      Previous value: -{
-  "additionalProperties": true,
-  "title": "memory_check_evidence_eligibilityDictOutput",
-  "type": "object"
-}New value: +null
  2. Changed2 schema fields changedv0.7.1
    • removedInput schema / properties / claim_type / default
      Removed value: -"factual"
    • changedInput schema / required
      Previous value: -[
-  "claim"
-]New value: +[
+  "claim",
+  "claim_type"
+]
  3. Addedv0.7.0

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds useful behavioral context beyond that: the gate activates based on claim consequence, and omitting claim_type would silently produce an inactive default. This is a meaningful non-obvious behavior disclosure.

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

Conciseness5/5

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

The description is short, front-loaded with the core scope, and every sentence earns its place. The second paragraph adds a specific design caveat rather than repeating schema information or filling space.

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

Completeness2/5

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

The tool has no output schema, and the description does not describe the result shape, what 'count' or 'integrity' operationally returns, how null evidence is handled, or how materiality affects behavior. It also does not route the agent relative to the many sibling tools, leaving an incomplete invocation picture.

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

Parameters2/5

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

With 0% schema description coverage, the description must compensate for undocumented parameters, but it only explains claim_type's requiredness. Claim, evidence, and materiality receive no semantic guidance, so an agent cannot confidently know how to populate or interpret those parameters beyond their bare names and enums.

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 states a concrete operation: checking exact evidence integrity and count, and explicitly negates semantic claim support. This gives an agent a specific verb, resource, and boundary without needing to infer from the name or schema.

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

Usage Guidelines3/5

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

It communicates a clear non-goal ('never semantic claim support') and explains why claim_type is mandatory, but it does not name sibling alternatives or give explicit conditions for when to choose this tool over memory_fetch_evidence, governance_evaluate_gate, or similar operations. Usage context is implied rather than explicitly routed.

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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