Dieser MCP-Server integriert die Honeybadger-Fehlerverfolgung mit Cursor IDE, sodass Sie Fehler direkt aus Ihrer Entwicklungsumgebung abrufen und analysieren können.
Node.js 18+ installiert
Honeybadger-Konto mit API-Zugriff
Cursor-IDE mit MCP-Unterstützung
Related MCP server: Jira Context MCP Server
git clone git@github.com:vishalzambre/honeybadger-mcp.git
cd honeybadger-mcp
npm installnpm run buildGlobale Installation:
npm install -g honeybadger-mcpKonfigurieren Sie es dann im Cursor:
{
"mcpServers": {
"honeybadger": {
"command": "honeybadger-mcp",
"env": {
"HONEYBADGER_API_KEY": "your_api_key_here",
"HONEYBADGER_PROJECT_ID": "your_project_id"
}
}
}
}Projektbasierte Installation (Alternative):
Wenn Sie den MCP-Server lieber als Projektabhängigkeit verwalten möchten:
npm install honeybadger-mcpIn diesem Fall würde der Befehl in Ihrem Cursor mcp_servers.json auf die lokale Installation innerhalb node_modules Ihres Projekts verweisen:
{
"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"
}
}
}
}Erstellen Sie eine .env Datei im Stammverzeichnis Ihres Projekts:
# 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-Schlüssel :
Gehen Sie zu https://app.honeybadger.io/users/auth\_tokens
Erstellen Sie ein neues persönliches Authentifizierungstoken
Kopieren Sie das Token für Ihre .env Datei
Projekt-ID :
Gehen Sie zu Ihrem Projekt in Honeybadger
Die Projekt-ID befindet sich in der URL: https://app.honeybadger.io/projects/{PROJECT_ID}
Oder finden Sie es in den Projekteinstellungen
Fügen Sie den MCP-Server zu Ihrer Cursor-Konfiguration hinzu. Bearbeiten Sie Ihre Datei ~/.cursor/mcp_servers.json (oder eine entsprechende Datei):
{
"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"
}
}
}
}Dieser Abschnitt wird aktualisiert oder entfernt, da er nun oben behandelt wird. Wenn Sie die alte Methode des Klonens und der globalen Installation von einem lokalen Pfad bevorzugen, können Sie dies weiterhin tun. Für einfachere Updates und Verwaltung wird jedoch die Verwendung des veröffentlichten npm-Pakets empfohlen.
Bei der Installation von einem lokalen Klon:
# 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)Nach der Konfiguration können Sie diese Tools in Cursor verwenden:
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_faultsListet aktuelle Fehler mit optionaler Filterung nach Umgebung und behobenem Status auf.
Parameter:
project_id (optional): Projekt-ID
limit (optional): Anzahl der Fehler (Standard: 20, Max: 100)
environment (optional): Filtern nach Umgebung
resolved (optional): Filtern nach gelöstem Status
get_honeybadger_faultRuft detaillierte Informationen zu einem bestimmten Fehler ab.
Parameter:
fault_id (erforderlich): Die Fehler-ID
project_id (optional): Projekt-ID
get_honeybadger_noticesHolt Hinweise (Fehlervorkommnisse) zu einem bestimmten Fehler.
Parameter:
fault_id (erforderlich): Die Fehler-ID
project_id (optional): Projekt-ID
limit (optional): Anzahl der Hinweise (Standard: 10, Max: 100)
analyze_honeybadger_issueBietet umfassende Analysen mit Lösungsvorschlägen.
Parameter:
fault_id (erforderlich): Die Fehler-ID
project_id (optional): Projekt-ID
include_context (optional): Anforderungskontext einschließen (Standard: true)
Liste der letzten Fehler : „Zeigen Sie mir die letzten ungelösten Fehler aus der Produktion.“
Bestimmten Fehler analysieren : „Honeybadger-Fehler 12345 analysieren und Korrekturen vorschlagen“
Fehlerkontext abrufen : „Die neuesten Vorkommnisse für Fehler 12345 mit vollständigem Kontext abrufen“
Überprüfen und beheben : Verwenden Sie die Analyse, um das Problem in Ihrem Code zu verstehen und zu beheben
Authentifizierungsfehler : Überprüfen Sie, ob Ihr API-Schlüssel korrekt ist und über die entsprechenden Berechtigungen verfügt
Projekt nicht gefunden : Überprüfen Sie, ob Ihre Projekt-ID korrekt ist
Verbindungsprobleme : Überprüfen Sie den Netzwerkzugriff auf die Honeybadger-API
Führen Sie den Server direkt aus, um Fehlermeldungen anzuzeigen:
node dist/index.jsÜberprüfen Sie die Cursor-Protokolle auf MCP-bezogene Probleme:
macOS: ~/Library/Logs/Cursor/
Windows: %APPDATA%\Cursor\logs\
Linux: ~/.config/Cursor/logs/
Speichern Sie API-Schlüssel sicher und übergeben Sie sie niemals der Versionskontrolle
Verwenden Sie nach Möglichkeit umgebungsspezifische API-Schlüssel
Erwägen Sie die Verwendung von schreibgeschützten API-Token für diese Integration
Rotieren Sie Ihre API-Schlüssel regelmäßig
So erweitern Sie diesen MCP-Server:
Fügen Sie neue Tools in der Methode setupToolHandlers() hinzu
Implementieren Sie entsprechende Handlermethoden
Aktualisieren Sie die Werkzeugliste und Dokumentation
Testen Sie gründlich mit Ihrem Honeybadger-Setup
Bei Problemen mit:
Dieser MCP-Server : Überprüfen Sie den Code und die Konfiguration
Honeybadger-API : Siehe Honeybadger-API-Dokumente
Cursor-MCP-Integration : Überprüfen Sie die Cursor-Dokumentation
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.
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