Skip to main content
Glama
xumingjun5208

Gemini MCP Server

by xumingjun5208
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

67%
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 with no overlap: gemini_chat handles chat interactions with the Gemini API, while gemini_list_models provides metadata about available models. An agent would never confuse these tools as they serve fundamentally different functions in the workflow.

    Naming Consistency5/5

    Both tools follow a consistent 'gemini_' prefix + descriptive_snake_case pattern (gemini_chat and gemini_list_models). This creates a predictable naming convention that clearly associates both tools with the Gemini service while maintaining readability and consistency throughout the toolset.

    Tool Count2/5

    With only 2 tools, this server feels severely under-equipped for a comprehensive Gemini API integration. While chat functionality is essential, the absence of tools for embeddings, file analysis, or other Gemini capabilities creates a thin surface that will limit agent effectiveness. The count is too low for the apparent scope of a full Gemini MCP server.

    Completeness2/5

    The tool surface is significantly incomplete for a Gemini integration server. While chat functionality is well-implemented, there are major gaps: no tools for embeddings generation, file content analysis beyond chat context, model information beyond listing, or other Gemini API endpoints. This creates dead ends for agents trying to perform common Gemini workflows beyond basic chat interactions.

  • Average 4/5 across 2 of 2 tools scored. Lowest: 3.3/5.

    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 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

  • Behavior3/5

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

    Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds no behavioral context beyond what annotations provide, such as rate limits, authentication needs, or what 'available' means in practice. No contradiction with annotations exists.

    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 without unnecessary words. It's appropriately sized for a simple listing operation and front-loaded with 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 low complexity, rich annotations, and presence of an output schema, the description is minimally adequate. However, it lacks details on parameter usage and doesn't leverage the output schema to hint at return values, leaving gaps in completeness for effective agent 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?

    Schema description coverage is 0%, but the description mentions no parameters at all. The input schema includes 'response_format' and 'filter_text', but the description doesn't explain their purpose or usage. Since there are parameters, the baseline is 3, but the description fails to compensate for the low 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 clearly states the verb ('List') and resource ('available Gemini models'), making the purpose immediately understandable. It distinguishes from the sibling tool 'gemini_chat' by focusing on listing rather than conversational interaction. However, it doesn't specify what information about models is returned, keeping 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 the sibling tool 'gemini_chat' or any other potential alternatives, nor does it specify prerequisites or contextual triggers for listing models versus other operations.

    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?

    The description adds valuable behavioral context beyond what annotations provide. While annotations indicate read-only and non-destructive operations, the description explains multi-turn chat capabilities with session_id, file handling with glob patterns, and model auto-selection behavior. No contradictions with annotations exist.

    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 well-structured with clear sections (Args, Returns, Examples), uses bullet points for readability, and every sentence adds value. It's appropriately sized for a complex tool with many parameters and provides essential information without unnecessary elaboration.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's complexity (8 parameters, multi-turn chat, file handling) and the presence of an output schema, the description provides complete context. It explains parameter usage, behavioral characteristics, and includes practical examples, making it fully adequate for an AI agent to understand and use the tool correctly.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Despite 0% schema description coverage, the description comprehensively documents all 8 parameters with clear explanations of their purpose, constraints, and usage patterns. It adds significant value beyond the bare schema by explaining what each parameter does and how to use them effectively.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the specific action ('Send a message to Google Gemini and get a response') with the exact resource (Google Gemini). It distinguishes from the sibling tool gemini_list_models by focusing on chat completion rather than model listing.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides clear context for when to use this tool (for sending messages to Gemini) and includes examples that illustrate different usage scenarios. However, it doesn't explicitly state when NOT to use it or mention the sibling tool as an alternative for different purposes.

    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

aistudio-gemini-mcp MCP server

Copy to your README.md:

Score Badge

aistudio-gemini-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/xumingjun5208/aistudio-gemini-mcp'

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