Google Jobs MCP Server
Eine Serverimplementierung des Model Context Protocol (MCP), die über die SerpAPI-Integration Suchfunktionen für Google Jobs bereitstellt. Bietet Mehrsprachenunterstützung, flexible Suchparameter und intelligente Fehlerbehandlung.
https://github.com/user-attachments/assets/8f6739e1-7db7-4171-88b4-59c6290a4c72
✨ Funktionen
🌍 Mehrsprachige Unterstützung
Vollständige Lokalisierungsunterstützung für Englisch, Chinesisch, Japanisch und Koreanisch mit automatischer Spracherkennung und Fallback.
🔍 Flexible Suchoptionen
Umfassende Suchparameter, darunter:
Berufsbezeichnung und Schlüsselwörter
Standort mit Radiusfilterung
Beschäftigungsart (Vollzeit, Teilzeit usw.)
Gehaltsspannenfilter
Filtern nach Veröffentlichungsdatum
Ergebnissortierung
💡 Intelligente Fehlerbehandlung
Umfassende Eingabevalidierung
Hilfreiche Fehlermeldungen und Vorschläge
Automatische Vorschläge zur Suchverfeinerung
Handhabung der Ratenbegrenzung
📊 Umfangreiche Jobdetails
Detaillierte Formatierung der Stelleninformationen
Unternehmensvorteile und Highlights
Gehaltsinformationen, sofern verfügbar
Direkte Bewerbungslinks
Zeitstempel für Stellenausschreibungen
🔄 Erweiterte Funktionen
Paginierungsunterstützung
Mehrere Sortieroptionen
Geografische Umkreissuche
Filterung der Beschäftigungsart
🔑 SERP-API-Setup-Anleitung
Bevor Sie beginnen, müssen Sie einen SERP-API-Schlüssel erhalten:
Besuchen Sie die SERP API-Website und erstellen Sie ein Konto
Gehen Sie nach der Registrierung zu Ihrem Dashboard:
Suchen Sie den Abschnitt „API-Schlüssel“
Kopieren Sie Ihren API-Schlüssel
Neue Benutzer erhalten 100 kostenlose API-Aufrufe
Details zur API-Nutzung:
Kostenlose Stufe: 100 Suchvorgänge pro Monat
Bezahlte Pläne beginnen bei 50 $/Monat für 5000 Suchvorgänge
Abrechnung basierend auf erfolgreichen API-Aufrufen
Mehrere Zahlungsmethoden: Kreditkarte, PayPal usw.
Nutzungsbeschränkungen:
Anforderungsrate: 2 Anfragen/Sekunde
IP-Einschränkungen: Keine
Gleichzeitige Anfragen: 5
Antwort-Cache-Zeit: 1 Stunde
👩🔧 Lösung für Verbindungsprobleme von MCP-Servern mit NVM/NPM
Klicken Sie hier, um meine Konfigurationslösung anzuzeigen 👉 https://github.com/modelcontextprotocol/servers/issues/76
🚀 Schnellstart
Installieren Sie Abhängigkeiten:
npm installUmgebung konfigurieren: Ändern Sie Ihre
claude_desktop_config.jsonmit dem folgenden Inhalt (passen Sie die Pfade entsprechend Ihrem System an):
{
"google-jobs": {
"command": "D:\\Program\\nvm\\node.exe",
"args": ["D:\\github_repository\\path_to\\dist\\index.js"],
"env": {
"SERP_API_KEY": "your-api-key"
}
}
}Erstellen Sie den Server:
npm run buildStarten Sie den Server:
npm startFehlerbehebung
Probleme mit API-Schlüsseln:
Schlüssel in der Konfiguration überprüfen
Überprüfen Sie den Schlüsselstatus im SERP-API-Dashboard
Bestätigen Sie, dass der Schlüssel über ein verbleibendes Kontingent verfügt
Suchprobleme:
Überprüfen des Suchparameterformats
Überprüfen der Netzwerkkonnektivität
Überprüfen der Länder-/Sprachcodeunterstützung
📦 Installation
Related MCP server: Scrapeless MCP Server
Installation über Smithery
So installieren Sie Google Jobs für Claude Desktop automatisch über Smithery :
npx -y @smithery/cli install @chanmeng666/google-jobs-server --client claudeManuelle Installation
@chanmeng666/google-jobs-server
# Using npm
npm i @chanmeng666/google-jobs-server
# or
npm install @chanmeng666/google-jobs-server
# Using yarn
yarn add @chanmeng666/google-jobs-server
# Using pnpm
pnpm add @chanmeng666/google-jobs-serverAusführen von Evaluierungen
Das Evals-Paket lädt einen MCP-Client, der anschließend die Datei index.ts ausführt, sodass zwischen den Tests kein Neuaufbau erforderlich ist. Sie können Umgebungsvariablen laden, indem Sie dem Befehl npx voranstellen. Die vollständige Dokumentation finden Sie hier .
OPENAI_API_KEY=your-key npx mcp-eval src/evals/evals.ts src/index.ts💻 Tech-Stack
📖 API-Dokumentation
Der Server implementiert das Model Context Protocol und stellt ein Tool zur Jobsuche mit den folgenden Parametern bereit:
query: Suchabfragezeichenfolge (erforderlich)location: Arbeitsort (optional)posted_age: Filter für das Veröffentlichungsdatum (optional)employment_type: Filter für die Art der Tätigkeit (optional)salary: Gehaltsspannenfilter (optional)radius: Geografischer Suchradius (optional)hl: Sprachcode (optional)page: Seitennummerierung (optional)sort_by: Sortierreihenfolge (optional)
🔧 Entwicklung
# Run in development mode
npm run dev
# Run type checking
npm run typecheck
# Build for production
npm run build📝 Lizenz
Dieses Projekt ist vom MIT lizenziert .
🙋♀ Autor
Erstellt und gepflegt von Chan Meng .
Available Tools
1 toolsearch_jobsB
Google Jobs API search tool.
Supported search parameters:
Basic Search: Job title or keywords
Location: City or region
Time Filter: Recently posted jobs
Job Type: Full-time, part-time, contract, internship
Salary Range: Filter by compensation
Geographic Range: Set search radius
Language: Multi-language support
All parameters except 'query' are optional and can be freely combined.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search keywords (Required, e.g., 'software engineer', 'data analyst', 'product manager') | |
| location | No | Job location (Optional, e.g., 'New York', 'London', 'Tokyo') | |
| posted_age | No | Posting date filter (Optional) Options: - "today": Posted today - "3days": Last 3 days - "week": Last week - "month": Last month | |
| employment_type | No | Job type (Optional) Options: - "FULLTIME": Full-time - "PARTTIME": Part-time - "CONTRACTOR": Contractor - "INTERN": Internship - "TEMPORARY": Temporary | |
| salary | No | Salary range (Optional) Format examples: - "$50K+": Above $50,000 - "$100K+": Above $100,000 - "$150K+": Above $150,000 | |
| radius | No | Search radius (Optional) Format examples: - "10mi": Within 10 miles - "20mi": Within 20 miles - "50mi": Within 50 miles | |
| hl | No | Result language (Optional) Options: - "en": English - "zh-CN": Chinese - "ja": Japanese - "ko": Korean | en |
| page | No | Page number (Optional, default: 1) - 10 results per page - Supports pagination | |
| sort_by | No | Sort order (Optional) Options: - "date": Sort by date - "relevance": Sort by relevance - "salary": Sort by salary | relevance |
TDQS
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 describes the search parameters and their optionality, which is useful, but it doesn't mention rate limits, authentication requirements, error handling, or what the output looks like (e.g., format, pagination details beyond '10 results per page' in the schema). For a tool with 9 parameters and no annotations, 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 appropriately sized and front-loaded, starting with the tool's purpose and followed by a structured list of parameters. Every sentence adds value, with no redundant information. However, the bulleted list could be slightly more concise, and the final sentence about optional parameters is necessary but adds length.
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 complexity (9 parameters, no output schema, no annotations), the description is partially complete. It covers the search parameters well but lacks details on behavioral aspects like rate limits, authentication, and output format. Without annotations or an output schema, the description should do more to compensate, but it provides a functional overview that is adequate for basic 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?
The schema description coverage is 100%, meaning all parameters are well-documented in the input schema itself. The description adds value by summarizing the supported search parameters in a bulleted list and noting their optionality, but it doesn't provide additional semantic context beyond what the schema already covers (e.g., no examples of combined usage). This meets the baseline for high schema coverage.
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 searches for jobs using the Google Jobs API with specific search parameters. It provides a verb ('search') and resource ('jobs'), making the purpose immediately understandable. However, since there are no sibling tools mentioned, it doesn't need to differentiate from alternatives, so a 5 is not warranted.
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 through the list of supported search parameters and notes that all parameters except 'query' are optional. This provides some context for when to use certain features, but it doesn't offer explicit guidance on when to use this tool versus alternatives (none mentioned) or any prerequisites. The guidance is functional but not strategic.
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.
1 tool update
v1.0.0- First observed
search_jobs
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'search_jobs' has a clearly defined and distinct purpose for job searching.
The tool name 'search_jobs' follows a consistent verb_noun pattern. Since there is only one tool, there is no inconsistency to evaluate, and the naming is straightforward and descriptive.
A single tool is too few for a server named 'Google Jobs MCP Server', which implies a broader domain of job-related operations. While search is a core function, the lack of tools for actions like retrieving job details, applying, or managing saved jobs makes the set feel incomplete and thin.
The tool surface is severely incomplete for a jobs domain. It only provides search functionality, missing essential operations such as getting detailed job information, applying to jobs, saving or bookmarking jobs, or filtering by employer. This will likely cause agent failures when trying to perform common job-related tasks beyond basic searching.
Maintenance
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