이 MCP 서버는 Honeybadger 오류 추적 기능을 Cursor IDE와 통합하여 개발 환경에서 직접 오류를 가져와 분석할 수 있도록 해줍니다.
Node.js 18+ 설치됨
API 액세스가 가능한 Honeybadger 계정
MCP를 지원하는 커서 IDE
Related MCP server: Jira Context MCP Server
지엑스피1
npm run build글로벌 설치:
npm install -g honeybadger-mcp그런 다음 커서에서 구성합니다.
{
"mcpServers": {
"honeybadger": {
"command": "honeybadger-mcp",
"env": {
"HONEYBADGER_API_KEY": "your_api_key_here",
"HONEYBADGER_PROJECT_ID": "your_project_id"
}
}
}
}프로젝트 기반 설치(대안):
MCP 서버를 프로젝트 종속성으로 관리하려면 다음을 수행하세요.
npm install honeybadger-mcp이 경우, Cursor mcp_servers.json 의 명령은 프로젝트의 node_modules 내의 로컬 설치를 가리킵니다.
{
"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"
}
}
}
}프로젝트 루트에 .env 파일을 만듭니다.
# 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 키 :
새로운 개인 인증 토큰을 생성하세요
.env 파일의 토큰을 복사하세요
프로젝트 ID :
Honeybadger에서 프로젝트로 이동하세요
프로젝트 ID는 URL: https://app.honeybadger.io/projects/{PROJECT_ID} 에 있습니다.
또는 프로젝트 설정에서 찾아보세요
커서 구성에 MCP 서버를 추가합니다. ~/.cursor/mcp_servers.json 파일(또는 이와 동등한 파일)을 편집합니다.
{
"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"
}
}
}
}이 섹션은 위에서 설명한 대로 업데이트되거나 삭제될 예정입니다. 로컬 경로에서 전역으로 복제하고 설치하는 기존 방식을 선호한다면 그대로 사용할 수 있지만, 업데이트 및 관리가 더 간편하도록 게시된 npm 패키지를 사용하는 것이 좋습니다.
로컬 복제본에서 설치하는 경우:
# 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)구성이 완료되면 Cursor에서 다음 도구를 사용할 수 있습니다.
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_faults환경 및 해결된 상태를 기준으로 선택적으로 필터링하여 최근 오류를 나열합니다.
매개변수:
project_id (선택 사항): 프로젝트 ID
limit (선택 사항): 오류 수(기본값: 20, 최대값: 100)
environment (선택 사항): 환경으로 필터링
resolved (선택 사항): 해결됨 상태로 필터링
get_honeybadger_fault특정 오류에 대한 자세한 정보를 가져옵니다.
매개변수:
fault_id (필수): 오류 ID
project_id (선택 사항): 프로젝트 ID
get_honeybadger_notices특정 오류에 대한 알림(오류 발생)을 가져옵니다.
매개변수:
fault_id (필수): 오류 ID
project_id (선택 사항): 프로젝트 ID
limit (선택 사항): 알림 수 (기본값: 10, 최대값: 100)
analyze_honeybadger_issue포괄적인 분석과 수정 제안을 제공합니다.
매개변수:
fault_id (필수): 오류 ID
project_id (선택 사항): 프로젝트 ID
include_context (선택 사항): 요청 컨텍스트 포함(기본값: true)
최근 오류 나열 : "프로덕션에서 해결되지 않은 최신 오류를 보여주세요"
특정 오류 분석 : "Honeybadger 오류 12345 분석 및 수정 제안"
오류 컨텍스트 가져오기 : "전체 컨텍스트와 함께 오류 12345에 대한 최신 발생 사항 가져오기"
검토 및 수정 : 분석을 사용하여 코드의 문제를 이해하고 수정합니다.
인증 오류 : API 키가 올바르고 적절한 권한이 있는지 확인하세요.
프로젝트를 찾을 수 없습니다 . 프로젝트 ID가 올바른지 확인하세요.
연결 문제 : Honeybadger API에 대한 네트워크 액세스 확인
오류 메시지를 보려면 서버를 직접 실행하세요.
node dist/index.jsMCP 관련 문제에 대한 커서 로그를 확인하세요.
macOS: ~/Library/Logs/Cursor/
윈도우: %APPDATA%\Cursor\logs\
리눅스: ~/.config/Cursor/logs/
API 키를 안전하게 저장하고 버전 제어에 커밋하지 마십시오.
가능하면 환경별 API 키를 사용하세요
이 통합에 대해 읽기 전용 API 토큰을 사용하는 것을 고려하세요.
API 키를 정기적으로 순환하세요
이 MCP 서버를 확장하려면:
setupToolHandlers() 메서드에 새 도구를 추가합니다.
해당 핸들러 메서드를 구현합니다.
도구 목록 및 문서 업데이트
Honeybadger 설정으로 철저히 테스트하세요
다음과 같은 문제가 있는 경우:
이 MCP 서버 : 코드 및 구성을 확인하세요
Honeybadger API : Honeybadger API 문서 를 참조하세요.
커서 MCP 통합 : 커서 설명서 확인
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
