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deenesjakoruzh
4dmrkey

cryptopolitan-mcp

by 4dmrkey
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

100%
Profile completionA complete profile improves this server's visibility in search results.
  • Disambiguation5/5

    With only one tool, there is no possibility of selecting the wrong tool or confusing purposes between tools.

    Naming Consistency4/5

    The single tool uses clear snake_case format (connect_info), though with only one tool, a naming pattern cannot be fully established.

    Tool Count2/5

    A single tool is insufficient for the apparent scope suggested by 'cryptopolitan' (likely cryptocurrency/news domain). The rubric explicitly identifies 1 tool as 'too few' for typical scopes.

    Completeness1/5

    Severely incomplete - the server provides only a meta-configuration tool (connection details) with zero tools covering the actual domain functionality implied by the server name.

  • Average 3.2/5 across 1 of 1 tools scored.

    See the tool scores section below for per-tool breakdowns.

  • This repository includes a README.md file.

  • This repository includes a LICENSE file.

  • Latest release: v1.0.0

  • Tools from this server were used 1 time in the last 30 days.

  • This repository includes a glama.json configuration file.

  • View server inspector

  • This server provides 1 tool. View schema

  • No known security issues or vulnerabilities reported.

    Report a security issue

  • This server has been verified by its author.

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Tool Scores

  • Behavior2/5

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

    No annotations provided, so description carries full disclosure burden. States what it retrieves but omits behavioral traits: whether details are sensitive/cached/static, authentication requirements, or if this is idempotent/read-only behavior.

    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?

    Single efficient sentence with no redundancy. Every word earns its place—immediately identifies action and target resource without filler.

    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?

    Adequate for a simple parameterless getter, but lacks specification of what 'connection details' includes (endpoint URLs, authentication tokens, status metadata?). No output schema exists to compensate, leaving return value ambiguous.

    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?

    Input schema contains 0 parameters (empty object) with 100% coverage. Description correctly makes no parameter claims. Baseline score applies per rubric for zero-parameter tools.

    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?

    Uses specific verb 'Get' with clear resource 'connection details' scoped to 'Cryptopolitan MCP server'. Lacks specificity on what 'details' entails (URLs, tokens, status?) but sufficiently clear for selection.

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

    Usage Guidelines2/5

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

    Provides no guidance on when to invoke this tool vs alternatives, prerequisites, or expected calling patterns. No siblings exist, but still lacks contextual guidance on when connection details are needed.

    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.

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How to claim the server?

If you are the author of the server, you simply need to authenticate using GitHub.

However, if the MCP server belongs to an organization, you need to first add glama.json to the root of your repository.

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

Then, authenticate using GitHub.

Browse examples.

How to make a release?

A "release" on Glama is not the same as a GitHub release. To create a Glama release:

  1. Claim the server if you haven't already.
  2. Go to the Dockerfile admin page, configure the build spec, and click Deploy.
  3. Once the build test succeeds, click Make Release, enter a version, and publish.

This process allows Glama to run security checks on your server and enables users to deploy it.

How to add a LICENSE?

Please follow the instructions in the GitHub documentation.

Once GitHub recognizes the license, the system will automatically detect it within a few hours.

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

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.

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