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

vishalveeraredd — Thu, 04 Dec 2025 07:03:13 GMT

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.

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.
  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" \ -d '{ "model": "claude-proxy", "messages": [{ "role": "user", "content": "Rebuild index" }], "tool_choice": { "type": "function", "function": { "name": "workspace_index_rebuild" } } }'
  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
  Links
  Dev.to
  DeepWiki
  
  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.

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

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

What Lynkr Enables for Databricks Users

✔ Use the Claude Code CLI with Databricks models

✔ Connect to Databricks Serving Endpoints

✔ Enable repo-aware intelligence

✔ Use Git + workspace tools

✔ Integrate Model Context Protocol (MCP) servers

✔ Use prompt caching

Architecture (High Level)

Getting Started (Databricks Setup)

1. Install Lynkr

2. Configure environment

3. Point Claude Code CLI to Lynkr

4. Use Claude Code normally

Example: Rebuilding the Repo Index

Why This Matters for Databricks

Roadmap

Links

Closing