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maxim-saplin

Safe Local Python Executor/Interpreter

by maxim-saplin
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap between tools. The single tool 'run_python' has a clearly defined and distinct purpose.

    Naming Consistency5/5

    The naming follows a consistent verb_noun pattern with 'run_python'. Since there's only one tool, consistency is inherently perfect with no deviations to assess.

    Tool Count2/5

    A single tool is too few for the server's purpose as a 'Safe Local Python Executor/Interpreter'. This suggests a thin surface that may lack essential operations like code validation, environment inspection, or error handling, limiting agent functionality.

    Completeness2/5

    The tool set is severely incomplete for the domain. While 'run_python' covers execution, there are obvious gaps such as tools for listing allowed imports, checking code safety, managing execution timeouts, or handling errors, which are critical for a secure interpreter.

  • Average 4.3/5 across 1 of 1 tools scored.

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

    • 0 of 1 community issues answered or closed 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 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.

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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

  • Behavior4/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: the secure sandbox environment, restrictions for security, requirement to create a single executable file, and default allowed imports. However, it doesn't cover aspects like execution time limits, memory constraints, or error handling, leaving some 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.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is well-structured and front-loaded with the core purpose, followed by usage notes, allowed imports, parameter details, and return information. It's appropriately sized for the tool's complexity, but the list of allowed imports and code example add some length that could be streamlined without losing clarity.

    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 tool's complexity (code execution with security constraints), no annotations, and an output schema that documents return values, the description is highly complete. It covers purpose, behavioral traits, parameter semantics with examples, and usage context, leaving minimal gaps for an AI agent to understand and invoke the tool correctly.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema has 0% description coverage, so the description must fully compensate. It adds substantial meaning beyond the schema by detailing the 'code' parameter: it must be valid Python 3 code, require storing results in a variable called 'result', and includes a clear example with syntax and usage. This provides comprehensive semantic context for the single parameter.

    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: 'Execute Python code in a secure sandbox environment' with specific verbs ('execute', 'run') and resource ('Python code'). It distinguishes the tool's scope by mentioning it's for 'simple Python code for calculations and data manipulations' and operates in a restricted environment, making its function unambiguous even without siblings.

    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 context by specifying it's for 'simple Python code for calculations and data manipulations' and mentions security restrictions, but it lacks explicit guidance on when to use this tool versus alternatives (e.g., other code execution tools or manual methods). No siblings are listed, so differentiation isn't needed, but general usage context is only partially addressed.

    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.

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