Skip to main content
Glama
attestagents

attest-mcp

Official
by attestagents
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Hybrid

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools are clearly distinct: attest_scan performs a full scan of an endpoint and returns a grade, while attest_grade retrieves a previously cached grade. There is no ambiguity about which to use.

    Naming Consistency5/5

    Both tools follow the consistent verb_noun pattern 'attest_scan' and 'attest_grade', using the same prefix and a clear action word.

    Tool Count4/5

    With only two tools, the set is minimal but focused. It covers the core scan-and-check workflow without unnecessary bloat. A third tool for listing or managing hosts would be nice but is not essential.

    Completeness3/5

    The two tools cover the primary use case of scanning and retrieving grades, but there are missing operations like listing all scanned hosts, deleting a host, or forcing a rescan. The surface is somewhat incomplete for lifecycle management.

  • Average 4.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 4 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.

    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

  • Behavior3/5

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

    No annotations are provided, so the description carries the full burden. It mentions the operation is quick, but does not disclose additional behavioral traits such as whether the host must exist, what happens if not found, or any side effects. Since it is a read operation, the basic purpose is clear, but more detail would improve transparency.

    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, front-loaded with the main action, no extraneous words. Efficient and to the 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 tool is simple (1 parameter, output schema exists), the description sufficiently covers purpose and usage. It could optionally mention the output format, but the output schema likely handles that. Slight room for improvement.

    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% for the single parameter, with the schema already explaining the parameter well. The tool description adds no extra meaning beyond what the schema provides, so 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 clearly states the tool looks up the most recent Attest grade for a host that has been scanned, using specific verb (look up) and resource (host grade). It distinguishes from the sibling tool attest_scan, which is for hosts that haven't been scanned.

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

    Usage Guidelines5/5

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

    Explicitly tells when to use this tool ('fast pre-check') and when not to ('host has never been scanned, use attest_scan instead'), providing clear guidance on alternatives.

    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 the full burden. It accurately describes the tool's action (scanning) and output (letter grade, safety verdict). However, it omits details like making a network request, potential latency, or side effects. Still, the core behavior is clear.

    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, each essential: first defines what the tool does, second provides usage guidance. No wasted words or 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 the tool has one parameter and an output schema (though not shown), the description covers the purpose, input, and output. Could mention whether the scan is synchronous or if there are side effects, but overall adequate for a focused tool.

    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?

    Schema coverage is 100% (1 param 'url' with description). The description reinforces that the URL should be a payment endpoint and provides example protocols. It adds meaningful context beyond the schema's generic 'uri' format.

    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 scans a payment endpoint (listing protocols like x402, MPP) and returns a letter grade with safety verdict. It distinguishes from the sibling 'attest_grade' by implying this tool performs the scan, while the sibling likely reads an existing result.

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

    Usage Guidelines5/5

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

    Explicitly advises calling this tool 'BEFORE authorizing a payment to an unfamiliar endpoint,' listing specific risks to check (impersonation, blocklisted wallets, bait-and-switch pricing, protocol problems). Provides clear when-to-use context.

    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

Install-attest-agent MCP server

Copy to your README.md:

Score Badge

Install-attest-agent 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/attestagents/Install-attest-agent'

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