Skip to main content
Glama
baryhuang
by baryhuang

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: retrieving email body chunks, listing recent emails, refreshing tokens, and sending emails. There is no overlap in functionality that would cause confusion or misselection.

    Naming Consistency5/5

    All tools follow a consistent 'gmail_verb_noun' pattern with snake_case, making them predictable and easy to understand. The naming convention is uniform across all four tools.

    Tool Count4/5

    With 4 tools, the count is reasonable for a Gmail server, though it feels slightly thin for covering all common email operations. It includes core functions but could benefit from additional tools like searching or managing drafts.

    Completeness3/5

    The tools cover basic email operations (read, list, send) and authentication, but there are notable gaps such as searching emails, managing labels, or handling attachments. This could limit agents in performing more complex email tasks.

  • Average 3/5 across 4 of 4 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • 0 of 1 community issues answered or closed 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 MIT 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.json to 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

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries full burden for behavioral disclosure. It mentions what data is returned (metadata, snippets, first 1k chars of body) which is helpful, but doesn't cover important behavioral aspects like authentication requirements (beyond the parameter), rate limits, pagination behavior, error conditions, or whether this is a read-only operation. For a tool with no annotations, this leaves significant gaps.

    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, efficient sentence that communicates the core functionality and return format. It's appropriately sized for a straightforward retrieval tool, though it could potentially benefit from slightly more detail given the lack of annotations and output schema.

    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 complexity of email retrieval (3 parameters, no annotations, no output schema), the description is insufficiently complete. It doesn't explain the return format in detail, doesn't mention authentication requirements beyond the parameter, and doesn't cover important behavioral aspects. For a tool with no annotations or output schema, the description should provide more context about what to expect from the operation.

    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?

    Schema description coverage is 100%, so the schema already fully documents all three parameters. The description doesn't add any parameter-specific information beyond what's in the schema. The baseline of 3 is appropriate when the schema does all the parameter documentation work.

    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 tool's purpose: 'Get the most recent emails from Gmail' specifies the verb (get) and resource (emails). It distinguishes from sibling 'gmail_get_email_body_chunk' by indicating it returns metadata, snippets, and partial body content, but doesn't explicitly differentiate from other siblings like 'gmail_send_email' or 'gmail_refresh_token' beyond the obvious functional difference.

    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?

    No explicit guidance on when to use this tool versus alternatives is provided. The description doesn't mention when to use this versus 'gmail_get_email_body_chunk' for full body retrieval, or when to use 'gmail_refresh_token' for token management. Usage context is implied by the tool name but not explicitly stated.

    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 provided, the description carries the full burden of behavioral disclosure. It states the action ('send an email') but lacks critical details: it doesn't mention authentication requirements (implied by the 'google_access_token' parameter but not explicitly stated), potential rate limits, error handling, or what happens upon success (e.g., whether it returns a confirmation). This leaves significant gaps for a 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 a single, efficient sentence with zero wasted words—'Send an email via Gmail' is front-loaded and directly conveys the core action. It's appropriately sized for the tool's complexity, making it easy to parse quickly.

    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 (a mutation tool with 5 parameters, no annotations, and no output schema), the description is incomplete. It fails to address key contextual aspects like authentication needs, behavioral traits (e.g., what 'send' entails operationally), or output expectations, leaving the agent with insufficient information for reliable 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 schema description coverage is 100%, with all parameters well-documented in the schema (e.g., 'to' as recipient email, 'body' as plain text content). The description adds no additional meaning beyond the schema, such as explaining parameter interactions (e.g., 'body' vs. 'html_body') or constraints. This meets the baseline for high schema coverage.

    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 'Send an email via Gmail' clearly states the verb ('send') and resource ('email via Gmail'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'gmail_get_recent_emails' or 'gmail_refresh_token' beyond the obvious action distinction, which keeps it from a perfect score.

    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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing authentication via 'google_access_token'), nor does it clarify scenarios where other tools like 'gmail_get_recent_emails' might be more appropriate, leaving usage context entirely implicit.

    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 provided, the description carries full burden for behavioral disclosure. It mentions the 1k character chunking behavior, which is valuable, but doesn't address authentication needs (though implied by google_access_token parameter), error handling, rate limits, or what happens with invalid offsets/message_ids. For a tool with no annotation coverage, this leaves significant gaps.

    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 a single, efficient sentence that immediately conveys the core functionality without any wasted words. It's appropriately sized and front-loaded with the essential information.

    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 (4 parameters, no output schema, no annotations), the description is minimally adequate. It explains the chunking behavior but lacks details about authentication requirements, error conditions, and how this tool relates to siblings. Without annotations or output schema, more behavioral context would be helpful for safe usage.

    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?

    Schema description coverage is 100%, so parameters are well-documented in the schema. The description adds context about the 'offset' parameter (default: 0) and clarifies that thread_id retrieves the first message if multiple exist, providing some value beyond the schema. However, it doesn't explain parameter interactions or provide additional semantic context.

    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 ('Get'), resource ('email body chunk'), and key constraint ('1k character chunk starting from specified offset'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like gmail_get_recent_emails, which retrieves multiple emails rather than a specific body chunk.

    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?

    No guidance is provided about when to use this tool versus alternatives. The description doesn't mention prerequisites (like needing a message_id or thread_id), nor does it explain when this tool is appropriate compared to gmail_get_recent_emails for retrieving email content.

    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 provided, the description carries full burden but only states what the tool does at a high level. It doesn't disclose behavioral traits like whether this invalidates previous tokens, rate limits, error conditions, or what the refreshed token enables. For a security-sensitive operation with zero annotation coverage, this is inadequate.

    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 a single, efficient sentence that directly states the tool's purpose with zero waste. It's appropriately sized and front-loaded, with every word contributing to understanding the core functionality.

    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?

    For a security-critical token refresh operation with no annotations and no output schema, the description is insufficient. It doesn't explain what happens after refresh (e.g., token lifetime, scope preservation), error handling, or integration with sibling tools. Given the complexity and lack of structured data, more context is needed.

    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?

    Schema description coverage is 100%, so the schema already documents all 4 parameters thoroughly. The description adds no parameter-specific semantics beyond what's in the schema (e.g., it doesn't explain relationships between parameters or provide usage examples). Baseline 3 is appropriate when schema does the heavy lifting.

    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 verb ('Refresh') and resource ('access token'), specifying it uses refresh token and client credentials. It distinguishes from sibling tools (email-related operations) by focusing on authentication token management, though it doesn't explicitly name alternatives for token refresh.

    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?

    The description implies usage when access tokens expire (via 'optional if expired' in schema), but doesn't explicitly state when to use this tool versus alternatives like initial authentication or other token management methods. No guidance on prerequisites or exclusions is provided.

    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

mcp-headless-gmail MCP server

Copy to your README.md:

Score Badge

mcp-headless-gmail MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/baryhuang/mcp-headless-gmail'

If you have feedback or need assistance with the MCP directory API, please join our Discord server