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Server Quality Checklist

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  • Latest release: v0.1.0

  • Disambiguation4/5

    The tools are mostly distinct: explain_proof describes a proof, compute_file_hash hashes a file, and verify_file/verify_hash both perform verification but are clearly differentiated by whether the input is a local file or an already-computed hash. The only mild overlap is between the two verification tools, but their input differences make selection straightforward.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern: explain_proof, compute_file_hash, verify_file, verify_hash. The naming style is uniform and predictable, making it easy to guess what each tool does.

    Tool Count5/5

    Four tools is well-scoped for a proof-verification server. Each tool serves a clear purpose in the workflow: understanding a proof, hashing a file, verifying a file against a proof, and verifying a precomputed hash against proof data.

    Completeness5/5

    The tool surface covers the full verification workflow: obtain a file hash, understand proof contents, verify a file against a proof, and verify an inline hash against proof data. There are no obvious dead ends or missing operations for the stated purpose.

  • Average 4.5/5 across 4 of 4 tools scored.

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

    • No community issues in the last 6 months
    • 8 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.

  • This repository includes a glama.json configuration file.

  • This server has been verified by its author.

  • Add related servers to improve discoverability.

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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

  • Behavior4/5

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

    With no annotations provided, the description carries the full behavioral disclosure burden. It does well by stating that the file is hashed locally and never uploaded, that SHA-256 is the default, and that the result is a hex-encoded digest. It does not discuss edge cases like large-file handling, but for this tool the core behavior is well covered.

    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 well-structured with purpose first, followed by Args and Returns. The blockchain context sentence is somewhat extra but still relevant to the intended use case, and the overall length is appropriate.

    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 simple two-parameter tool, the description covers everything needed to call it correctly: the file path, optional algorithm, default behavior, local-only processing, and return format. The output schema exists, so the return value description is sufficient.

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

    Parameters5/5

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

    Schema description coverage is 0%, so the description must fully explain the parameters, and it does. It explains file_path as a local path and algorithm with its default and valid Python hashlib options, adding substantial meaning beyond the bare schema.

    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 a specific action and resource: computing the cryptographic hash of a local file. It even provides use cases (obtaining a proof fingerprint, confirming a file has not changed), but it does not explicitly differentiate itself from siblings like verify_hash or verify_file beyond the implied compute-vs-verify distinction.

    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 gives concrete scenarios for when to use the tool: obtaining the fingerprint for a timestamp proof or confirming a file has not changed. It does not explicitly mention when not to use it or name alternative tools, but the context is clear enough to guide selection among siblings.

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

  • Behavior3/5

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

    With no annotations, the description carries the full burden, and it does disclose the input style, the operation, and the return format ('human-readable verification summary followed by the full structured result as JSON'). However, it does not mention failure modes, error conditions, or whether the tool is strictly non-mutating beyond what the operation inherently implies.

    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 well-structured and front-loaded with the primary purpose, followed by a clear usage condition, parameter details, and returns. Every sentence contributes value without repetition or filler.

    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 two-parameter tool with an output schema, the description covers the decision context, parameter semantics, and expected return shape. No critical information for invoking the tool correctly appears to be missing.

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

    Parameters5/5

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

    Schema description coverage is 0%, so the description fully compensates. It specifies file_hash as 'hex-encoded SHA-256 hash' and proof_json as 'a JSON string, OR a path to a proof JSON file', adding meaningful format and alternative-value semantics not present in 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 opens with a specific verb and object: 'Verify a known file hash against inline proof data.' It further clarifies the scope by noting the agent already has the hash and proof content and does not need to read a file from disk, which distinguishes it from siblings like verify_file and compute_file_hash.

    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 explicitly states when to use the tool: 'Use this when you already have a file's SHA-256 hash and the proof content... and do not need to read a file from disk.' This gives a clear usage context and an implicit when-not condition, but it does not name alternative sibling tools explicitly, so the guidance isn't quite at the level of directly routing to an alternate.

    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 carries the full transparency burden and does well: it discloses the local hash recomputation, Merkle-root recomputation when applicable, transaction-reference confirmation, and what a passing result does and does not mean. It stops short of stating whether any network access occurs or how failures/mismatches are surfaced.

    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 well-structured: purpose first, then verification mechanics, then parameter definitions, then return-value note. Every sentence contributes useful information, and there is no redundant restating of the tool name.

    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 tool with an output schema and only three simple string parameters, the description is nearly complete. It explains the verifier's behavior, parameter roles, and return structure. Remaining gaps are minor: no explicit failure/error behavior and no statement about whether the tool contacts the blockchain network.

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

    Parameters5/5

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

    Schema description coverage is 0%, so the description must fully document parameters. It does: file_path is the local file, proof_path is the proof JSON produced by ProofLedger or a compatible service, and algorithm has its default noted. This adds real meaning beyond the bare 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 opens with a specific verb-resource pairing: 'Verify a local file against a blockchain-anchored timestamp proof.' It then explains exactly what verification involves (recomputing the file hash and comparing to the proof), which clearly distinguishes this from siblings like compute_file_hash or verify_hash.

    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 intended use case is clear: use this when you have a local file plus a proof JSON and want to confirm byte-for-byte integrity against a timestamped anchor. It provides useful context but does not explicitly state when-not-to-use or name alternatives, so it falls just short of a 5.

    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 carries the transparency burden. It discloses a read-only behavior ('Reads the proof's metadata'), says the original file is not needed, and implies it does not perform verification itself ('how to independently verify'). It omits error handling details, but for a simple explainer this is adequate.

    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 front-loads the core purpose, then adds a compact metadata summary and clean Args/Returns sections. Every sentence earns its place without redundancy.

    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?

    With a single well-documented parameter, a clear return description, and an output schema present, an agent has everything necessary to select and invoke the tool correctly. The only minor gap is explicit sibling routing, already covered in usage_guidelines.

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

    Parameters5/5

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

    Schema coverage is 0% — the schema only says proof_json is a string. The description's Args section fully compensates by explaining the string can be a JSON payload OR a path to a proof JSON file, adding essential meaning 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 first sentence names a specific verb ('Explain') and resource ('blockchain timestamp proof'), and the rest clarifies it reads metadata and describes verification. This clearly distinguishes it from sibling tools that compute or verify hashes/files.

    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 by noting it 'does not require the original file' and works by reading proof metadata. However, it does not explicitly name alternative tools or state when not to use it, so it falls short of a full 5.

    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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