この MCP サーバーは、Honeybadger エラー追跡を Cursor IDE と統合し、開発環境から直接エラーを取得して分析できるようにします。
Node.js 18以上がインストールされている
APIアクセス可能なHoneybadgerアカウント
MCP をサポートするカーソル IDE
Related MCP server: Jira Context MCP Server
git clone git@github.com:vishalzambre/honeybadger-mcp.git
cd honeybadger-mcp
npm installnpm run buildグローバルインストール:
npm install -g honeybadger-mcp次に、カーソルで設定します。
{
"mcpServers": {
"honeybadger": {
"command": "honeybadger-mcp",
"env": {
"HONEYBADGER_API_KEY": "your_api_key_here",
"HONEYBADGER_PROJECT_ID": "your_project_id"
}
}
}
}プロジェクトベースのインストール（代替）:
MCP サーバーをプロジェクトの依存関係として管理する場合:
npm install honeybadger-mcpこの場合、カーソルmcp_servers.json内のコマンドは、プロジェクトのnode_modules内のローカル インストールを指します。
{
"mcpServers": {
"honeybadger": {
"command": "node",
"args": ["./node_modules/honeybadger-mcp/dist/index.js"],
"env": {
"HONEYBADGER_API_KEY": "your_api_key_here",
"HONEYBADGER_PROJECT_ID": "your_project_id"
}
}
}
}プロジェクト ルートに.envファイルを作成します。
# Required: Your Honeybadger API key
HONEYBADGER_API_KEY=your_api_key_here
# Optional: Default project ID (can be overridden per request)
HONEYBADGER_PROJECT_ID=your_project_id
# Optional: Custom Honeybadger URL (defaults to https://app.honeybadger.io)
HONEYBADGER_BASE_URL=https://app.honeybadger.ioAPIキー:
https://app.honeybadger.io/users/auth\_tokensにアクセスしてください。
新しい個人認証トークンを作成する
.envファイルのトークンをコピーします
プロジェクトID :
Honeybadgerのプロジェクトへ移動
プロジェクト ID は URL にあります: https://app.honeybadger.io/projects/{PROJECT_ID}
またはプロジェクト設定で見つける
MCPサーバーをCursorの設定に追加します。 ~/.cursor/mcp_servers.json （または同等のファイル）を編集します。
{
"mcpServers": {
"honeybadger": {
"command": "node",
"args": ["/path/to/honeybadger-mcp/dist/index.js"],
"env": {
"HONEYBADGER_API_KEY": "your_api_key_here",
"HONEYBADGER_PROJECT_ID": "your_project_id"
}
}
}
}このセクションは、上記の説明に従って更新または削除されます。ローカルパスからクローンしてグローバルにインストールする従来の方法をご希望の場合は、引き続きご利用いただけますが、更新と管理を容易にするために、公開されているnpmパッケージの使用をお勧めします。
ローカルクローンからインストールする場合:
# Navigate to your cloned honeybadger-mcp directory
npm install -g . # Installs from the current directory
# Then configure in Cursor as before
# ... (Cursor configuration for local global install)設定が完了すると、Cursor で次のツールを使用できるようになります。
List recent unresolved errors from Honeybadger in production environmentGet details for Honeybadger fault ID 12345Analyze Honeybadger issue 12345 and provide fix suggestionsGet the latest 5 occurrences for Honeybadger fault 12345list_honeybadger_faultsオプションで環境および解決ステータス別にフィルタリングして最近の障害を一覧表示します。
パラメータ:
project_id (オプション): プロジェクトID
limit （オプション）：障害の数（デフォルト：20、最大：100）
environment （オプション）: 環境でフィルタリング
resolved （オプション）: 解決済みのステータスでフィルタリング
get_honeybadger_fault特定の障害に関する詳細情報を取得します。
パラメータ:
fault_id (必須): 障害ID
project_id (オプション): プロジェクトID
get_honeybadger_notices特定の障害に関する通知（エラー発生）を取得します。
パラメータ:
fault_id (必須): 障害ID
project_id (オプション): プロジェクトID
limit （オプション）：通知の数（デフォルト：10、最大：100）
analyze_honeybadger_issue包括的な分析と修正提案を提供します。
パラメータ:
fault_id (必須): 障害ID
project_id (オプション): プロジェクトID
include_context (オプション): リクエストコンテキストを含める (デフォルト: true)
最近のエラーを一覧表示する: 「本番環境からの最新の未解決エラーを表示します」
特定のエラーを分析する: 「Honeybadger 障害 12345 を分析し、修正を提案する」
エラー コンテキストを取得: 「障害 12345 の最新の発生を完全なコンテキストで取得する」
レビューと修正: 分析を使用してコードの問題を理解し、修正します
認証エラー: APIキーが正しく、適切な権限があることを確認してください
プロジェクトが見つかりません: プロジェクトIDが正しいことを確認してください
接続の問題: Honeybadger API へのネットワーク アクセスを確認してください
エラー メッセージを表示するには、サーバーを直接実行します。
node dist/index.jsMCP 関連の問題については、カーソル ログを確認してください。
macOS: ~/Library/Logs/Cursor/
Windows: %APPDATA%\Cursor\logs\
Linux: ~/.config/Cursor/logs/
APIキーを安全に保存し、バージョン管理にコミットしないでください
可能な場合は環境固有の API キーを使用する
この統合には読み取り専用APIトークンの使用を検討してください
APIキーを定期的にローテーションする
この MCP サーバーを拡張するには:
setupToolHandlers()メソッドに新しいツールを追加する
対応するハンドラメソッドを実装する
ツールリストとドキュメントを更新する
Honeybadgerの設定で徹底的にテストする
以下の問題については:
このMCPサーバー: コードと構成を確認してください
Honeybadger API : Honeybadger APIドキュメントを参照してください
カーソルMCP統合: カーソルのドキュメントを確認してください
Comprehensive analysis of a Honeybadger issue with fix suggestions
| Name | Required | Description | Default |
|---|---|---|---|
| fault_id | Yes | The ID of the fault to analyze | |
| project_id | No | Optional project ID (uses env var if not provided) | |
| include_context | No | Include request context and parameters in analysis |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'analysis' and 'fix suggestions' imply a read-only operation, it doesn't specify whether this requires authentication, has rate limits, or what the output format looks like. For a tool with zero annotation coverage, this leaves significant behavioral gaps.
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, efficient sentence that clearly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded with the core functionality.
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 moderate complexity (analysis with suggestions), no annotations, and no output schema, the description is minimally adequate. It states what the tool does but lacks details on output format, error handling, or integration with siblings. It meets basic requirements but has clear gaps for effective agent use.
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 documents all three parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain what 'comprehensive analysis' entails or how parameters affect it). Baseline 3 is appropriate when the schema does the heavy lifting.
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 performs 'comprehensive analysis' of a Honeybadger issue and provides 'fix suggestions', which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'get_honeybadger_fault' or 'get_honeybadger_notices', which might provide similar data without analysis or suggestions.
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 no guidance on when to use this tool versus alternatives. There's no mention of when to choose this analysis tool over the sibling 'get' tools, nor any prerequisites or exclusions. The agent must infer usage from the description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch a specific fault/error from Honeybadger by ID
| Name | Required | Description | Default |
|---|---|---|---|
| fault_id | Yes | The ID of the fault to fetch | |
| project_id | No | Optional project ID (uses env var if not provided) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('Fetch') but does not describe what 'fetch' entails—e.g., whether it's a read-only operation, requires authentication, has rate limits, or what the return format looks like. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
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, efficient sentence that front-loads the core purpose ('Fetch a specific fault/error from Honeybadger by ID'). There is no wasted language or redundancy, making it highly concise and well-structured for quick understanding.
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 lack of annotations and output schema, the description is incomplete. It does not cover behavioral aspects like safety, authentication, or response format, which are crucial for a tool that fetches data. While the purpose is clear, the overall context for effective use by an AI agent is insufficient.
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%, with both parameters ('fault_id' and 'project_id') fully described in the schema. The description adds no additional meaning beyond what the schema provides, such as explaining parameter interactions or usage nuances. Baseline 3 is appropriate when the schema does the heavy lifting.
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 action ('Fetch') and resource ('a specific fault/error from Honeybadger by ID'), distinguishing it from sibling tools like 'list_honeybadger_faults' (which presumably lists multiple faults) and 'get_honeybadger_notices' (which fetches notices rather than faults). It precisely communicates the tool's function without ambiguity.
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 implies usage by specifying 'by ID,' suggesting it's for retrieving a single, known fault. However, it does not explicitly state when to use this tool versus alternatives like 'list_honeybadger_faults' or 'analyze_honeybadger_issue,' nor does it mention prerequisites or exclusions. The guidance is implied but lacks explicit comparison.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch notices (occurrences) for a specific fault
| Name | Required | Description | Default |
|---|---|---|---|
| fault_id | Yes | The ID of the fault to fetch notices for | |
| project_id | No | Optional project ID (uses env var if not provided) | |
| limit | No | Number of notices to fetch (default: 10, max: 100) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('Fetch') but does not cover critical aspects like whether this is a read-only operation, potential rate limits, authentication needs, error handling, or what the return format looks like (e.g., pagination, structure). This leaves significant gaps for a tool with no annotation support.
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, direct sentence that efficiently conveys the core purpose without any redundant or unnecessary information. It is front-loaded and appropriately sized for the tool's complexity.
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 lack of annotations and no output schema, the description is incomplete. It does not address behavioral traits (e.g., safety, performance) or explain what the tool returns, which is critical for a tool with three parameters and no structured output documentation. The high schema coverage is insufficient to compensate for these gaps.
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 input schema has 100% description coverage, clearly documenting all three parameters (fault_id, project_id, limit) with their purposes, optionality, and defaults. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline for high schema coverage without compensating value.
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 action ('Fetch') and resource ('notices (occurrences) for a specific fault'), distinguishing it from sibling tools like 'get_honeybadger_fault' (which fetches fault details) and 'list_honeybadger_faults' (which lists faults). It precisely defines the scope of the operation.
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 no guidance on when to use this tool versus alternatives. It does not mention sibling tools like 'analyze_honeybadger_issue' or explain scenarios where fetching notices is appropriate versus other operations, leaving the agent without contextual usage cues.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List recent faults from Honeybadger
| Name | Required | Description | Default |
|---|---|---|---|
| project_id | No | Optional project ID (uses env var if not provided) | |
| limit | No | Number of faults to fetch (default: 20, max: 100) | |
| environment | No | Filter by environment (e.g., production, staging) | |
| resolved | No | Filter by resolved status |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'List recent faults' but doesn't explain what 'recent' means (e.g., time range), how results are ordered, pagination behavior, or error handling. This leaves significant gaps for a tool with 4 parameters and no output schema.
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, clear sentence with zero waste. It's front-loaded with the core action and resource, making it highly efficient and easy to parse.
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 complexity (4 parameters, no annotations, no output schema), the description is inadequate. It doesn't explain return values, error conditions, or behavioral traits like rate limits or authentication needs. For a list operation with filtering options, more context is needed to use it 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 description coverage is 100%, so the schema fully documents all 4 parameters. The description adds no additional parameter information beyond implying a list operation. This meets the baseline of 3, as the schema handles the heavy lifting, but the description doesn't enhance understanding of parameter interactions or defaults.
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 action ('List') and resource ('recent faults from Honeybadger'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'get_honeybadger_fault' or 'get_honeybadger_notices', which likely retrieve specific items rather than lists.
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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'analyze_honeybadger_issue' or 'get_honeybadger_fault', nor does it specify use cases or prerequisites beyond the basic action.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
analyze_honeybadger_issueget_honeybadger_faultget_honeybadger_noticeslist_honeybadger_faultsScored across 4 tools
Each tool has a clearly distinct purpose with no overlap: analyze_honeybadger_issue provides analysis and fix suggestions, get_honeybadger_fault retrieves a specific fault by ID, get_honeybadger_notices fetches occurrences for a fault, and list_honeybadger_faults lists recent faults. The descriptions make it easy to distinguish between analyzing, fetching specific items, fetching related data, and listing collections.
All tool names follow a consistent verb_noun pattern with snake_case: analyze_honeybadger_issue, get_honeybadger_fault, get_honeybadger_notices, and list_honeybadger_faults. The naming is predictable and readable, using clear verbs like 'analyze', 'get', and 'list' paired with specific nouns.
With 4 tools, the count is reasonable for a Honeybadger server, covering core operations like listing, fetching, and analyzing faults. It's slightly on the minimal side but well-scoped for basic error monitoring tasks, though additional tools for actions like creating or updating issues might be expected in a more comprehensive set.
The tool set covers key read operations for Honeybadger: listing faults, fetching specific faults and their notices, and analyzing issues. However, it lacks write operations (e.g., creating, updating, or resolving faults) and broader management features, which are minor gaps that agents might need to work around for full lifecycle coverage.
Error tracking for Laravel and PHP apps. Let your agent fetch errors, stack traces and context.
The bug mediator for AI-built apps: plain-English diagnosis + a ready fix, in your editor.
Product Hunt for AI tools. Search, submit, upvote from Claude Code or Cursor.
Investigate errors, track deployments, analyze performance, and manage application monitoring
