eleata-verify-mcp

check_groundedness

Verify that a model's generated answer is supported by the retrieved context. Detects hallucinations by returning whether the answer is grounded, helping agents retract or re-retrieve if ungrounded.

Instructions

Hallucination guard for RAG / agent answers. Pass the model's generated answer and the retrieved context; returns whether the answer is grounded in the context. Treat any verdict other than 'Supported', or abstained=true, as 'ungrounded / likely hallucination' and have the agent retract or re-retrieve.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "answer": "<answer>",
      "context": "<context>"
    },
    "name": "check_groundedness"
  }
}

Input Schema

Name	Required	Description	Default
`answer`	Yes	The model-generated answer to check for grounding.
`context`	Yes	The retrieved context the answer should be grounded in.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.1Jun 30, 2026

TDQS

A4/5.0

Behavior4/5

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

With no annotations provided, the description bears the full burden. It discloses that the tool returns a verdict and an abstained flag, with specific interpretation for agent behavior. This is adequate for a stateless validation tool, though it could elaborate on the exact response format.

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 very concise: two sentences with no wasted words. It front-loads the core purpose and then provides actionable usage guidance. Every sentence is necessary.

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

Completeness4/5

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

Given the simplicity of the tool (two string params, no output schema), the description is fairly complete. It explains the purpose, input, and interpretation of output. Minor gaps: exact return structure not specified, but the interpretation rules suffice for an agent.

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

Parameters3/5

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

Schema coverage is 100%, so baseline 3 applies. The description restates that the answer and context should be passed, adding no new semantic detail beyond the schema descriptions. It does not enhance understanding of parameter constraints or formats.

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

Purpose4/5

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

The description clearly identifies the tool as a hallucination guard for RAG/agent answers, stating it checks grounding of a generated answer against retrieved context. This is specific and distinct from siblings like verify_claim or verify_strict, though not explicitly contrasted.

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 provides clear instructions on when to use (pass answer and context) and how to interpret results (treat verdicts other than 'Supported' or abstained=true as ungrounded). It lacks explicit alternatives or when-not-to-use guidance but offers sufficient operational context.

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

Deploy Server