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neco001

Python Executor MCP Server

by neco001
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Python
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Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools are clearly distinct: run_python for a single snippet and run_python_batch for parallel execution. Descriptions explicitly state when to use each, eliminating ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent pattern: 'run_python' and 'run_python_batch', with the second adding a descriptive qualifier. This is predictable and easy to understand.

    Tool Count3/5

    With only two tools, the server feels minimal. The tools cover the core need, but the count is borderline per calibration standards (1-2 tools is considered thin).

    Completeness4/5

    For a Python execution server, single and batch execution cover the primary use cases. Minor gaps exist (e.g., no explicit environment management), but the core functionality is well-covered.

  • Average 4.1/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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

  • Behavior3/5

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

    With no annotations, the description carries the full burden. It discloses parallel execution and limits (max workers, max snippets) but does not explain error handling, result ordering, isolation, or what happens when limits are exceeded, leaving notable behavioral gaps.

    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?

    Three concise sentences with no fluff: first states the core function, second gives usage context, third names the alternative and points to examples. Information is front-loaded.

    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?

    The description covers the primary use case and points to README, but omits important operational details such as timeout behavior, cwd handling, and behavior when max limits are exceeded. The output schema may cover return values, but the tool still feels incomplete for a batch execution tool.

    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?

    Schema description coverage is 0%, and the description does not explain the meaning of cwd, timeout, or max_workers beyond implicit mention of worker/snippet limits. It fails to compensate for the undocumented schema parameters.

    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 'Execute multiple Python snippets in parallel' with specific constraints (max 4 workers, max 20 snippets), distinguishing it from the sibling run_python tool by emphasizing the batch/parallel aspect.

    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?

    It explicitly states when to use this tool ('independent tasks: file processing, parallel data analysis') and when not to ('Use run_python for stateful/dependent operations'), providing clear guidance and naming the alternative.

    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 must carry the full burden. It discloses limits (1MB code, 60s timeout) and the stateful nature of execution, adding valuable context beyond the schema. However, it doesn't mention side effects, return format, or environment specifics, which would be expected for complete 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?

    The description is three sentences, front-loaded with the primary action, then usage context, then constraints and reference. Every sentence earns its place with no redundancy.

    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?

    The tool has no output schema and no annotations, so the description must compensate. It provides essential usage and constraints, and points to README for more details, but omits return value behavior and other environmental context. The pointer to README assigns completeness, but the description itself is not fully self-contained.

    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?

    Schema description coverage is 0%, so the description should explain the parameters. It mentions the code size limit and timeout constraint, but does not clarify the meaning of the 'cwd' parameter or describe the semantics of each field beyond what the schema titles already imply. This is insufficient given the lack of schema-level descriptions.

    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 verb+resource: 'Execute a single Python code snippet.' It also distinguishes itself from the sibling tool by specifying 'Use for stateful/dependent operations' vs. 'run_python_batch for parallel independent tasks.'

    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 provides when-to-use guidance ('Use for stateful/dependent operations') and names the alternative ('Use run_python_batch for parallel independent tasks'). Also states constraints (Max 1MB code, 60s timeout) and points to README for examples.

    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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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.

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