mcp-colab-gpu

Server Quality Checklist

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Latest release: v0.3.1
Disambiguation5/5
Each tool has a clearly distinct purpose: execution variants (colab_execute, colab_execute_file, colab_execute_notebook) differ in input and output handling, job management tools are separate, and Drive utilities are distinct. No overlap that would cause misselection.
Naming Consistency5/5
All tools follow the consistent 'colab_' prefix with snake_case verb_noun pattern (e.g., colab_execute, colab_poll, colab_drive_upload). No mixing of conventions.
Tool Count5/5
10 tools is well-scoped for a GPU execution server: three execution variants, job lifecycle management, Drive integration, and status/version check. Each tool earns its place without being excessive or sparse.
Completeness4/5
Covers core workflows: code execution (with file and artifact options), job management, Drive upload/download, and status. Minor gaps like lack of a tool to list Drive files or manage folders, but overall sufficient for common tasks.
Average 4.7/5 across 10 of 10 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- No commit activity data available
- 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.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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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?
Discloses OAuth authorization requirement and automatic folder creation. Annotations already indicate non-read-only and non-destructive nature. Missing details about overwrite behavior and file size limits.
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?
Well-structured with separate sections for usage, post-upload steps, common issues, and Args. Concise but not overly terse; each sentence adds useful 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?
Covers prerequisites (OAuth), integration with colab_execute, and common issues. Output schema exists but description doesn't mention return value; still fairly complete for the tool's purpose.
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?
Schema has no parameter descriptions (0% coverage). Description explains both parameters thoroughly: local_path is path to local file, drive_folder includes default value, nested folder support, and auto-creation. Adds significant value beyond the schema.
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?
Clear verb 'Upload', specific resource 'local file to Google Drive', and distinguishes from sibling tools by mentioning its role in staging data for colab_execute.
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?
Explicitly states when to use: before colab_execute with drive_fetch parameter. Provides usage example and mentions OAuth prerequisite, but lacks explicit when-not-to-use guidance.
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?
Annotations (readOnlyHint, destructiveHint) already cover safety. Description adds use context (debugging) without contradicting 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 sentences, front-loaded with verb, no wasted words. Perfectly concise.
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 no parameters, existing output schema, and annotations, description fully explains purpose and usage. Complete for a simple version tool.
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?
No parameters exist; schema coverage is 100%. Baseline for 0 parameters is 4, and description does not need to add parameter details.
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?
Description clearly states 'Return the mcp-colab-gpu server version' with a specific verb and resource. It distinguishes from sibling tools like colab_execute by being unique in purpose.
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?
Explicitly says to use for 'verifying server compatibility or debugging', which provides clear guidance. No alternatives needed given simplicity.
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?
Annotations declare destructiveHint=true and readOnlyHint=false, but the description adds value by explaining the underlying async task cancellation, automatic runtime release, and verification step. 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with sections for main action, post-cancellation steps, common issues, and args. Front-loaded with purpose. A bit lengthy but every sentence adds value.
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 an output schema exists (not shown), return values are not needed. The description covers purpose, usage, behavioral effects, common issues, and sibling references thoroughly for a cancellation tool.
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?
Only one parameter (job_id) with 0% schema description coverage. The description explains it as 'The job identifier returned by colab_execute,' which is clear and sufficient given the single required param.
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 'Cancel an active background job' and explains the action (marks as cancelled, cancels async task). It distinguishes from siblings by referencing colab_execute, colab_jobs, and colab_poll for different lifecycle stages.
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 states when to use (cancel active job), when not to (cannot cancel already completed/failed/cancelled), and recommends colab_jobs first if job_id is unknown. Provides post-cancellation steps (verify with colab_poll, then start new job with colab_execute).
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?
Annotations already indicate non-read-only and non-destructive. The description adds details about the download process, local file creation, and first-use OAuth, providing behavioral context beyond 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 concise, front-loaded with purpose, followed by usage guidelines, then common issues in bullet points. Every sentence adds value with no wasted words.
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 simplicity, an output schema exists, so return values don't need explanation. The description covers purpose, usage, parameters, and troubleshooting thoroughly, making it 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?
With 0% schema description coverage, the description fully compensates by documenting both parameters: drive_path relative to MyDrive and local_path as destination. This adds crucial meaning beyond the schema.
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 downloads a file from Google Drive to a local path, specifying the exact resource and action. It distinguishes from siblings like colab_drive_upload and colab_execute by focusing on retrieval.
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 says to use this for retrieving results from colab_execute or any Drive file, and lists common issues like file not found and OAuth. It doesn't explicitly mention when not to use or alternatives, but the context is clear.
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?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the description adds value by detailing the response statuses (starting/running/completed/failed/cancelled), cleanup after 5 minutes, and common issues, providing rich behavioral context beyond 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections for usage, response interpretation, common issues, and args. It is front-loaded with the purpose and avoids unnecessary words, though it is slightly lengthy.
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 existence of an output schema, the description adequately covers all necessary context: usage, response interpretation, common issues, and parameter semantics. It is complete for a polling tool.
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?
Schema coverage is 0%, but the description compensates by explaining job_id as 'The job identifier returned by colab_execute', adding meaning to the only 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 'Poll a background job for its current status and results' and distinguishes itself by explicitly directing usage after colab_execute(..., background=True), differentiating from sibling tools like colab_jobs and colab_cancel.
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 says when to use ('after launching a background execution with colab_execute(..., background=True)') and provides context for when not to use or alternatives (e.g., 'use colab_jobs to list all tracked jobs' for unknown job_id).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are minimal (readOnlyHint=false, destructiveHint=false), so the description carries full burden. It extensively discloses behaviors: hardware allocation, structured JSON return, background job limitations (only one at a time), error handling for OOM, import errors, rate limits, and CUDA errors. It also explains side effects of drive_fetch/drive_save and the need for fresh tokens.
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 with a clear first sentence, followed by execution context, common issues, and a parameter list. It is front-loaded with key information. However, it is somewhat lengthy; a slightly more concise breakdown could improve readability, but the level of detail is justified by the tool's complexity.
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 (7 parameters, background jobs, accelerator types, drive integration), the description covers all essential aspects: purpose, usage flow, error scenarios, parameter details, and return behavior. It also references sibling tools (colab_poll) and notes limitations, making it a complete guide for the agent.
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?
Schema description coverage is 0%, but the description compensates fully with detailed parameter explanations: accelerator types with GPU/TPU lists, examples for drive_fetch/drive_save, defaults, and constraints (e.g., background incompatible with drive operations). This goes well beyond what the bare schema provides.
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 Python code on a Google Colab GPU/TPU runtime,' providing a specific verb and resource. It distinguishes itself from siblings by positioning as the 'primary tool' for running GPU/TPU-accelerated code and mentions background execution with polling, which differentiates it from other execute tools like colab_execute_file or colab_execute_notebook.
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 gives clear context on when to use the tool (primary for GPU/TPU code) and includes after-execution steps (polling, error handling) and common issues. However, it lacks explicit guidance on when NOT to use this tool versus specific siblings, such as using colab_execute_file for file-based execution or colab_execute_notebook for notebooks.
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?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds context about the one-background-job limit and the need to check before executing, which is valuable beyond 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?
Three sentences: first states purpose, second gives usage context, third provides post-check actions. No wasted words; structure is front-loaded and efficient.
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?
Covers purpose, when to use, and what to do with the results. The output schema exists to describe return values, so the description is complete for this tool.
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?
No parameters exist, so baseline is 4. The description does not need to add parameter information.
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 returns current server status with accelerator info and job state. It distinguishes itself from sibling tools like colab_execute and colab_cancel by being a read-only status check.
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 advises to use before executing code and provides follow-up actions: choose accelerator for colab_execute, wait or cancel if a job is running. This is clear guidance.
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?
Discloses security restriction (only .py files), file existence requirement, and response structure (exit_code, cell outputs). Could add more on runtime side effects, but annotations are minimal so description adds significant value.
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?
Well-structured with clear sections, front-loaded purpose, and no redundant information. Every sentence adds value.
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?
Covers purpose, usage, parameters, execution flow, and post-execution steps. Output schema exists, so return details are not needed. Complete for a 4-param tool with sibling context.
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?
Schema has 0% coverage, but description provides full parameter details including accelerator options with availability tiers, high_memory, timeout, and required file_path. Compensates completely.
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 the tool executes a local Python file on Colab with specific hardware. Differentiates from sibling tools colab_execute and colab_execute_notebook.
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?
Provides explicit when-to-use (for .py files) and when-to-use alternatives (colab_execute_notebook for output files). Includes common issues and post-execution instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description details what the tool does after execution: scans runtime for artifacts, zips them, and downloads to local directory. It discloses file size limits (50 MB per artifact) and expected artifact locations. Annotations provide no contradictory info, and description adds significant behavioral context beyond them.
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 with clear sections (after execution, common issues, args) and is front-loaded with purpose. It is slightly lengthy but every sentence serves a purpose. Could be marginally tightened.
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 (5 parameters, artifact handling, output schema), the description covers all necessary aspects: what the tool does, how to use it, parameter details, expected outputs, common issues, and even limitations. The existence of an output schema is acknowledged by mentioning response fields.
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 description coverage, the description extensively documents each parameter: code, output_dir, accelerator (with full list of GPU/TPU types and their VRAM/plan), high_memory, and timeout. This adds crucial meaning beyond the bare schema titles.
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 executes Python code on Colab GPU/TPU and collects generated artifacts. It distinguishes itself from siblings by focusing on artifact retrieval, and includes a usage directive: 'Use this when your code produces files you need to retrieve locally.'
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?
The description explicitly tells when to use this tool (when producing files) and when not (for files >50MB, use colab_execute with drive_save). It also provides post-execution steps and common issues, offering clear guidance on alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark readOnlyHint true and destructiveHint false, so the tool is safe. The description adds value by detailing the return format (JSON array with job_id, status, accelerator type, timestamps) and cleanup policy (5 minutes auto-clean). No contradictions.
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 concise with 4 sentences, front-loaded with the main action, and each sentence adds essential information without waste.
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 no parameters, an output schema (implied), and sibling tools, the description fully covers what the tool does, what it returns, how to use its results, and cleanup behavior. It is complete for agent selection and invocation.
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 tool has zero parameters, so the baseline is 4. The description correctly omits parameter information as none exist.
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 lists all tracked background jobs, which is a specific verb+resource. It distinguishes from siblings like colab_poll and colab_cancel by noting this tool provides job IDs for them.
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?
The description explicitly tells when to use this tool (to find job IDs) and provides clear next steps: use colab_poll for details, colab_cancel to stop, and notes auto-cleanup. This guides the agent on context and alternatives.
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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