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job-mcp

by md-sakib1122
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Python
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job-mcp

Ein FastMCP-Server (stdio-Transport) mit:

  • Ressource cv://profile — dein CV/Lebenslauf als JSON, damit der LLM-Client ihn lesen kann.

  • Tool send_email — sendet eine E-Mail über SMTP mit Red Mail.

Workflow: Du gibst dem MCP-Client eine Stellenausschreibung + die E-Mail-Adresse des Recruiters im Chat → der Client liest cv://profile → entwirft eine maßgeschneiderte Bewerbungs-E-Mail → ruft send_email auf, um sie zu senden.

1. Installieren

cd job-mcp
python -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -e .

Related MCP server: MCP Email Server

2. SMTP-Zugangsdaten konfigurieren

cp .env.example .env

Bearbeite .env mit deinen SMTP-Zugangsdaten. Für Gmail verwende ein App-Passwort (nicht dein normales Anmelde-Passwort), und aktiviere zuerst die zweistufige Verifizierung.

3. Lebenslauf ausfüllen

Bearbeite src/job_mcp/data/cv.json mit deinen echten Daten (Kontaktinformationen, Fähigkeiten, Berufserfahrung, Ausbildung, Projekte, Zertifizierungen). Die Ressource liest diese Datei einfach unverändert.

4. Server eigenständig ausführen (Plausibilitätsprüfung)

python -m job_mcp.server

Er wartet dann auf MCP-JSON-RPC-Nachrichten auf stdin/stdout (das ist normal — er soll von einem MCP-Client gestartet werden, nicht interaktiv verwendet werden).

Du kannst es auch mit dem MCP-Entwicklungsinspektor untersuchen:

mcp dev src/job_mcp/server.py

5. Mit einem MCP-Client verbinden

Beispielkonfiguration für einen Client, der Server über stdio startet (z.B. die claude_desktop_config.json von Claude Desktop):

{
  "mcpServers": {
    "job-mcp": {
      "command": "/absolute/path/to/job-mcp/.venv/bin/python",
      "args": ["-m", "job_mcp.server"],
      "env": {
        "SMTP_HOST": "smtp.gmail.com",
        "SMTP_PORT": "587",
        "SMTP_USERNAME": "you@gmail.com",
        "SMTP_PASSWORD": "your-app-password",
        "SMTP_SENDER_EMAIL": "you@gmail.com",
        "SMTP_SENDER_NAME": "Your Name"
      }
    }
  }
}

(Du kannst die Zugangsdaten hier in env statt in .env angeben — beides funktioniert, da config.py in jedem Fall aus os.environ liest.)

6. Verwenden

Füge im Client etwa Folgendes ein:

Hier ist eine Stellenausschreibung und die E-Mail-Adresse des Recruiters. Lies meine CV-Ressource, entwirf dann und sende eine maßgeschneiderte Bewerbungs-E-Mail.

Stellenausschreibung: <Text einfügen> Recruiter-E-Mail: hr@company.com

Der Client sollte cv://profile aufrufen, die E-Mail unter Verwendung beider Kontextteile entwerfen und send_email(to=..., subject=..., body_html=..., body_text=...) aufrufen.

Notizen / spätere Härtungsmaßnahmen

  • send_email hat derzeit keinen Bestätigungsschritt in den Server selbst eingebaut — wenn du serverseitig (nicht nur clientseitig) eine „Prüfung vor dem Senden“ als Schutzmaßnahme erzwingen möchtest, füge einen zweistufigen Ablauf hinzu (z.B. ein draft_email-Tool, das eine Vorschau + eine id zurückgibt, und ein confirm_send-Tool, das die E-Mail tatsächlich sendet).

  • Erwäge Ratenbegrenzung oder das Protokollieren gesendeter E-Mails, um versehentliche Doppelsendungen zu vermeiden.

  • cv.json wird bei jedem Ressourcenaufruf neu gelesen, sodass du es bearbeiten kannst, ohne den Server neu zu starten.

Available Tools

1 tool
send_emailA

Send an email via SMTP using Red Mail. Use this to send the final, tailored job-application email to the recruiter/HR address. Provide a polished HTML body (and optionally a plain-text fallback).

NameRequiredDescriptionDefault
ccNo
toYes
bccNo
subjectYes
reply_toNo
body_htmlYes
body_textNo

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Since no annotations are provided, the description carries the behavioral disclosure burden. It transparently states that the tool sends an email via SMTP, which is an external side effect, and adds a practical requirement to provide polished HTML. It omits details like SMTP prerequisites or failure modes, but the core behavior is explicit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, front-loaded with the core action, and every clause adds value: the action, the use case, and the body-format expectation. No redundant or generic phrases.

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?

The tool has 7 parameters, no annotations, and no output schema, so the description must be reasonably self-contained. It covers the main action, the intended purpose, and the key body requirement. However, it leaves several parameter semantics and any error/response behavior unaddressed. The operation is simple and the names are fairly self-evident, so the gaps are moderate rather than severe.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It only explains body_html and optionally body_text; to, subject, cc, bcc, and reply_to are left undocumented. While the parameter names are conventional, the description does not add sufficient meaning for a zero-coverage schema, especially for required fields like 'to' and 'subject'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Send an email via SMTP using Red Mail') and the specific use case ('final, tailored job-application email to the recruiter/HR address'). It is a precise verb+resource combination, with no ambiguity or tautology.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use this to send the final, tailored job-application email to the recruiter/HR address', giving a clear when-to-use context. There are no sibling tools listed, so there is no opportunity to name alternatives or when-not scenarios, but the intended scenario is well defined.

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 updatev0.1.0
    • First observedsend_email
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.9/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or misselection. The tool's purpose is clearly distinct simply because it is the only tool.

Naming Consistency5/5

A single tool name cannot be inconsistent with itself. 'send_email' follows a clear verb_noun convention, and there are no other names to compare against.

Tool Count2/5

One tool feels too thin for a server named 'job-mcp', which implies a broader job-application workflow. While sending an email is a concrete task, the server likely needs supporting tools for content generation or attachment handling to be useful.

Completeness2/5

The surface is severely limited: it only sends an email. Missing operations include email preview, attachment support, and any integration with job-application data, leaving obvious gaps for real-world workflows.

Maintenance

ActivityStale
ResponsivenessNo issues

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