Skip to main content
Glama
ohaiibuzzle

Copilot MCP Server

by ohaiibuzzle
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation2/5

    copilot_prompt and copilot_command overlap significantly since copilot_command can also run prompts, making it unclear which tool to use for a prompt task. The descriptions provide some clarity but do not fully resolve the boundary.

    Naming Consistency5/5

    Both tool names follow a consistent 'copilot_<mode>' pattern with snake_case, which is predictable and easy to remember. The naming is uniform and well-aligned with the server's purpose.

    Tool Count3/5

    With only 2 tools, the server is minimal but matches the narrow scope of wrapping the Copilot CLI. However, the presence of a general command makes the prompt-specific tool feel somewhat redundant, and the count is at the low end of reasonable.

    Completeness3/5

    The general copilot_command covers all CLI operations, avoiding dead ends, but the server lacks specialized tools for common Copilot workflows such as auth, session management, or structured prompt handling. The surface is functional yet sparse.

  • Average 3.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • No commit activity data available
    • 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 GPL 3.0.

  • 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 the full burden of behavioral disclosure. It only says it runs a command and how to format args, but does not mention potential side effects, output behavior, execution context, or safety implications of executing arbitrary Copilot commands.

    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 with no redundant information. It leads with the purpose ('Run a general Copilot command') and then provides the essential usage detail about args.

    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?

    This tool executes arbitrary Copilot commands, yet the description is minimal. It does not explain how output is returned, how errors or timeouts are handled, or how `cwd` affects execution. The output schema exists but the description lacks enough detail to fully understand the tool's behavior in context.

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

    Parameters2/5

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

    The description adds meaningful semantics for the `args` parameter by explaining its format, but it does not describe `cwd` or `timeout_seconds`. With 0% schema description coverage, the description only compensates for one of three parameters, leaving the other two to be inferred from their names and defaults.

    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 'Run a general Copilot command' with a specific verb and resource, and the guidance to use args exactly like the shell command after `copilot` distinguishes this tool from the sibling `copilot_prompt` by framing it as a CLI command runner.

    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 gives a usage instruction for args format ('Use args exactly like the shell command after `copilot`'), but it does not explicitly state when to use this tool versus the alternative `copilot_prompt` or mention any exclusions. The usage context is implied rather than explicitly contrasted with alternatives.

    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 full burden. It discloses that execution is non-interactive and that stdout/stderr/exit code are captured and returned. However, it does not mention potential side effects, prerequisites like authentication, or that the prompt may trigger external service calls. This is a moderate level of transparency.

    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, front-loaded sentence with no wasted words. It clearly states the action and the return value, 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?

    Despite having an output schema, the description is too thin for a tool with 7 parameters and a sibling tool. It omits any information about when to use this over `copilot_command`, does not explain non-obvious parameters like `allow_all` or `extra_args`, and lacks any caveats about timeout or output format. The completeness is low.

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

    Parameters2/5

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

    Schema description coverage is 0%, so the description must compensate for unannotated parameters. It only implicitly covers the 'prompt' parameter by mentioning the `copilot -p` command; no explanation is given for cwd, model, allow_all, extra_args, output_format, or timeout_seconds. This is insufficient for a 7-parameter tool.

    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 runs a non-interactive Copilot prompt via `copilot -p` and returns stdout/stderr/exit code. It identifies the specific verb ('run'), resource ('Copilot prompt'), and distinguishes itself from the sibling tool by emphasizing 'non-interactive'.

    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 context by specifying 'non-interactive', which suggests using this tool when you want a one-shot prompt rather than an interactive session. However, it does not explicitly mention the sibling tool `copilot_command` or provide when-not-to-use guidance, so the usage guidance is only implied.

    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

copilot-mcp MCP server

Copy to your README.md:

Score Badge

copilot-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/ohaiibuzzle/copilot-mcp'

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