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kud

mcp-github-copilot

by kud
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Local

TypeScript Node.js npm MIT

MCP server for GitHub Copilot — query any Copilot model programmatically via the official SDK.

Website · Documentation

Features

  • No extra API key — uses your existing GitHub Copilot CLI credentials automatically.

  • Any model — target GPT-5, Codex, Claude Sonnet, or any model your subscription grants.

  • File and image attachments — attach local files or base64 blobs alongside a prompt.

  • Model discovery — list available models with context window limits and billing multipliers.

  • Streaming progress — sends MCP progress notifications for each streamed chunk.

Related MCP server: BMAD MCP Server

Install

npm install -g @kud/mcp-github-copilot

Usage

Add the server to your MCP client configuration:

{
  "mcpServers": {
    "mcp-github-copilot": {
      "command": "mcp-github-copilot"
    }
  }
}

Tools

Tool

Description

query

Send a prompt to a Copilot model and return the response. Accepts an optional model name and optional attachments (file paths or base64 blobs).

list_models

List all available Copilot models with capabilities, context window limits, and billing multipliers.

Development

git clone https://github.com/kud/mcp-github-copilot.git
cd mcp-github-copilot
npm install
npm run dev

📚 Full documentation → mcp-github-copilot/docs

Available Tools

2 tools
list_modelsA

List available Copilot models.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are present, so the description must carry all behavioral information. It only implies a read-only listing and offers no details about authentication, output format, or possible limitations. This falls short of disclosing meaningful behavioral traits.

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, focused sentence with no unnecessary words. It is appropriately sized for the tool's simplicity.

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 zero-parameter listing tool, the description is adequate but minimal. It doesn't specify what information is returned or what 'available' means, and since there's no output schema, the agent has limited context. It's functional but not thorough.

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?

The tool has zero parameters, so no parameter documentation is needed. The baseline of 4 applies because nothing is required to clarify argument 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 action ('List') and the resource ('available Copilot models'), making it distinct from sibling tools like 'query'. The verb and target are specific and unambiguous.

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?

No guidance is provided on when to use this tool versus alternatives. The description simply states what it does without indicating scenarios or exclusions, leaving the agent without decision support.

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

queryA

Send a prompt to GitHub Copilot and return the response. Uses logged-in Copilot CLI credentials automatically.

ParametersJSON Schema
NameRequiredDescriptionDefault
modelNoModel to use (e.g. gpt-5, gpt-5.3-codex, claude-sonnet-4.5). Defaults to Copilot default.
promptYesThe prompt to send to Copilot
attachmentsNoFile or image attachments to include with the prompt

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description must carry behavioral disclosure. It usefully notes that logged-in Copilot CLI credentials are used automatically, but it does not describe output format, potential errors, rate limits, or other behavioral traits.

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?

Two sentences, no filler, front-loaded with the core purpose and a key behavioral note. Every word earns its place.

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 relatively simple prompt-response tool with a thorough schema, the description is nearly sufficient. It lacks detail about the response format and edge cases, but the core function is clearly stated; no output schema further increases the need, yet the simplicity keeps this at a 4.

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 each parameter described in the schema. The description adds no parameter-level detail beyond what the schema already provides, so the baseline of 3 applies.

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 states a specific verb and resource: 'Send a prompt to GitHub Copilot and return the response.' It clearly distinguishes this tool from the sibling list_models, which lists models rather than querying one.

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?

Usage is implied: use when you want to send a prompt to GitHub Copilot. There is no explicit 'when not to use' or mention of alternatives, but the contrast with list_models provides some implicit guidance.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 2 tool updatesv1.2.0
    • First observedlist_models
    • First observedquery
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TDQS

A3.5/5.0
Disambiguation5/5

The two tools are completely distinct: one sends prompts to Copilot, the other lists available models. There is no overlap or ambiguity between them.

Naming Consistency2/5

The naming is inconsistent: 'query' is a single-word verb while 'list_models' follows a verb_noun pattern. The generic 'query' also lacks specificity compared to 'list_models'.

Tool Count3/5

With only two tools, the server feels minimal but borderline appropriate for a basic Copilot integration. It covers essential actions but leaves little room for a richer workflow.

Completeness3/5

The server covers the core action of querying Copilot and listing models, but lacks features like streaming, conversation management, or model details, which may be expected in a fuller implementation.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

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