Building a Claude Code-Compatible Proxy for Databricks (with MCP, Git Tools, and Prompt Caching)

[vishalveeraredd]

Many Databricks engineers have asked whether it's possible to use Claude Code CLI directly against Databricks-hosted Claude models instead of Anthropic's cloud API. This enables repo-aware AI workflows—navigation, diffs, testing, MCP tools—right inside their Databricks projects.

I recently built an open-source tool called Lynkr, which acts as a Claude Code–compatible backend that runs locally or inside a Databricks environment. The proxy forwards /v1/messages requests to Databricks Serving Endpoints, while maintaining Claude Code’s structure for chat, tools, context, and workspace actions.

GitHub Repo:
👉https://github.com/vishalveerareddy123/Lynkr

The goal is to make Databricks a first-class environment for LLM-driven development workflows—while keeping everything transparent and configurable.

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What Lynkr Enables for Databricks Users

With Lynkr running locally or in a VM, you can:

✔ Use the Claude Code CLI with Databricks models

No need for Anthropic cloud access. Just point the CLI at:

 

 

export ANTHROPIC_BASE_URL=http://localhost:8080

✔ Connect to Databricks Serving Endpoints

The proxy normalizes requests into the Databricks format and returns a Claude-compatible response.

✔ Enable repo-aware intelligence

Lynkr maintains a lightweight SQLite index of your repo, including:

• symbol search

• cross-file references

• framework/language detection

• auto-generated CLAUDE.md project summary

  This feeds richer context into the model.

  ✔ Use Git + workspace tools

  The proxy implements many of the Git + tooling features you get with Claude Code:

  • status, diff, stage, commit, push

  • automated diff summaries

  • test-gating & policies

  • release-note generation

    ✔ Integrate Model Context Protocol (MCP) servers

    Lynkr automatically discovers MCP manifests (e.g., GitHub, Jira, internal tools) and exposes them as Claude Code tools.

    ✔ Use prompt caching

    You can cache repeated prompts (configurable TTL + LRU size), drastically reducing Databricks compute calls for iterative work.

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    Architecture (High Level)

     

     

    Claude Code CLI
            ↓
       Lynkr Proxy
            ↓
    Databricks Model Serving
            +
       Repo Indexing
            +
          MCP Tools
            +
       Git / Diff Tools

    Everything is visible and tweakable—no hidden backend logic.

    ---

    Getting Started (Databricks Setup)

    1. Install Lynkr

     

    npm install -g lynkr lynkr start

    2. Configure environment

    Create an .env file:

     

    
    

    MODEL_PROVIDER=databricks DATABRICKS_API_BASE=https://<your-workspace>.cloud.databricks.com DATABRICKS_API_KEY=<your-databricks-pat> WORKSPACE_ROOT=/path/to/your/repo PORT=8080 PROMPT_CACHE_ENABLED=true

    3. Point Claude Code CLI to Lynkr

     

    
    

    export ANTHROPIC_BASE_URL=http://localhost:8080/

    export ANTHROPIC_API_KEY="#dummy"

    4. Use Claude Code normally

    Commands like:

     

    
    

    claude explain file.js 

    claude diff 

    claude review

     claude apply

    will now run against Databricks models.

    ---

    Example: Rebuilding the Repo Index

    This is one tool exposed by the proxy:

     

    
    

    curl http://localhost:8080/v1/messages \ -H "Content-Type: application/json" \ -H "x-session-id: test" \ ...

    This refreshes CLAUDE.md, symbol search tables, and all metadata.

    ---

    Why This Matters for Databricks

    Databricks is becoming a powerful environment for:

    • LLM-assisted development

    • agent workflows

    • code automation

    • data/ETL debugging with AI

    • internal tooling built on Claude models

      Lynkr helps bridge the gap between:

      “I have a Databricks model endpoint”
      and
      “I want Claude Code-style interactions with my repo”

      without relying on a closed backend.

      ---

      Roadmap

      Upcoming features:

      • deeper LSP integration (for even smarter repo context)

      • richer diff-thread reviews

      • expanded MCP tooling

      • fine-grained Git risk scoring

      • historical test dashboards

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        Links

        Dev.to 
        DeepWiki 
        
        

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        Closing

        If you’re exploring AI-assisted development inside Databricks, or want to experiment with Claude tools locally, I’d love feedback. Feel free to reply here or open issues/PRs on GitHub.