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proofjson

mcp-proofjson

by proofjson
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Server Quality Checklist

67%
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  • Latest release: v0.1.1

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: assessing invoice risk, listing verification packs, and verifying proofs. No overlap or ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with snake_case (assess_invoice, list_proofjson_packs, verify_proofjson_proof).

    Tool Count4/5

    Three tools is slightly low but appropriate for the narrow scope of invoice risk assessment with ProofJSON v1. The set feels minimal but not insufficient.

    Completeness3/5

    The tools cover assessment, listing capabilities, and verification, but lack configuration, invoice management, or external verification tools. Gaps exist for a full workflow, but acceptable for a v1 limitation.

  • Average 4.3/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 2 commits in the last 12 weeks
    • Last stable release on
    • 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.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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

  • Behavior5/5

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

    Even without annotations, the description fully discloses the tool's behavioral traits: it does not verify real-world truth, is supplied_data_only, and does not independently verify external facts unless trust adapters are configured. This goes beyond what annotations typically provide.

    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 long but each sentence adds an important caveat or clarification. It is front-loaded with the main action and structured logically, with only minor redundancy.

    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?

    While the description covers purpose and limitations well, it omits details about the return value/output format and does not explain parameters. Given no output schema and no annotations, this is a moderate gap.

    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?

    The description does not explain the meaning or usage of the individual parameters (proof, expected_subject, expected_hash, options). With 0% schema description coverage, the description should compensate, but it only mentions 'supplied invoice data, supplied context and configured policy' in a general sense.

    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 tool's purpose: 'Verify a ProofJSON proof for structure, hash consistency and signature validity.' It uses a specific verb ('Verify') and resource ('ProofJSON proof'), and distinguishes from siblings by focusing on proof verification versus invoice assessment or listing packs.

    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 context on when to use the tool and its limitations, but does not explicitly compare to sibling tools. It clarifies what the tool does NOT verify (e.g., real-world truth, supplier identity) and states that 'allow is not a guarantee.'

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

  • Behavior5/5

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

    With no annotations, the description fully discloses limitations: no independent verification unless external adapters configured, and that 'allow' is not a guarantee. This is comprehensive behavioral transparency.

    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?

    Description is a single paragraph of four sentences, front-loaded with purpose and return value. It is efficient but could be slightly more concise; no wasted words.

    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?

    For a complex tool with nested objects and no output schema, the description covers purpose, return structure, limitations, and behavioral traits adequately. Minor omissions (e.g., detailed output fields) but sufficient for safe 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?

    Schema description coverage is 100%, so baseline 3. The description adds context about 'ProofJSON v1' and 'supplied_data_only' but does not significantly enhance parameter understanding beyond the schema.

    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 tool assesses invoice payment risk before actions like paying or approving, and distinguishes from siblings (list_proofjson_packs, verify_proofjson_proof) which deal with different ProofJSON operations.

    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 specifies when to use (before paying/approving/scheduling/escalating) and what it does not do (e.g., independent verification of IBAN ownership, sanctions). Lacks explicit when-not-to-use but provides good 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?

    No annotations are provided, so the description carries full burden. It discloses that ProofJSON v1 is supplied_data_only and does not independently verify certain aspects unless configured. This adds valuable behavioral context beyond just 'list packs'.

    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 with two sentences: the first states purpose, the second adds critical context. Every sentence earns its place with no redundancy.

    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 no parameters, no output schema, and no annotations, the description sufficiently explains the tool's purpose and the context of the packs. It could potentially mention output format but is complete enough for use.

    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?

    With 0 parameters, the description adds value by explaining the nature of the listed items (packs and their limitations). This is beyond what the empty schema provides, achieving the baseline 4 for parameterless tools.

    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 tool lists live ProofJSON Pack Tasks (verification capabilities). It distinguishes from siblings like assess_invoice_before_payment and verify_proofjson_proof by focusing on listing available packs rather than performing assessments or verifications.

    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 context on when to use the tool by explaining what ProofJSON v1 does and does not do, implying its use is for understanding available verification packs. However, it lacks explicit direct comparison to siblings or explicit 'use this when' guidance.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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