MCP Google Workspace Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool targets a distinct action and resource: Gmail draft creation, Gmail sending, and Docs appending. The draft/send distinction is explicitly explained in both descriptions, so there is no real ambiguity.
Naming Consistency5/5All tool names follow a consistent snake_case service_verb_noun pattern: gmail_create_draft, gmail_send_email, google_docs_append_content. The naming makes the target service and action predictable.
Tool Count3/5Three tools is on the thin side for a server claiming to cover Google Workspace. The scope is limited to outbound Gmail actions and one Docs mutation, which feels under-scoped for the stated domain.
Completeness1/5The tool surface is severely incomplete for Google Workspace: there is no way to read or search Gmail, create or update Google Docs, or access Calendar, Drive, Sheets, or other core Workspace services. It only supports writing outbound email and appending to existing docs, leaving agents unable to perform basic lifecycle operations.
Average 4.3/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 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 ISC 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.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add
glama.jsonto the root of your repository:{ "$schema": "https://glama.ai/mcp/schemas/server.json", "maintainers": [ "your-github-username" ] }Then . Browse examples.
Add related servers to improve discoverability.
How to sync the server with GitHub?
Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
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?
With no annotations provided, the description carries the behavioral burden. It prominently states that existing content is never overwritten or deleted and that only plain text is appended, which are the key behavioral guarantees an agent needs. It does not cover failure modes or permissions, but the main non-destructive trait is well disclosed.
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 short and front-loaded with the core action. There is minor redundancy between 'without modifying existing content' and 'never overwritten or deleted,' but overall every sentence earns its place.
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?
For a simple two-parameter append operation with no output schema, the description covers the core semantics, the non-destructive behavior, and how to locate the required document ID. It could mention access requirements or behavior on nonexistent documents, but the essential information for correct use is present.
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 fully documents both parameters. The description adds a helpful hint about finding the document ID in the URL, but that information is also implied by the schema example, so it provides only marginal added value.
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: appending plain text to the end of an existing Google Document. It makes the scope obvious and is easily distinguishable from the unrelated gmail 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 Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly says when to use the tool ('add new content at the bottom of a Google Doc') and clarifies that existing content is preserved. It does not name alternatives, but the siblings are unrelated to Docs, so no exclusion is necessary.
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?
No annotations are present, so the description must carry behavioral disclosure. It clearly warns that the action is immediate, irreversible, and cannot be recalled, plus it emphasizes user confirmation. It could go further (e.g., failure handling, rate limits), but for a send action the key permanence trait is clearly disclosed.
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?
Four concise sentences, each with a distinct purpose (action, precondition, warning, alternative). No fluff.
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?
For a side-effecting mutation with no annotations, the description covers the critical context: user confirmation, permanence, and relationship to gmail_create_draft. It doesn't describe return values or error behavior, but those are less critical for a send action and the schema covers parameters.
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 coverage is 100%, all parameters have descriptions. The description does not add additional parameter semantics but relies on the schema, which is acceptable.
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 uses a specific verb ('Sends') with a clear resource ('email via the authenticated Gmail account'). It also distinguishes itself from the sibling tool gmail_create_draft by explicitly directing agents to that alternative for drafts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance: use only after the user has explicitly confirmed sending, and use gmail_create_draft for draft review instead. This clearly states when and when not to use the tool.
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?
No annotations are provided, so the description carries the full burden. It discloses the critical behavioral trait: the draft is only saved to Gmail Drafts and is not sent. It also scopes the operation to the authenticated user's Gmail account. It doesn't discuss return values or failure modes, but for a simple draft-creation tool this is a minor gap.
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?
Four short sentences with no wasted words. The first sentence states the core action, the second gives usage context, the third clarifies what it does not do, and the fourth routes to the correct sibling tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 6-parameter tool with a fully described schema and no output schema, the description covers everything needed for correct invocation: what it does, when to use it, that it does not send, and which sibling to use instead. No essential context is missing.
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%, with every parameter documented, including the html_body override behavior. The tool description itself adds no additional parameter-level meaning, but the schema already handles this fully, so the baseline of 3 applies.
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 opens with a specific verb-resource pair: 'Creates an email draft in the authenticated user's Gmail account.' It further distinguishes itself from gmail_send_email by explicitly stating that it does NOT send the email and only saves the draft to Gmail Drafts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit usage context: 'Use this tool when you want to prepare an email for review before sending.' It also states a clear exclusion ('This tool does NOT send the email') and names the correct alternative: 'To send an email, use the gmail_send_email tool instead.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
Card Badge
Copy to your README.md:
Score Badge
Copy to your README.md: