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ChanMeng666

Google Jobs MCP Server

by ChanMeng666

Eine Serverimplementierung des Model Context Protocol (MCP), die über die SerpAPI-Integration Suchfunktionen für Google Jobs bereitstellt. Bietet Mehrsprachenunterstützung, flexible Suchparameter und intelligente Fehlerbehandlung.

Fragen Sie DeepWiki

👉Jetzt ausprobieren!👈

https://github.com/user-attachments/assets/8f6739e1-7db7-4171-88b4-59c6290a4c72

屏幕截图 2024-12-31 183813

屏幕截图 2024-12-31 183754

屏幕截图 2024-12-31 180734

Datum: 31.12.2024, 182106

✨ 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:

  1. Besuchen Sie die SERP API-Website und erstellen Sie ein Konto

  2. 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

  3. 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.

  4. 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

  1. Installieren Sie Abhängigkeiten:

npm install
  1. Umgebung konfigurieren: Ändern Sie Ihre claude_desktop_config.json mit 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"
    }
  }
}
  1. Erstellen Sie den Server:

npm run build
  1. Starten Sie den Server:

npm start

Fehlerbehebung

  1. 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

  1. 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 claude

Manuelle 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-server

Ausführen 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

TyposkriptNodeJSMCP

📖 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 . GitHub LinkedIn

Available Tools

1 tool
search_jobsB

Google Jobs API search tool.

Supported search parameters:

  1. Basic Search: Job title or keywords

  2. Location: City or region

  3. Time Filter: Recently posted jobs

  4. Job Type: Full-time, part-time, contract, internship

  5. Salary Range: Filter by compensation

  6. Geographic Range: Set search radius

  7. Language: Multi-language support

All parameters except 'query' are optional and can be freely combined.

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYesSearch keywords (Required, e.g., 'software engineer', 'data analyst', 'product manager')
locationNoJob location (Optional, e.g., 'New York', 'London', 'Tokyo')
posted_ageNoPosting date filter (Optional) Options: - "today": Posted today - "3days": Last 3 days - "week": Last week - "month": Last month
employment_typeNoJob type (Optional) Options: - "FULLTIME": Full-time - "PARTTIME": Part-time - "CONTRACTOR": Contractor - "INTERN": Internship - "TEMPORARY": Temporary
salaryNoSalary range (Optional) Format examples: - "$50K+": Above $50,000 - "$100K+": Above $100,000 - "$150K+": Above $150,000
radiusNoSearch radius (Optional) Format examples: - "10mi": Within 10 miles - "20mi": Within 20 miles - "50mi": Within 50 miles
hlNoResult language (Optional) Options: - "en": English - "zh-CN": Chinese - "ja": Japanese - "ko": Koreanen
pageNoPage number (Optional, default: 1) - 10 results per page - Supports pagination
sort_byNoSort order (Optional) Options: - "date": Sort by date - "relevance": Sort by relevance - "salary": Sort by salaryrelevance

TDQS

B3.2/5.0
Behavior2/5

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.

Conciseness4/5

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.

Completeness3/5

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.

Parameters3/5

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.

Purpose4/5

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.

Usage Guidelines3/5

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. 1 tool updatev1.0.0
    • First observedsearch_jobs

TDQS

B3.3/5.0

Scored across 1 tool

Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count2/5

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.

Completeness2/5

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

ActivityInactive
ResponsivenessNo issues

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