colab-mcp
Server Quality Checklist
Latest release: v1.0.1
- Disambiguation5/5
Only one tool exists, so there is no possibility of confusion between tools. The purpose is clearly stated as opening a Colab browser connection.
Naming Consistency5/5The single tool name follows a clear snake_case pattern with a verb and object (open_colab_browser_connection). There is no inconsistency since only one name exists.
Tool Count2/5A single tool is too few for a Colab integration server. The description references 'unlocking notebook editing tools' but none are actually provided, making the tool count inappropriate for the apparent scope.
Completeness1/5The server only offers a connection opener, with no actual editing or interaction tools. The functionality implied by 'unlocking notebook editing tools' is completely absent, leaving the surface severely incomplete.
Average 4/5 across 1 of 1 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 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.
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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?
There are no annotations, so the description must fully disclose behavioral traits. It discloses the return type (boolean) but does not explain side effects, prerequisites, or failure behavior. 'Opens a connection' implies a state change but doesn't specify whether it's idempotent, whether it requires authentication, or what happens on failure. This is a significant gap for a tool that likely mutates browser state.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the primary action and followed by the return type. No unnecessary words, and it achieves clarity without fluff. It is an excellent example of concise, well-structured documentation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (zero parameters, no siblings, output schema present), the description covers the essential information: what it does and what it returns. It lacks additional behavioral context that would enhance completeness, but for a tool this straightforward, it is largely adequate. The missing failure semantics and side-effect details prevent a perfect score.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the description does not need to add parameter details. The schema is empty, and the baseline for 0 params is 4. The description correctly omits any parameter references, consistent with the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action (opens a connection) and its resource (Google Colab browser session), and mentions the effect (unlocks notebook editing tools). It also specifies the return type, making the purpose unambiguous. With no sibling tools to differentiate from, this is a fully specified purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on what the tool does and its purpose (connecting to a Colab browser session), implying when it would be used. However, it does not explicitly state when not to use it or any alternatives, but since there are no sibling tools, this is not a significant gap. The phrasing 'unlocks notebook editing tools' gives a sense of the intended use case.
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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