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pdwi2020

mcp-server-colab-exec

by pdwi2020
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation4/5

    Tools serve distinct use cases: inline code, file execution, and artifact collection. However, colab_execute and colab_execute_notebook both take 'code' parameter, risking slight confusion if descriptions aren't read carefully.

    Naming Consistency5/5

    All tools follow a consistent 'colab_execute_<action>' pattern using snake_case. The naming is predictable and clear.

    Tool Count4/5

    Three tools cover the core action of executing code on Colab. The count is slightly low but each tool provides distinct functionality, making it appropriate for the focused scope.

    Completeness3/5

    The set covers code execution and artifact retrieval, but lacks tools for managing runtimes (e.g., list, stop) or retrieving outputs separately. This creates minor operational gaps for agents.

  • Average 4.1/5 across 3 of 3 tools scored.

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

    • 1 of 2 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?

    The description explains the execution process, artifact scanning, zipping, and downloading, which adds value beyond annotations. Annotations already indicate non-readonly and non-destructive, and the description aligns with that.

    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 reasonably concise, with a clear header and bulleted parameter list. It could be slightly more compact by omitting the 'Args' label, but it remains efficient and scannable.

    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?

    The description covers the main workflow and parameters effectively. Although the output schema exists (not shown), the description adequately sets expectations for what the tool does, making it sufficiently complete.

    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 no descriptions (0% coverage), so the description's parameter documentation (code, output_dir, accelerator, timeout) provides essential meaning that the schema lacks, fully compensating for the gap.

    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?

    The purpose is clearly stated: execute Python code on Colab GPU and collect downloaded artifacts. However, it does not differentiate from sibling tools colab_execute and colab_execute_file, which may have overlapping functionality.

    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?

    No guidance is provided on when to use this tool versus its siblings or when not to use it. The description implies usage but does not explicitly state context or exclusions.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior3/5

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

    Annotations already indicate non-read-only and non-destructive behavior. Description adds that it allocates a GPU and returns JSON, but omits potential state changes from code execution. No contradiction.

    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?

    Extremely concise: one-sentence summary, allocation details, and bulleted Args. No redundant information.

    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?

    Adequate for a simple 3-param tool with an output schema. Could mention execution mode (async? blocking?) and setup overhead, but not required.

    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?

    Despite 0% schema coverage, the description provides a detailed Args section explaining each parameter (code, accelerator, timeout) with defaults and options, fully compensating for schema gaps.

    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 it executes Python code on a Google Colab GPU runtime, with specific verb and resource. It also lists the output format and distinguishes from file/notebook variants.

    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 for executing raw code on GPU, but does not explicitly contrast with siblings (colab_execute_file, colab_execute_notebook). Usage context is clear for experienced users.

    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?

    Beyond annotations (readOnlyHint=false, destructiveHint=false), the description discloses that the tool reads file contents, sends for execution on GPU, and specifies accelerator options and timeout. Adds value by providing execution context not in 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?

    Two concise paragraphs: clear action statement followed by well-structured argument list. Front-loaded with main purpose. No redundant information.

    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 presence of an output schema (relieving need to describe return values), the description is adequately complete for a code execution tool. However, it lacks mention of prerequisites like active Colab runtime or authentication, which could be inferred from context but not explicitly stated.

    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?

    With 0% schema description coverage, the description fully compensates by explaining each parameter: file_path as local .py path, accelerator with T4/L4 options and defaults, timeout with seconds and default. Provides clear meaning beyond schema titles and defaults.

    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?

    Clearly states it executes a local Python file on a Colab GPU, with specific verb+resource. Distinguishes from siblings (colab_execute, colab_execute_notebook) by emphasizing 'local .py file'.

    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?

    Implies usage for local .py files but offers no explicit guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites. Could be improved by directly contrasting with sibling tools.

    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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