Skip to main content
Glama
philschmid

Gemini Docs MCP Server

by philschmid
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

  • Disambiguation4/5

    Tools are mostly distinct: get_capability_page retrieves any page by title (and can list all titles), get_current_model is a shortcut for the models page (overlapping with get_capability_page), and search_documentation does keyword search. The overlap between get_current_model and get_capability_page causes minor ambiguity.

    Naming Consistency4/5

    All tools use snake_case and follow a verb_noun pattern (get_ or search_). The nouns are descriptive but not uniformly structured (capability_page vs current_model vs documentation). Naming is mostly consistent with minor variation.

    Tool Count3/5

    Three tools is on the low side for a documentation server, but the tools are versatile: get_capability_page doubles as a list tool, and get_current_model is a convenience. The count feels thin but not severely inadequate.

    Completeness4/5

    The set covers key actions: listing pages, retrieving a specific page, searching. Minor gaps like missing page hierarchy or section-level navigation exist, but the ability to list all pages via get_capability_page without arguments mitigates the need for an explicit list tool.

  • Average 4.2/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 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?

    No annotations provided, so description carries burden. It discloses the calling pattern and that omitting the argument returns a list, but does not mention side effects, authentication, or data volume. Adequate but could be more detailed on what the output contains beyond the schema.

    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?

    Three sentences, each adding value: purpose, usage pattern, and further instruction. No fluff, well-structured, and front-loaded with the core functionality.

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

    Completeness4/5

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

    For a simple tool with one parameter and an output schema, the description covers the main use case and provides a clear workflow. Output format is not described but is covered by the output schema. Leaves little ambiguity.

    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 coverage is 100% with a detailed description. The description reiterates the schema's guidance on omitting the argument if title unknown, adding no new information beyond the schema. With high schema coverage, baseline 3 is appropriate.

    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?

    Clear verb 'Retrieves' and specific resource 'documentation page by exact title'. Distinguishes from sibling tools like search_documentation by focusing on exact title retrieval. The two-step usage pattern further clarifies purpose.

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

    Usage Guidelines5/5

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

    Explicitly states when to call without arguments (to get a list) and when to call with the exact title. Provides a clear usage workflow that guides the agent on optimal invocation order.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior3/5

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

    No annotations are provided. The description explains that the tool retrieves a documentation page, which implies read-only behavior. It adds context by calling it a 'shortcut tool' but does not disclose any additional behavioral traits like caching or network requirements.

    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 two sentences: the first states the purpose, the second gives usage advice. Every sentence is necessary and front-loaded. No wasted words.

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

    Completeness4/5

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

    Given the tool has no parameters, an output schema is present (context signal), and siblings provide contrast, the description is fairly complete. It covers the tool's purpose and what to expect. Minor omission: could mention if output is HTML or text, but the output schema likely covers that.

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

    Parameters4/5

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

    There are no parameters, and schema coverage is 100% (no params). The description adds meaning beyond the schema by explaining what the retrieved documentation page contains (model variants, capabilities, versioning).

    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 it retrieves the canonical 'Gemini Models' documentation page. It uses specific verbs and resource, and distinguishes from siblings like 'search_documentation' by calling itself a 'shortcut tool'.

    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 says 'Use this to fast-track finding details about available model variants...' which gives clear context for when to use. However, it does not explicitly state when not to use or mention alternatives beyond the implicit sibling tools.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior3/5

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

    No annotations are provided, so the description must fully disclose behavior. It reveals that the search is naive keyword-based, that long queries fail, and that it returns full documentation pages. However, it does not mention response format, pagination, or what happens if no results are found, which are gaps for a retrieval tool.

    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 front-loaded with the purpose and follows with critical usage warnings in capitals. It could be slightly more concise, but every sentence adds value and the structure aids readability.

    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 presence of an output schema, the description does not need to detail return values. It covers purpose, usage guidelines, and key behavioral traits. The tool is simple (1 parameter), and the description is sufficient for an agent to use it 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?

    The input schema already provides a detailed description for the 'queries' parameter, but the description adds critical context: limit to 1-3 keywords, focus on unique terms, break complex questions. This goes well beyond the schema, teaching effective usage.

    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 tool performs a standard keyword search on Gemini API documentation, distinguishing it from siblings like get_capability_page (returns a specific page) and get_current_model (model info). The verb 'search' and resource 'documentation' are specific, and the mention of 'keyword search' differentiates it from semantic search.

    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 explicit guidance: use short keyword queries (1-3 words), avoid long queries, break complex questions into separate simple queries. It does not explicitly state when not to use this tool versus alternatives, but the context implies it's for keyword-based documentation lookup, not for retrieving specific pages or model details.

    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-api-docs-mcp MCP server

Copy to your README.md:

Score Badge

gemini-api-docs-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/philschmid/gemini-api-docs-mcp'

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