Skip to main content
Glama

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.1.1

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusing it with others. The tool's purpose is clearly stated, making selection unambiguous.

    Naming Consistency5/5

    The single tool name 'execute_code' follows a clear verb_noun pattern and accurately reflects its function. Consistency is trivially maintained.

    Tool Count4/5

    At one tool, the server is minimal but appropriate for its narrow purpose of code execution. It is not a trivial tool, and no additional tools seem necessary for the described sandbox functionality.

    Completeness5/5

    The server fully covers its stated domain: executing Python and JavaScript in a sandbox. All necessary parameters and constraints are documented, and there are no obvious missing operations for this focused use case.

  • Average 4.7/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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

  • Behavior5/5

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

    The description goes far beyond the annotations by detailing the gVisor isolation, read-only filesystem except /tmp, 10-second execution limit, 256 MB memory, 0.5 CPU, lack of network access, disposable fresh sandbox per call, and that only stdout/stderr are returned. It also clarifies that nothing persists between calls. This discloses behavioral traits and constraints comprehensively, and it does not contradict 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?

    The description is well-structured with a clear opening sentence, a use-case rationale, and a 'Read these limits' bulleted list. The length is appropriate for the tool's complexity — every sentence carries essential information about safety, constraints, or output behavior. It is front-loaded with purpose and usage, making it easy to scan.

    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 sandbox's complexity and the absence of an output schema, the description thoroughly covers the environment, resource limits, persistence semantics, return values (stdout/stderr only), and security model. It provides enough context for an agent to use the tool correctly and anticipate failures such as no network or missing packages. No critical operational detail appears omitted.

    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?

    The schema already provides 100% coverage of both parameters, including descriptions for `code` (self-contained, must print to stdout) and `language` (enum, default, runtime versions). The description adds meaningful guidance beyond the schema, such as explicitly noting that a bare expression returns nothing, listing available stdlib modules, and emphasizing self-containedness. These additions help the agent construct valid code, though some repetition of schema text exists.

    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 precise statement: 'Execute Python or JavaScript in a secure, isolated, disposable sandbox and return its output.' This gives a specific verb, resource, and environment, making the tool's core purpose immediately clear. Since there are no sibling tools, the description doesn't need to differentiate, but it still does by emphasizing isolation/disposability.

    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 guides usage: 'Use it whenever running code gives a more reliable answer than reasoning about it' with concrete examples like checking scripts, exact calculations, and testing regex. It also communicates when not to use it through constraints such as no network, no third-party packages, and no persistence, though it does not name alternative tools.

    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

cinch-mcp MCP server

Copy to your README.md:

Score Badge

cinch-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/yusufkadry/cinch-mcp'

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