# Career Copilot > AI hiring copilot built on Google ADK v2. Recruiter mode scores fit + drafts outreach. Candidate mode detects agency postings + generates full interview prep. - **Type:** MCP server - **Install:** `agentstack add mcp-naji-najari-career-copilot` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [Naji-Najari](https://agentstack.voostack.com/s/naji-najari) - **Installs:** 0 - **Category:** [Cloud & Infrastructure](https://agentstack.voostack.com/c/cloud-infrastructure) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [Naji-Najari](https://github.com/Naji-Najari) - **Source:** https://github.com/Naji-Najari/career-copilot - **Website:** https://career-copilot.najinajari.com ## Install ```sh agentstack add mcp-naji-najari-career-copilot ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About Career Copilot A multi-agent CV × JD pipeline on Google ADK v2. Live demo · Project write-up --- Career Copilot reads a CV against a job description and produces a different output depending on who is asking. Recruiters get a fit verdict and a LinkedIn outreach draft that quotes a real achievement from the CV. Candidates get a company brief researched live through Tavily MCP and a tailored interview-prep bundle. The point of the project is the orchestration. Google ADK v2 shipped its graph workflow API recently, in the same spirit as LangGraph. This is an end-to-end test of it on a real use case. ## Why a graph A single big-prompt agent works until you need any of: deterministic branching, parallel work, typed contracts at every step, or per-step tracing. Once you do, a graph stops being optional. - **Explicit topology**: nodes, edges, parallel branches, conditional routing, and sync points are first-class. - **Testable units**: each node can be tested, swapped, or traced in isolation. - **Typed state**: contracts at every boundary instead of free-form prompt parsing. - **Predictable cost**: you know upfront which steps run, in what order, and which model serves each. ## How it works ```mermaid flowchart LR IN(["CV + JD+ mode"]):::io IN --> CV[CV Parser]:::agent IN --> JD[JD Parser]:::agent CV --> MR(["Mode Router"]):::router JD --> MR MR -- recruiter --> FA[Fit Analyzer]:::agent FA --> VR(["Verdict Router"]):::router VR -- fit / borderline --> OW["Outreach Writermedium"]:::agent VR -- no_fit --> GE[Gap Explainer]:::agent MR -- candidate --> CFORK((·)):::fork CFORK --> RA["Research Agent+ Tavily MCP"]:::agent CFORK --> CO[CV Optimizer]:::agent RA --> IP[Interview Prep]:::agent CO --> IP OW --> OUT1(["RecruiterFit"]):::io GE --> OUT2(["RecruiterNoFit"]):::io IP --> OUT3(["CandidateResp"]):::io classDef agent fill:#E3F2FD,stroke:#1565C0,stroke-width:2px,color:#0D47A1 classDef router fill:#E8F5E9,stroke:#2E7D32,stroke-width:1.5px,color:#1B5E20 classDef fork fill:#9E9E9E,stroke:#616161,stroke-width:1px,color:#9E9E9E classDef io fill:#F5F5F5,stroke:#616161,stroke-width:1px,color:#212121 ``` CV and JD are parsed in parallel. The mode router (a `FunctionNode`, not an LLM) splits the flow: - **Recruiter branch**: linear chain. Fit Analyzer scores fit, Verdict Router routes to Outreach Writer (on fit / borderline) or Gap Explainer (on no_fit). - **Candidate branch**: parallel fan-out. Research Agent (Tavily MCP) and CV Optimizer run concurrently, synchronize through a `JoinNode`, then feed Interview Prep. Internal `JoinNode`s are omitted from the diagram for clarity. See [`app/agent/agent.py`](backend/app/agent/agent.py) for the full topology. ## Architecture decisions The graph encodes a deliberate separation between deterministic control flow and LLM-driven generation. Routing and synchronization stay in pure Python; only generation crosses an LLM boundary, and every output is constrained by a Pydantic schema before it leaves a node. - **Control flow stays out of the LLMs.** Routing lives in `FunctionNode`s that decide branches in pure Python on already-typed state. `JoinNode`s wait for parallel branches to complete before firing downstream. No LLM ever picks which branch fires next, which removes a whole class of failure modes from the critical path. - **One schema per agent.** Each LLM agent declares its own `output_schema`. Invalid output fails the node loud and fast instead of corrupting state in a downstream agent. - **Tool-use escape hatch.** ADK currently disallows combining `output_schema` with tools on `gpt-5.4-mini`. The Research Agent uses the Tavily MCP toolset and emits `CompanyIntelligence` as a JSON string, validated with `model_validate_json` at the API boundary so the typed-state invariant is preserved at the edge. ## Agents All agents run OpenAI `gpt-5.4-mini` via `LiteLlm`. The Outreach Writer is the only one dialled up to medium reasoning effort. | Agent | Role | Output schema | | ------------------ | --------- | ------------------------- | | CV Parser | Parser | `ParsedCV` | | JD Parser | Parser | `ParsedJD` | | Fit Analyzer | Recruiter | `FitVerdict` | | Outreach Writer | Recruiter | `OutreachDraft` | | Gap Explainer | Recruiter | `GapReport` | | Research Agent | Candidate | `CompanyIntelligence` | | CV Optimizer | Candidate | `CVOptimizationBundle` | | Interview Prep | Candidate | `InterviewPrepBundle` | ## Tracing Every `/v1/analyze` run is traced end-to-end with Langfuse. Sub-agent calls, tool calls, latency, and token counts nest under a parent agent observation. The filterable trace attributes propagated to Langfuse carry only metadata and sizes (`mode`, `model`, `version`, `cv_chars`, `jd_chars`); raw CV and JD content is kept out of the trace tags so traces stay free of PII. ## Stack | Layer | Stack | | --------- | ------------------------------------------------------------------ | | Backend | Python 3.12, FastAPI, Google ADK v2, `uv` | | Models | OpenAI `gpt-5.4-mini` via `LiteLlm` | | Research | Tavily via MCP (`McpToolset`) | | Frontend | Next.js 15, React 19, Tailwind v4, shadcn/ui, TanStack Query, Zod | | Tracing | Langfuse | | Deploy | Docker (Cloud Run / Fly.io / HuggingFace Spaces) | ## Run it ### Prerequisites - Python 3.12 with [uv](https://docs.astral.sh/uv/) - An [OpenAI API key](https://platform.openai.com/api-keys) for `gpt-5.4-mini` - A [Tavily API key](https://tavily.com) for the candidate-mode Research Agent ### Install and configure ```bash cd backend uv sync cp .env.example .env # fill in OPENAI_API_KEY and TAVILY_API_KEY ``` ### Start the backend ```bash uv run uvicorn app.main:app --reload --port 8080 ``` API docs at . Try `POST /v1/analyze` with `{"cv_text": "...", "jd_text": "...", "mode": "recruiter" | "candidate"}`. ### Run the tests ```bash uv run pytest tests/ -q ``` ## License [MIT](./LICENSE) ## Source & license This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [Naji-Najari](https://github.com/Naji-Najari) - **Source:** [Naji-Najari/career-copilot](https://github.com/Naji-Najari/career-copilot) - **License:** MIT - **Homepage:** https://career-copilot.najinajari.com Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** yes - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/mcp-naji-najari-career-copilot - Seller: https://agentstack.voostack.com/s/naji-najari - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.