Google Workspace Code MCP
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool has a single, clearly defined purpose: executing JavaScript/TypeScript code in a Google Workspace context.
Naming Consistency5/5A single tool inherently has perfect naming consistency. The tool name 'execute' follows a clear verb-based pattern, and there are no other tools to create inconsistency.
Tool Count2/5One tool is too few for a server with the broad scope implied by 'Google Workspace Code MCP'. This suggests a single-purpose utility rather than a comprehensive interface to Google Workspace, which typically involves multiple resources and operations.
Completeness1/5The tool set is severely incomplete for the stated purpose. A Google Workspace integration would typically require tools for managing users, groups, documents, calendars, etc., but this server only provides a generic code execution tool with no specific Workspace operations.
Average 3.4/5 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
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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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the execution environment (Node vm), authentication context, TypeScript handling, and available variables, which is valuable. However, it lacks critical behavioral details like security implications, error handling, resource limits, or what happens with the 'state' object persistence.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences that each add value: the first establishes the execution context and capabilities, the second explains return value mechanics. There's minimal redundancy, though it could be slightly more front-loaded about the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool that executes arbitrary code with authentication and state management, the description provides adequate technical context about the execution environment but lacks important completeness elements. With no output schema and no annotations, it should explain more about return values, error conditions, security boundaries, and the persistence model for the 'state' object.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal parameter semantics beyond the schema - it mentions 'auth, google, workspace, state' for the script parameter, which slightly reinforces the schema. This meets the baseline expectation when schema coverage is complete.
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 specific action ('Execute JavaScript/TypeScript'), the environment ('inside a Node vm context with authenticated Google Workspace access'), and the available resources ('auth, google, workspace, state'). It distinguishes this as a code execution tool with specific runtime characteristics, which is unambiguous even without sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives or what scenarios it's designed for. It explains technical capabilities but offers no context about appropriate use cases, prerequisites, or limitations beyond the execution mechanics.
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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