Which integrations are available for this server?

Provides claim verification services from eleata through the RapidAPI marketplace, allowing AI agents to use the hosted Claim Verifier API via RapidAPI's infrastructure.

How do I use eleata-verify-mcp?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@eleata-verify-mcp Does evidence support 'Earth orbits the Sun'?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

eleata-verify-mcp

by hernaninverso

Overview Schema Source Related Servers Discussions Admin

JavaScript

Remote

eleata Claim Verifier — MCP server

A grounding / hallucination guardrail for AI agents. Give it a claim and supporting evidence; it returns whether the evidence Supports / Refutes / gives Not Enough Evidence for the claim (natural-language inference), with a confidence and an abstained flag for low-confidence cases. Wraps the hosted eleata Claim Verifier.

Use it as a fact-check / RAG hallucination guard: before an agent trusts or repeats a statement, check it against its source. Treat abstained=true (or any verdict other than Supported) as "do not rely".

Tools

Tool	What it does
`verify_claim(claim, evidence)`	Is the claim supported by the evidence? Verdict + confidence + `abstained`.
`check_groundedness(answer, context)`	RAG guard: is the model's answer grounded in the retrieved context?
`verify_strict(claim, evidence)`	Same, with a higher abstention threshold — for compliance/legal where a wrong "Supported" is costly.

Related MCP server: Fact-Checker MCP Server

Setup

{
  "mcpServers": {
    "eleata-verify": {
      "command": "npx",
      "args": ["-y", "eleata-verify-mcp"],
      "env": { "EVERIFY_API_KEY": "your_key" }
    }
  }
}

Get a key at https://eleata.io/checkout?p=verifypro (paid channel, sent as Authorization: Bearer). For the RapidAPI marketplace channel, set EVERIFY_RAPIDAPI=1 and EVERIFY_API_BASE to the RapidAPI host (the key is then sent as X-RapidAPI-Key).

Notes

Confidence is uncalibrated on the public channels (not a probability) — rely on the verdict and the abstained flag, not the raw number. The engine is a multilingual NLI model (mDeBERTa-v3-mnli-xnli). verify sends your claim + evidence to the hosted API; see https://eleata.io/privacy/.

MIT licensed.

Available Tools

3 tools

check_groundednessA

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.

ParametersJSON 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.

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.

verify_claimA

Fact-check a claim against a piece of evidence using natural-language inference. Returns a grounded verdict — Supported, Refuted, or Not Enough Evidence — with a confidence (0..1) and an abstained flag. Use it to check whether a statement is actually backed by a source before trusting or repeating it. If abstained is true, treat the result as 'cannot verify — do not rely'.

ParametersJSON Schema

Name	Required	Description	Default
`claim`	Yes	The statement to check.
`evidence`	Yes	The source text the claim should be supported by.

TDQS

A4.1/5.0

Behavior4/5

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

With no annotations, the description fully explains behavior: returns a grounded verdict with confidence and abstained flag, and clarifies the meaning of abstained. It is transparent about the tool's operation and limitations.

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 concise at 4 sentences, with the main action front-loaded. Every sentence adds value: purpose, use case, and handling of abstained. No unnecessary words.

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?

For a 2-parameter tool with no output schema, the description adequately covers the return value details (verdict types, confidence range, abstained flag) and provides interpretive guidance. It is complete for the complexity level.

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 description coverage is 100%, so baseline is 3. The description adds context about the tool's purpose but does not add significant meaning beyond the schema's parameter descriptions for claim and evidence.

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 states the tool's purpose: fact-checking a claim against evidence using natural language inference. It specifies the output verdict types and additional fields, but does not differentiate from siblings like check_groundedness or verify_strict.

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 explicit usage guidance: 'Use it to check whether a statement is actually backed by a source before trusting or repeating it'. It also explains how to handle the abstained flag. However, it does not mention when not to use it or alternatives.

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

verify_strictA

Same as verify_claim but with a raised abstention threshold — abstains more readily. Use when a wrong 'Supported' is costly (compliance, legal, medical-adjacent) and the agent must not over-assert.

ParametersJSON Schema

Name	Required	Description	Default
`claim`	Yes	The statement to check.
`evidence`	Yes	The source text the claim should be supported by.

TDQS

A3.9/5.0

Behavior3/5

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

No annotations provided, so description carries burden. It discloses the key behavioral trait of higher abstention threshold, but does not describe output format, whether it is read-only, or other side effects.

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?

Two sentences with no wasted words, efficiently conveying purpose and usage context.

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

Completeness3/5

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

No output schema and no annotations; description does not explain return values or error states. However, given the tool's simplicity and sibling context, it provides enough for basic use.

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?

Input schema covers 100% of parameters with clear descriptions. Description adds no extra meaning beyond stating similarity to verify_claim, but schema descriptions are sufficient.

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?

Description clearly states it's the same as verify_claim but with a raised abstention threshold, distinguishing it from siblings. It names the verb+resource (verify claim) and the specific behavioral difference.

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?

Explicitly advises use when a wrong 'Supported' is costly (compliance, legal, medical-adjacent), implying when not to use (low-cost errors). Could be improved by directly stating alternatives like verify_claim.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

3 tool updatesv0.1.1Jun 30, 2026
- First observedcheck_groundedness
- First observedverify_claim
- First observedverify_strict

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.7/5.0

Scored 2026-06-30 12:25 across 3 tools

Disambiguation2/5

verify_claim and verify_strict are nearly identical except for an abstention threshold, making it unclear which to choose. check_groundedness overlaps in purpose (checking support in context/evidence). Agents will struggle to differentiate without additional heuristics.

Naming Consistency3/5

Two tools use 'verify_' (one with noun 'claim', one with adjective 'strict'), and one uses 'check_groundedness'. The verbs 'check' and 'verify' are synonymous but break pattern consistency. The mix of noun and adjective styles is acceptable but not uniform.

Tool Count4/5

With 3 tools, the count is slightly low but appropriate for a narrowly scoped verification server. Each tool serves a distinct mode (normal, strict, groundedness), so no tool feels superfluous.

Completeness3/5

Covers the core verification operations but lacks any tool for evidence retrieval, source management, or batch verification. The strict version adds nuance but also indicates incomplete coverage of confidence levels. Agents may need to work around missing functionality.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

Need Help?

Related Servers

Related MCP Connectors

Agenda Intelligence MD A
io.github.vassiliylakhonin
Evidence-readiness MCP server: validate, audit, and score briefs, memos, and evidence packs.
Legal & Compliance Research & Data
116
Probe V0 A
io.github.lexbritten
Experimental MCP server for current empirical verification of explicit public HTTPS endpoint claims.
API Testing Penetration Testing
21
mcp AOAuth
io.scorecard
MCP server providing access to the Scorecard API to evaluate and optimize LLM systems.
RAG Systems Monitoring Testing & QA Tools
3
mcp A
ai.llmse
Public MCP server for the LLM Search Engine
Search RAG Systems Developer Tools
102

Related MCP Servers

tru8-mcp
Research & Data Search Knowledge & Memory
SamYatesSmith
A
license
A
quality
C
maintenance
MCP server for Tru8 Evidence Research that enables AI agents to submit claims or URLs and receive source-traced evidence organized by tier and type with element decomposition and relationship mapping.
Updated a month ago (2026-08-13 15:21 UTC)
3
50 PyPI
MIT
Fact-Checker MCP Server
AI & Machine Learning Research & Data Knowledge & Memory
luminarylane
A
license
Not graded
quality
A
maintenance
This MCP server enables AI agents to extract, verify, and cache factual claims from text using Claude Haiku and semantic fingerprinting, with tools for content verification, signal verification, and fact memory search.
Updated 14 days ago (2026-09-07 03:55 UTC)
12 npm
MIT
corroborate-mcp
Search Research & Data
chefcohen
A
license
A
quality
A
maintenance
An MCP server that evaluates claims by counting independent story origins, giving AI agents a verdict on reporting corroboration rather than truth.
Updated 2 months ago (2026-07-29 14:22 UTC)
2
35 npm
1
MIT
mcp-citation-checker
RAG Systems Search
ethanchand
A
license
Not graded
quality
C
maintenance
An MCP server that verifies whether a claim is actually supported by the source text at a given citation — independent of what the calling LLM asserts.
Updated 2 months ago (2026-07-25 23:57 UTC)
MIT