Skip to main content
Glama
falahgs

Gemini Email Subject Generator MCP

by falahgs
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one generates thinking process text, while the other sends emails with AI-generated subjects. There is no overlap or ambiguity between these functions, making it impossible for an agent to confuse them.

    Naming Consistency4/5

    Both tools use a verb-object naming pattern (generate-thinking, send-email), which is consistent and readable. The minor deviation is the hyphenation style, but this is uniform across both tools, so it does not cause confusion.

    Tool Count2/5

    With only 2 tools, the server feels thin for its stated purpose of email subject generation. It lacks essential operations like retrieving email history, managing templates, or handling errors, which limits its utility and scope.

    Completeness2/5

    The server is severely incomplete for email subject generation. It provides no way to list, update, or delete generated content, and lacks supporting tools for email management (e.g., checking sent emails, setting recipients). This creates significant gaps that will hinder agent workflows.

  • Average 2.9/5 across 2 of 2 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

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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 the specific model being used but doesn't describe important behavioral aspects like rate limits, authentication requirements, response format, error conditions, or whether this is a read-only or mutating operation. The description is minimal and lacks crucial operational context.

    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 extremely concise - a single sentence that gets straight to the point without unnecessary words. However, this brevity comes at the cost of completeness. While structurally efficient, it may be too minimal for a tool that likely has important behavioral characteristics that should be disclosed.

    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 text generation tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns, how the 'thinking process' output is structured, what limitations exist, or what happens when outputDir is specified. The agent would need to guess about important operational aspects of this tool.

    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 both parameters. The description adds no additional parameter information beyond what's in the schema - it doesn't explain what constitutes appropriate 'thinking process' prompts, provide examples, or clarify the output directory usage. Baseline 3 is appropriate when 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 action ('generate') and resource ('detailed thinking process text') with specific model information ('using Gemini Flash 2 model'). It distinguishes from the sibling 'send-email' tool by focusing on text generation rather than communication. However, it doesn't fully differentiate what makes this 'thinking process' generation unique versus other text generation tools that might exist.

    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. There's no mention of appropriate use cases, prerequisites, or comparisons to other text generation methods. The sibling tool 'send-email' is completely unrelated, so no comparative guidance is offered.

    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 mentions AI subject generation but lacks critical details: whether this is a read-only or mutating operation (implied mutation from 'send'), authentication requirements, rate limits, error handling, or what happens upon success/failure. This is inadequate for a tool that likely performs external communication.

    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. It front-loads the core purpose and key feature without unnecessary elaboration, 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?

    For a tool with 5 parameters, no annotations, and no output schema, the description is insufficient. It lacks behavioral context (e.g., side effects, permissions), doesn't explain the relationship with the sibling tool, and provides minimal guidance on usage. The AI subject generation is noted but not elaborated, leaving gaps in understanding the tool's full scope.

    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 fully documents all parameters. The description adds minimal value beyond the schema—it implies 'subjectPrompt' is used for AI generation but doesn't explain how this interacts with other parameters or provide usage examples. Baseline 3 is appropriate as the 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 action ('Send an email') and specifies a key feature ('with AI-generated subject using Gemini Flash 2'), which distinguishes it from generic email tools. However, it doesn't explicitly differentiate from the sibling tool 'generate-thinking', which might be related but has an unclear relationship.

    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, prerequisites, or constraints. It mentions AI-generated subjects but doesn't specify scenarios where this is beneficial or when manual subjects might be preferred. No exclusions or sibling tool comparisons are included.

    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

Gemini-Email-Subject-Generator-MCP MCP server

Copy to your README.md:

Score Badge

Gemini-Email-Subject-Generator-MCP 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/falahgs/Gemini-Email-Subject-Generator-MCP'

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