Skip to main content
Glama

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool targets a distinct operation: browsing shelves, searching entries, fetching one entry, live-probing an endpoint, and retrieving a paid verdict. The free technical probe and paid editorial verdict are explicitly differentiated, so there is no realistic confusion.

    Naming Consistency4/5

    Most tools follow a consistent gold402_verb_noun pattern (gold402_list_sections, gold402_get_entry, gold402_check_endpoint, gold402_search). The one exception is gold402_verdict, which uses a noun instead of a verb, creating a minor but noticeable deviation.

    Tool Count5/5

    Five tools is exactly right for this directory-style MCP server. Each tool earns its place: browse, search, detail, live verification, and paid human judgment. The scope is tight and no tool feels redundant.

    Completeness5/5

    The server covers the full user workflow: discover categories, search entries, retrieve full records, independently probe any endpoint, and optionally purchase an editorial verdict. The paid verdict flow with signature-based settlement is included, so there are no dead ends.

  • Average 4.3/5 across 5 of 5 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
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
      "$schema": "https://glama.ai/mcp/schemas/server.json",
      "maintainers": [
        "your-github-username"
      ]
    }

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    The description adds context about the hand-probed, curated nature of entries and specifies output fields including provenance stamp, going beyond the annotation hints to give users confidence in reliability and result contents.

    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, front-loaded with the primary action, and structured in three clear sentences covering purpose, curation, and features. It avoids unnecessary detail.

    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?

    Given the detailed schema and output schema, the description provides sufficient context for usage without redundant explanations. It covers what the tool returns and the quality of entries, making it complete.

    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?

    The schema already provides detailed descriptions for each parameter (limit, query, offset, section, response_format). The tool description only paraphrases filtering and pagination, adding minimal extra meaning beyond what the schema covers.

    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 identifies the tool's function as searching the curated x402 directory, specifying the types of entries it covers. It distinguishes itself from sibling tools like list_sections and get_entry by focusing on search.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies usage for searching and mentions filtering/pagination, but does not explicitly state when to prefer this tool over alternatives like list_sections. It lacks direct comparative guidance.

    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?

    Annotations already declare read-only/idempotent/non-destructive behavior, and the description adds beyond that by saying the tool returns counts and section IDs and how those IDs feed into search. This is useful behavioral/return context without contradicting the annotations.

    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 short, purposeful sentences: the first names the action and result, the second gives the concrete integration hint. No filler, repetition, or wasted 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 simple list tool with safe annotations, a one-parameter schema, and an output schema, this description is complete: it explains what the tool lists, what it returns, and how to use the result in a sibling tool. No missing contextual element is apparent.

    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?

    The description is silent on the response_format parameter, but the schema itself documents that parameter thoroughly with an enum, default, and usage note. The description adds no parameter-level meaning beyond the schema, so a baseline score of 3 is appropriate.

    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 uses a concrete verb and resource ('List the shelves (categories) of the gold-402 directory') and adds a useful output detail ('entry count for each'), clearly distinguishing it from the sibling search/retrieval/endpoint tools.

    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 tells the agent the primary downstream use ('Use the returned section id as the 'section' filter in gold402_search'), which gives clear context for when to call it. It does not explicitly say when not to use it versus alternatives, so it stops 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.

  • Behavior5/5

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

    Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds meaningful behavioral context beyond those: the $0.001 USDC payment, the two-call settlement flow, and the fact that the server never holds a wallet. No contradiction with annotations.

    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 dense but efficient: it front-loads the paid nature, defines the output, distinguishes the tool from a sibling, and explains the two-call flow in four short sentences. Minor redundancy with schema text on payment_signature costs it a point.

    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 output schema exists and the main behavioral complexity is the payment handshake, the description is largely sufficient. It explains what the verdict contains, how to pay, and what makes it different from the free check. It does not mention the markdown response_format option, but that is visible in the schema.

    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?

    The description adds useful meaning about the payment_signature workflow and the resource scope, but the schema already documents resource and payment_signature semantics. The claim that output is 'as JSON' is potentially confusing because response_format defaults to markdown and allows json, and response_format is not discussed in the description.

    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 names a specific, differentiated purpose: 'the 24K Labs editorial verdict for one x402 resource, plus aggregated independent rater grades.' It also explicitly contrasts with gold402_check_endpoint, making the tool's role clear among its siblings.

    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?

    It gives concrete usage guidance: call once without payment_signature to receive payment terms, then call again with the signature to settle. It explicitly distinguishes from the free gold402_check_endpoint, though it does not address when to choose this over gold402_search, list_sections, or get_entry.

    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?

    The description discloses specific behavioral details beyond annotations: it sends one unpaid request, does not spend money, does not verify delivery, and checks the manifest. These details are not captured by readOnlyHint or openWorldHint, adding valuable context for the agent to manage expectations.

    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 front-loaded with the core purpose, followed by specific outputs and limitations. It is concise (five sentences) and every sentence adds value—no filler, no redundancy, and the structure flows logically.

    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?

    Given that an output schema exists (not shown but indicated), the description need not detail return formatting. It covers the essential output elements (status, 402 challenge, payment terms, manifest check) and clearly states constraints (does not spend money, does not verify delivery), making it complete for an agent to decide when and how to use it.

    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?

    Despite the schema having descriptions for url, method, and response_format, the coverage signal is 0% (likely top-level params lacks description). The tool description does not compensate by explaining how to use these parameters; it omits any mention of method or response_format, leaving the agent to rely solely on schema descriptions for parameter guidance.

    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: to live-probe any x402 endpoint, sending an unpaid request and reporting liveness, HTTP status, payment challenge, and terms. It explicitly distinguishes from siblings (search, list_sections, get_entry, verdict) by focusing on endpoint probing rather than content retrieval, and clarifies it applies to any endpoint, listed or not.

    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 it ('before paying it') and explicitly states what it does NOT do (spend money or verify delivery). It implies this is for pre-payment validation but does not explicitly name alternative tools for delivery verification or content exploration, which minor gap prevents 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?

    The description discloses the matching behavior (exact then partial) and lists return fields. Annotations already provide safety hints (read-only, idempotent), so the description adds meaningful behavioral detail without redundancy.

    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 two concise sentences, free of fluff, and clearly structured. It communicates purpose and output without unnecessary detail.

    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?

    Given the low complexity (single entry fetch) and presence of an output schema, the description sufficiently covers the return content by listing all key fields (URL, description, shelf, date first listed, permanent listing page). No gaps found.

    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?

    Both parameters have schema descriptions, and the tool description enriches the 'name' parameter by explaining the exact/partial matching semantics. The response_format parameter is adequately described in the schema, so no additional clarification is needed.

    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 fetches a single gold-402 entry by name, with exact match preferred and fallback to partial. This precisely defines the action and distinguishes from search-oriented siblings.

    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?

    It explicitly indicates when to use: to retrieve a single entry by name. While it doesn't contrast with sibling tools like gold402_search, the scope is unambiguous enough for the agent to select appropriately.

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

GitHub Badge

Glama performs regular codebase and documentation scans to:

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

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

gold402-mcp MCP server

Copy to your README.md:

Score Badge

gold402-mcp MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Haustorium12/gold402-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server