Skip to main content
Glama
RohitCds

Local MCP Gemini Automation Engine

by RohitCds
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one reads documents and the other edits/overwrites them. There is no overlap or ambiguity in their intended actions.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern with a common 'mcp_' prefix: mcp_read_document and mcp_edit_document. Naming is uniform and predictable.

    Tool Count3/5

    With only 2 tools, the set is on the thin side for a server named 'Gemini Automation Engine.' While the tools are focused, the count feels minimal and may not justify a broader automation scope.

    Completeness2/5

    The domain appears to be document management, but the surface only covers reading and editing. Missing operations like create, delete, or list documents represent significant gaps that would hinder agents from performing basic lifecycle tasks.

  • Average 3.5/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 4 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?

    No annotations are provided, so the description carries the full burden. The verb 'reads' implies non-destructive behavior, but the description does not explicitly state that it is read-only, nor does it mention error handling, permissions, or output formatting. This leaves significant behavioral ambiguity.

    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, clear sentence with no filler words. It is front-loaded with the action and outcome, making it easy to scan. No unnecessary information is present.

    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?

    With no annotations and no output schema, the description should clarify what 'contents' means (e.g., plain text, JSON) and what happens for invalid doc_id. These gaps leave the agent uncertain about the return format and error conditions, making the description incomplete for a reliable invocation.

    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 only parameter, doc_id, is fully documented in the schema with a clear description ('The ID of the document to read'). The tool description does not add any extra meaning beyond referring to the document, which 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.

    Purpose5/5

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

    The description uses a specific verb ('reads') and resource ('document'), and states the outcome ('returns its contents'). This clearly distinguishes it from the sibling tool mcp_edit_document, which is for modifications.

    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 for retrieving document contents, but it does not explicitly state when to prefer this over mcp_edit_document or any exclusion criteria. No alternatives are mentioned, so the agent must infer from the sibling name.

    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?

    With no annotations, the description carries the burden of disclosure. It clearly states that the operation overwrites the total content, which is a key behavioral trait. However, it does not mention prerequisites (e.g., document must exist), reversibility, permissions, or error behavior.

    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 sentence that is front-loaded and to the point. It does not waste words, though 'overwrites or updates' is slightly redundant; 'overwrites' alone would be more direct. Overall, it is concise and readable.

    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?

    For a simple two-parameter mutation tool, the description conveys the core effect (full content replacement), but lacks information about return values, error conditions, and whether the document must already exist. Since there is no output schema, the description should have provided some of this context to be fully complete.

    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% for both parameters, so the schema already describes doc_id and content adequately. The description adds no additional parameter semantics beyond the schema; it repeats the notion of 'total content' which matches the content parameter.

    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 uses a specific verb ('overwrites or updates') and identifies the resource ('a document's total content'), making it clear this is a write operation. It distinguishes from the sibling tool mcp_read_document by focusing on modification rather than reading.

    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 (to modify a document's content) but does not explicitly state when to use this over mcp_read_document or mention any exclusions. The sibling tool name provides context, but the description itself offers no direct guidance on selection.

    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

local-mcp-gemini-cli MCP server

Copy to your README.md:

Score Badge

local-mcp-gemini-cli 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/RohitCds/local-mcp-gemini-cli'

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