colab-mcp (enhanced fork)

Server Quality Checklist

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Latest release: v1.1.0
Disambiguation5/5
Each tool has a clear and distinct purpose, targeting specific actions like adding, deleting, moving, or running cells, with no overlapping functionality. The consistent requirement for an active browser connection via open_colab_browser_connection further clarifies dependencies.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case (e.g., add_code_cell, get_cells, run_code_cell), making it easy for an agent to infer functionality from the name.
Tool Count5/5
With 9 tools, the server is well-scoped for its purpose of managing Google Colab notebooks. It covers essential operations without being overwhelming or too minimal.
Completeness4/5
The tool set covers core notebook interactions: connection, cell CRUD, execution, and runtime changes. Minor gaps exist, such as missing functions to clear output or restart the kernel, but the surface is sufficient for most workflows.
Average 3.7/5 across 9 of 9 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 12 commits in the last 12 weeks
- Last stable release on June 19, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under Apache 2.0.
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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{
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}
```
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden for behavioral disclosure. It only states the action and prerequisite, but does not disclose that the tool modifies the notebook state, nor does it mention any potential side effects or safety concerns.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is only one sentence, which is concise but under-specified. It omits critical details that would be valuable for correct invocation, such as parameter roles or positional insertion behavior.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 parameters with no descriptions and no annotation coverage, the description is far from complete. The existence of an output schema does not compensate for missing semantic guidance on inputs and usage context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% for the 3 parameters, and the description adds no information about what 'code', 'cellIndex', or 'language' mean, how they are used, or expected formats. The description provides no parameter-level guidance.
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 description clearly states the action of adding a code cell, distinguishing it from adding a text cell by specifying 'code cell.' However, it does not mention the ability to insert at a specific index, which is implied by the cellIndex parameter.
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?
The description mentions a necessary prerequisite (active browser connection via open_colab_browser_connection) but provides no guidance on when to use this tool versus siblings like add_text_cell, change_runtime, or delete_cell.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description carries full burden but only adds a prerequisite. Fails to disclose if addition is immediate, idempotent, or any constraints on content.
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?
Two concise sentences, front-loaded with the main action. Could benefit from including parameter details without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a simple tool with an output schema, omitting parameter descriptions and behavioral details makes it incomplete for reliable invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description provides no explanation for 'content' or 'cellIndex' parameters, leaving their purpose and constraints unclear.
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 'Add a new text/markdown cell', specifying verb and resource. Distinguishes from sibling 'add_code_cell' which adds code instead of text.
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 requires an active browser connection via 'open_colab_browser_connection', providing essential usage context. However, no exclusions or comparisons to alternatives like 'update_cell'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It indicates mutation (update) but does not disclose permissions, reversibility, or whether content replaces or appends. More detail on behavioral traits is needed.
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: one for purpose, one for prerequisite. No wasted words. Front-loaded with the core action.
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?
With output schema present, description need not explain return values. However, parameter details are missing, and for a simple update tool with 2 params, more context on content format or cell identification would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, meaning no parameter descriptions in the schema. The description does not explain cellId or content, their format, or expected values. It adds no 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 verb 'Update' and the resource 'contents of an existing cell in the Colab notebook'. This distinguishes it from sibling tools like add_code_cell, delete_cell, etc.
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?
It explicitly states a prerequisite: 'Requires an active browser connection via open_colab_browser_connection'. This gives clear context for when to use, though it does not exclude alternatives or mention when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully convey behavioral traits. It only mentions the prerequisite and the move action, but fails to disclose potential side effects (e.g., whether it is destructive, reversible, or what happens if the cellId is invalid). The description is insufficient for safe invocation.
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 a single concise sentence that front-loads the core action and prerequisite. It avoids unnecessary words, though it could benefit from slightly more structure to separate purpose from requirement.
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?
Given the tool's moderate complexity (a move operation with a prerequisite), the description covers the essential purpose and a key dependency. However, it lacks details on error handling, behavior with invalid inputs, and coordination with sibling tools like update_cell. An output schema exists but is not shown; the description does not reference it.
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?
With 0% schema description coverage, the description should compensate by explaining parameter meaning. It mentions 'cellId' and 'target index' but does not clarify the index's base (0-based or 1-based), valid ranges, or relationships between parameters. This leaves ambiguity for correct usage.
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 action ('Move a cell'), the resource ('Colab notebook'), and the key parameters ('by cellId and target index'). It effectively distinguishes from sibling tools like add_code_cell or delete_cell by focusing on repositioning.
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 states a critical prerequisite: 'Requires an active browser connection via open_colab_browser_connection.' This guides the agent on necessary prior steps. However, it does not specify when not to use this tool or mention alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden for behavioral disclosure. It only states the basic action and prerequisite, but does not explain what happens on execution (e.g., output handling, error behavior, side effects, or rate limits). This is insufficient for a tool that executes arbitrary code.
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 extremely concise: two short sentences, no redundant information, and front-loads the action. Every word serves a purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (executing code, with a prerequisite and output schema), the description is minimal. It lacks details on return values, error handling, and what 'execute' entails. The existence of an output schema is not leveraged to explain the response. This leaves the agent underinformed for safe and effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning to the 'cellId' parameter by specifying it comes from 'add_code_cell or get_cells', which is helpful given the 0% schema description coverage. However, it does not explain format, constraints, or the fact that the schema marks it as not required (with default empty string), potentially causing confusion. More detail would be beneficial.
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 action ('Execute a code cell') and identifies the resource ('Colab notebook') and required parameter ('cellId'). It distinguishes from sibling tools by focusing on execution rather than addition, deletion, or modification.
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 specifies a prerequisite ('Requires an active browser connection via open_colab_browser_connection') and indicates the source of cellId ('from add_code_cell or get_cells'), giving context on when to use this tool. However, it does not provide explicit when-not-to-use guidance or compare to alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Only mentions opening connection and return value. Lacks details on authentication, idempotency, side effects, or failure behavior.
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, no verbose language. Front-loaded purpose and return value. Every sentence adds value.
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 it's a simple connection tool with output schema, description adequately covers purpose and outcome. Could mention prerequisites or state implications of failure, but acceptable.
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; baseline score of 4 applies. Description adds no param info, but none needed.
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 verb 'opens a connection', resource 'Google Colab browser session', and outcome 'unlocks notebook editing tools'. Distinguishes from sibling tools which are about cell manipulation and runtime.
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?
Implied use as a prerequisite for editing tools, but no explicit 'when to use' or alternatives provided. Context suggests it's a setup step, but not definitive.
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?
States destructive action (delete) and prerequisite. No annotations exist, so description carries burden. Could mention error handling or irreversibility, but sufficient for a simple deletion.
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, no fluff. Action stated first, prerequisite second. Efficient and well-structured.
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?
Has output schema so return values not needed. But lacks details on success/error behavior, resource impact, or edge cases (e.g., invalid cellId). Adequate but not comprehensive.
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 coverage 0%; description only restates parameter name 'by cellId' without explaining its format, origin, or that it's likely required despite optional schema. Adds minimal value.
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 ('Delete'), resource ('cell from Colab notebook'), and parameter ('by cellId'). Distinguishes from sibling tools like add_cell or move_cell.
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?
Explicit prerequisite: 'Requires an active browser connection via open_colab_browser_connection.' Provides clear context for when to use, though no explicit mention of when not to or alternatives.
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, description covers that it is a read operation and lists returned data. It mentions the prerequisite connection but could add details like no side effects or limitations. Adequate given simplicity.
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 sentences, front-loaded with purpose, no waste.
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 zero parameters and existence of output schema, description covers return type and prerequisite. Lacks explanation of 'current notebook state' but sufficient for agent.
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; baseline score of 4 applies. Description does not need to add parameter info.
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 (Read) and resource (current notebook state: cells) and specifies what is returned (IDs, contents, outputs). It distinguishes from sibling write tools.
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?
Provides explicit usage context ('Essential for iterative work') and a prerequisite ('Requires an active browser connection via open_colab_browser_connection'). Could mention when not to use or alternative read tools if any, but still 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?
With no annotations provided, the description carries full burden. It discloses the behavioral trait of changing the accelerator and the OAuth requirement, which is adequate for a simple mutation tool.
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 two sentences, front-loaded with purpose and valid values, then the prerequisite. Every sentence earns its place with no 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 the low complexity (single parameter, no required params, output schema exists), the description is complete. It covers purpose, allowed values, and a key prerequisite.
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%, so the description must compensate. It adds meaning to the 'accelerator' parameter by listing valid values, fully covering what the schema lacks.
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 changes the Colab runtime to use a specific GPU accelerator, listing valid values (NONE, T4, L4, A100). It distinguishes itself from sibling tools, none of which are related to runtime or accelerator settings.
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 lists valid accelerator values and mentions the OAuth setup requirement for first use. While it does not compare to alternatives, the context is clear and provides necessary usage guidance.
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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