bgl-mcp-server
Allows managing Backlog issues and comments directly from GitHub Copilot Chat in VSCode.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@bgl-mcp-serverget me the details of issue PROJ-123 and its latest comments"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
bgl-mcp-server
bgl CLIをラップしたMCPサーバーです。
VSCode + GitHub Copilot や Claude Desktop から、直接Backlogの課題を参照・コメント追加ができるようになります。
前提条件
Node.js v20以上
bgl がインストール済み&ログイン済み
# bglのインストール(まだの場合)
brew tap dannygim/tap
brew install bgl
# Backlogにログイン
bgl auth loginRelated MCP server: Backlog MCP Server
インストール
# リポジトリをクローン
git clone <this-repo>
cd bgl-mcp-server
# 依存関係インストール
npm install
# ビルド
npm run buildVSCode + GitHub Copilot での設定
プロジェクトの .vscode/mcp.json を作成:
{
"servers": {
"backlog": {
"command": "node",
"args": ["/path/to/bgl-mcp-server/dist/index.js"]
}
}
}または、グローバル設定 ~/.vscode/mcp.json に追加。
Claude Desktop での設定
~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"backlog": {
"command": "node",
"args": ["/path/to/bgl-mcp-server/dist/index.js"]
}
}
}利用可能なツール
ツール名 | 説明 |
| 課題の内容を取得 |
| 課題のコメント一覧を取得 |
| 課題にコメントを追加 |
使い方の例
Copilot Chatで以下のように話しかけるだけ:
EBC_101-25956 の課題内容を確認してこの課題の原因を分析して、修正方針を検討して検討した修正方針をBacklogにコメントとして投稿してライセンス
MIT
Available Tools
3 toolsbacklog_add_commentA
Backlogの課題にコメントを追加します。作業計画やPR作成報告などに使用できます。
| Name | Required | Description | Default |
|---|---|---|---|
| message | Yes | 投稿するコメント内容 | |
| issueKey | Yes | 課題キー(例: EBC_101-25956) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It only states 'adds a comment' without disclosing behavioral traits like permissions, notifications, or idempotency. Lacks depth for a mutation tool.
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?
Two sentences, front-loaded with main action, no wasted words. Highly concise.
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 simple 2-param tool with no output schema, the description is fairly complete. It explains purpose and usage scenarios. Could mention error cases, but acceptable.
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% with descriptions for both parameters. Description adds no extra meaning beyond schema, so baseline 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 tool adds a comment to a Backlog issue (specific verb+resource). It distinguishes from siblings like backlog_get_issue and backlog_get_comments.
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 usage examples (work plans, PR creation reports) but does not explicitly state when not to use or mention alternatives. Implied usage only.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
backlog_get_commentsA
Backlogの課題に対するコメント一覧を取得します。
| Name | Required | Description | Default |
|---|---|---|---|
| issueKey | Yes | 課題キー(例: EBC_101-25956) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It only states that it retrieves comments, with no mention of read-only behavior, pagination, ordering, or authentication needs. For a read operation, minimal behavioral info is disclosed.
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, terse sentence that directly states the tool's function. No extraneous words, perfectly sized for a simple tool.
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?
Given the tool's simplicity (one required parameter, no output schema), the description is nearly complete. However, it lacks mention of potential pagination or ordering of comments, which might be relevant for an agent to know. Still, for a basic list retrieval, it is adequate.
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?
The schema description coverage is 100% for the single required parameter 'issueKey'. The tool description does not add any additional meaning beyond the schema's parameter description. Thus, baseline 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 tool retrieves a list of comments for a specific issue, using the verb '取得' (retrieve) and the resource 'コメント一覧' (comment list). It differentiates from sibling tools like backlog_get_issue (issue details) and backlog_add_comment (add comment).
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 does not provide explicit guidance on when to use this tool versus alternatives. While the purpose is clear, it does not state that the tool is only for reading comments or when not to use it (e.g., for adding comments). Usage is implied but not articulated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
backlog_get_issueA
Backlogの課題(Issue)を取得します。課題キー(例: EBC_101-25956)を指定してください。
| Name | Required | Description | Default |
|---|---|---|---|
| issueKey | Yes | 課題キー(例: EBC_101-25956, PRJ-123) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description indicates a read operation ('gets') but lacks details on authentication, error handling, or rate limits. Adequate for a simple tool.
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?
Two sentences, no fluff. Information is front-loaded and efficient.
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?
Given the tool's simplicity (one parameter, no output schema), the description is complete: it explains what the tool does and what input is required.
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% (parameter documented). The description adds no extra meaning beyond the schema, so baseline 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 it retrieves a Backlog issue using the issue key. It distinguishes from sibling tools (comments) by focusing on the issue entity.
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?
Provides clear context: use to get an issue by key. No explicit when-not-to or alternatives, but sibling tools are comment-related, so usage is implied.
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.
3 tool updates
v1.0.0- First observed
backlog_add_comment - First observed
backlog_get_comments - First observed
backlog_get_issue
TDQS
Each tool has a clearly distinct purpose: retrieving an issue, listing comments, and adding a comment. No overlap in functionality.
All tool names follow a consistent 'backlog_verb_noun' pattern using snake_case (get_issue, get_comments, add_comment).
Three tools is slightly low but still reasonable for a focused Backlog integration covering basic issue and comment operations.
The set covers reading an issue, reading comments, and adding a comment, but lacks update/delete operations and listing issues, which are notable gaps.
Maintenance
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