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hernaninverso

eleata-verify-mcp

by hernaninverso
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

75%
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  • Latest release: v0.1.1

  • 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.

  • Average 4/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 3 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • 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.

  • 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.

  • 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.

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