release-intel
release-intel-mCP
GitHubリポジトリデータからリリースインテリジェンスを生成するMCPサーバーです。2つのgit参照間のコミット、プルリクエスト、イシュー、コントリビューターを関連付け、リリースノート、チェンジログ、リリースサマリーのAI合成に適した構造化コンテキストを返します。
ツール
get_changes_between_refs
2つのgit参照間のすべてのコミットを、関連するPRメタデータ、著者情報、リンクされたイシューとともに取得します。
フィールド | 型 | 説明 |
| string | GitHubリポジトリの所有者または組織 |
| string | GitHubリポジトリ名 |
| string | ベース参照(古いタグ、ブランチ、またはSHA) |
| string | ヘッド参照(新しいタグ、ブランチ、またはSHA) |
get_pull_requests_in_range
2つの参照間のすべてのマージ済みPRを、ラベルに基づいて自動分類して取得します:breaking、feature、fix、docs、chore、dependencies、other。
入力フィールドはget_changes_between_refsと同じです。
get_release_summary
コミットデータ、PRメタデータ、リンクされたイシュー、コントリビューターリスト、集計統計を組み合わせた構造化リリースコンテキストオブジェクトを生成します。
フィールド | 型 | 説明 |
| string | GitHubリポジトリの所有者 |
| string | GitHubリポジトリ名 |
| string | 前回のリリースタグ(ベース) |
| string | 新しいリリースタグまたはHEAD |
Related MCP server: MCP Releases Server
セットアップ
すべてのオプションで、repo読み取りアクセス権を持つGitHub個人アクセストークンが必要です。
トークンはhttps://github.com/settings/tokensで生成できます。
オプションA:stdio(ローカルプロセス)
標準的なアプローチ:MCPクライアントがサーバーをローカルサブプロセスとして起動します。
Claude Desktop
設定ファイル:~/Library/Application Support/Claude/claude_desktop_config.json
{
"mcpServers": {
"release-intel": {
"command": "npx",
"args": ["-y", "@barissozudogru/release-intel-mcp"],
"env": {
"GITHUB_TOKEN": "ghp_your_token"
}
}
}
}Claude Code
claude mcp add release-intel -e GITHUB_TOKEN=ghp_your_token -- npx -y @barissozudogru/release-intel-mcpCursor
設定ファイル:~/.cursor/mcp.json
{
"mcpServers": {
"release-intel": {
"command": "npx",
"args": ["-y", "@barissozudogru/release-intel-mcp"],
"env": {
"GITHUB_TOKEN": "ghp_your_token"
}
}
}
}Windsurf
設定ファイル:~/.codeium/windsurf/mcp_config.json
{
"mcpServers": {
"release-intel": {
"command": "npx",
"args": ["-y", "@barissozudogru/release-intel-mcp"],
"env": {
"GITHUB_TOKEN": "ghp_your_token"
}
}
}
}VS Code + Copilot
設定ファイル:.vscode/mcp.json
{
"servers": {
"release-intel": {
"type": "stdio",
"command": "npx",
"args": ["-y", "@barissozudogru/release-intel-mcp"],
"env": {
"GITHUB_TOKEN": "ghp_your_token"
}
}
}
}Cline
設定ファイル:~/.cline/mcp_settings.json(またはCline拡張機能の設定UI経由)
{
"mcpServers": {
"release-intel": {
"command": "npx",
"args": ["-y", "@barissozudogru/release-intel-mcp"],
"env": {
"GITHUB_TOKEN": "ghp_your_token"
}
}
}
}Continue.dev
設定ファイル:~/.continue/config.yaml
mcpServers:
- name: release-intel
command: npx
args:
- -y
- "@barissozudogru/release-intel-mcp"
env:
GITHUB_TOKEN: ghp_your_tokenZed
設定ファイル:~/.config/zed/settings.json
{
"context_servers": {
"release-intel": {
"command": {
"path": "npx",
"args": ["-y", "@barissozudogru/release-intel-mcp"],
"env": {
"GITHUB_TOKEN": "ghp_your_token"
}
}
}
}
}JetBrains(AI Assistantプラグイン)
{
"mcpServers": {
"release-intel": {
"command": "npx",
"args": ["-y", "@barissozudogru/release-intel-mcp"],
"env": {
"GITHUB_TOKEN": "ghp_your_token"
}
}
}
}オプションB:HTTP(リモート / ステートレス)
サーバーをHTTPエンドポイントとして実行します。リモートクライアント、共有チームデプロイメント、またはURLベースの接続を好むクライアントに便利です。
GITHUB_TOKEN=ghp_your_token npx @barissozudogru/release-intel-mcp --httpデフォルトではポート3000で起動します。PORTを設定して変更できます。
Cursor(HTTP)
{
"mcpServers": {
"release-intel": {
"url": "http://localhost:3000/mcp"
}
}
}VS Code + Copilot(HTTP)
{
"servers": {
"release-intel": {
"type": "http",
"url": "http://localhost:3000/mcp"
}
}
}Windsurf(HTTP)
{
"mcpServers": {
"release-intel": {
"serverUrl": "http://localhost:3000/mcp"
}
}
}Continue.dev(HTTP)
mcpServers:
- name: release-intel
type: streamable-http
url: http://localhost:3000/mcpヘルスチェックエンドポイントはGET /healthで利用できます。
オプションC:Docker
docker build -t release-intel-mcp .
docker run -p 3000:3000 -e GITHUB_TOKEN=ghp_your_token release-intel-mcpコンテナはデフォルトでHTTPモードで起動します。MCPエンドポイントはhttp://localhost:3000/mcpです。
環境変数
変数 | 必須 | デフォルト | 説明 |
| はい | - | GitHub個人アクセストークン(repoスコープ) |
| いいえ | stdio |
|
| いいえ | 3000 | HTTPポート(HTTPモードのみ) |
ローカル開発
git clone https://github.com/barissozudogru/release-intel-mcp.git
cd release-intel-mcp
npm install
npm run build
GITHUB_TOKEN=ghp_... node dist/index.jsライセンス
MIT
Available Tools
3 toolsget_changes_between_refsGet Changes Between RefsB
Get all commits between two git refs enriched with associated PR metadata, author information, and linked issues. Uses the GitHub compare API.
| Name | Required | Description | Default |
|---|---|---|---|
| base | Yes | Base ref (tag, branch, or commit SHA), the older point | |
| head | Yes | Head ref (tag, branch, or commit SHA), the newer point | |
| repo | Yes | GitHub repository name | |
| owner | Yes | GitHub repository owner (user or organization) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must carry the full burden of behavioral disclosure. It only says the tool 'gets' commits and uses the GitHub compare API; it does not mention pagination, rate limits, auth requirements, potential response size, or any other operational behavior beyond the implied read-only nature.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences with the main operation front-loaded and no filler. The mention of the GitHub compare API is brief and serves as useful context without bloating the definition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with four required and fully documented parameters, the description plus schema covers the core invocation. It also states what the response contains semantically. However, with no output schema and no annotations, it leaves response shape, pagination, and error/limit behavior unspecified, so it is not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline applies. The schema already documents owner, repo, base, and head, including that base is the older point and head is the newer point. The description's phrase 'between two git refs' adds little semantic value beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the action: get all commits between two git refs, and specifies the enrichment (PR metadata, author information, linked issues). It does not explicitly contrast with sibling tools like get_pull_requests_in_range or get_release_summary, but the commit-focused wording makes the distinction mostly clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to use this tool versus the sibling tools. It names the GitHub compare API but does not explain when an agent should prefer this over get_pull_requests_in_range or get_release_summary, nor does it state prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_pull_requests_in_rangeGet Pull Requests in RangeA
Get all merged pull requests between two refs with full metadata including labels, linked issues, review counts, and files changed. PRs are categorized by label into: breaking, feature, fix, docs, chore, dependencies, other.
| Name | Required | Description | Default |
|---|---|---|---|
| base | Yes | Base ref (tag, branch, or commit SHA), the older point | |
| head | Yes | Head ref (tag, branch, or commit SHA), the newer point | |
| repo | Yes | GitHub repository name | |
| owner | Yes | GitHub repository owner (user or organization) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior. It specifies that only merged PRs are returned, lists included metadata (labels, linked issues, review counts, files changed), and explains label categorization. It does not mention auth, pagination, or error behavior, but for a retrieval tool the core behavior is transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no filler. The key action and scope are front-loaded, and the category list adds useful detail without bloating the text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a straightforward read/list tool with all parameters documented in the schema, the description adequately covers what the tool returns and how results are categorized. It does not describe return format or pagination, but it is sufficiently complete for an agent to call it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all four parameters. The description adds no parameter-specific details beyond what the schema provides, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific verb 'Get' and the precise resource ('all merged pull requests between two refs'). It differentiates from the siblings by emphasizing PRs with labels, metadata, and categorization, which is distinct from get_changes_between_refs and get_release_summary.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 the tool: when you need merged PRs between two refs, with the additional distinction that only merged PRs are included. However, it does not explicitly name alternatives or state when not to use this tool, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_release_summaryGet Release SummaryA
Generate a structured release context object ready for AI synthesis into release notes. Combines commit data, PR metadata, linked issues, contributor list, and aggregate statistics for the range between two tags.
| Name | Required | Description | Default |
|---|---|---|---|
| repo | Yes | GitHub repository name | |
| owner | Yes | GitHub repository owner (user or organization) | |
| to_tag | Yes | The new release tag or HEAD (head / newer ref) | |
| from_tag | Yes | The previous release tag (base / older ref) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It transparently describes behavior by listing what data the tool combines and clarifying that the output is a structured object intended for synthesis. It does not discuss side effects, rate limits, or edge cases, but 'Generate' and the aggregation wording sufficiently signal a read-only, non-destructive operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no wasted words. It front-loads the core purpose and then efficiently enumerates the combined data sources, giving the agent maximum signal per word.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, but the description compensates by naming the output type and its major components. The four required parameters are fully covered by the schema. Some detail about output shape or edge cases is absent, but the description is complete enough for an agent to select and invoke the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds only the 'range between two tags' framing, which reinforces from_tag and to_tag semantics but does not meaningfully expand on what the schema already documents for owner, repo, from_tag, or to_tag.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb ('Generate'), a specific resource ('structured release context object'), and the scope ('range between two tags'). It also lists concrete contents (commit data, PR metadata, linked issues, contributors, aggregate statistics), which clearly distinguishes it from sibling tools focused on individual data types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'ready for AI synthesis into release notes' gives a clear context for when to use the tool. It does not explicitly name sibling alternatives or state when not to use them, but the 'Combines...' clause implies that this is the aggregate choice when multiple data sources are needed.
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.
3 tool updates
v0.6.1- First observed
get_changes_between_refs - First observed
get_pull_requests_in_range - First observed
get_release_summary
TDQS
Scored across 3 tools
The three tools are largely distinct: one fetches commits, one fetches PRs, and one combines both into a summary. However, get_changes_between_refs and get_pull_requests_in_range both retrieve PR-related data, which could cause slight confusion for an agent deciding between them. The descriptions provide enough clarity to mitigate overlap.
All tool names follow a consistent 'get_' verb prefix followed by descriptive noun phrases in snake_case (changes_between_refs, pull_requests_in_range, release_summary). This pattern is predictable and aligns with common conventions.
With only 3 tools, the server is lean but each tool serves a clear purpose in the release-intel workflow: retrieving commits, retrieving PRs, and generating a summary. The count is minimal yet sufficient for the stated domain, though it sits at the low end of the ideal range.
The server covers the core operations for generating release context: commits, PRs, and a combined summary. However, there is no tool to list available tags/refs, which would be a natural precursor to using the other tools, and no way to directly fetch issues outside of PRs. These gaps could require agents to work around them.
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