Emulating the Claude Code Backend for Databricks LLM Models(with MCP, Git Tools, and Prompt Caching)

Claude Code has quickly become one of my favorite tools for repo-aware AI workflows. It understands your codebase, navigates files, summarizes diffs, runs tools, and integrates with Git—all through a simple CLI.

But there’s a catch:
The Claude Code CLI expects to speak directly to Anthropic’s hosted backend.

That means if you want to:

use Databricks-hosted Claude models,

route requests through Azure’s Anthropic /anthropic/v1/messages endpoint,

extend Claude Code with local tools and Model Context Protocol (MCP) servers,

add prompt caching,

or simply run your own backend for experimentation…

…you’re out of luck.

So I built Lynkr, a self-hosted Claude Code–compatible proxy that solves this.

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

🚀 What Lynkr Does

At a high level:

Lynkr is an HTTP proxy that emulates the Claude Code backend, forwards requests to Databricks or Azure Anthropic, and wires in workspace tools, Git helpers, prompt caching, and MCP servers.

You can continue using the regular Claude Code CLI, but point it at your own backend:

Claude Code CLI → Lynkr → Databricks / Azure Anthropic / MCP tools

This lets you keep the familiar development workflow while customizing everything under the hood.

🔧 Core Features

1. Provider Adapters

Built-in support for two upstream providers:

Databricks (default)

Azure Anthropic

Requests are normalized so the CLI sees standard Claude-style responses.

1. Repo Intelligence

Lynkr builds a lightweight SQLite index of your workspace:

Symbol definitions & references

Framework & dependency hints

Language mix

Lint/test config discovery

It also generates a CLAUDE.md summary that gives the model structured context about your project.

1. Git Workflow Integration

Includes Git helpers similar to Claude Code:

status, diff, stage, commit, push, pull

diff review summaries

release-note generation

Plus policy guards:

POLICY_GIT_ALLOW_PUSH

POLICY_GIT_REQUIRE_TESTS

POLICY_GIT_TEST_COMMAND

1. Prompt Caching

A local LRU+TTL cache keyed by prompt signature:

speeds up repeated prompts

reduces Databricks/Azure tokens

avoids re-running identical analysis steps

Tool-invoking turns bypass caching to avoid unsafe side effects.

1. MCP Orchestration

Lynkr automatically:

discovers MCP manifests

launches servers

wraps them with JSON-RPC

exposes all tools back to the assistant

Optional Docker sandboxing isolates MCP tools when needed.

1. Workspace Tools

Includes:

repo indexing

symbol search

diff review

test runner

file I/O tools

lightweight task tracker (TODOs stored in SQLite)

1. Full Transparency

Everything is logged (Pino-based structured logs), including:

request/response traces

repo indexer events

prompt cache hits/misses

MCP registry diagnostics

No black boxes.

🧱 Architecture Overview
Claude Code CLI
↓ (HTTP)
Lynkr Proxy (Express)
├─ Orchestrator (agent loop)
├─ Prompt Cache (LRU + TTL)
├─ Session DB (SQLite)
├─ Repo Indexer (Tree-sitter + CLAUDE.md)
├─ Tool Registry (workspace + git + diff + test)
├─ MCP Registry (JSON-RPC bridge)
└─ Provider Adapters (Databricks / Azure Anthropic)

The codebase is intentionally small and hackable—everything lives in src/.

🛠️ Installing Lynkr
Prerequisites

Node.js 18+

npm

Databricks or Azure Anthropic credentials

(Optional) Docker for MCP sandboxing

(Optional) Claude Code CLI

Install from npm
npm install -g lynkr
lynkr start

or via Homebrew:

brew tap vishalveerareddy123/lynkr
brew install vishalveerareddy123/lynkr/lynkr

or from source:

git clone https://github.com/vishalveerareddy123/Lynkr.git
cd Lynkr
npm install
npm start

⚙️ Configuring the Proxy
Databricks
MODEL_PROVIDER=databricks
DATABRICKS_API_BASE=https://.cloud.databricks.com
DATABRICKS_API_KEY=
WORKSPACE_ROOT=/path/to/repo
PORT=8080

Azure Anthropic
MODEL_PROVIDER=azure-anthropic
AZURE_ANTHROPIC_ENDPOINT=https://.services.ai.azure.com/anthropic/v1/messages
AZURE_ANTHROPIC_API_KEY=
AZURE_ANTHROPIC_VERSION=2023-06-01
WORKSPACE_ROOT=/path/to/repo
PORT=8080

🧩 Hooking Up Claude Code CLI
export ANTHROPIC_BASE_URL=http://localhost:8080
export ANTHROPIC_API_KEY=dummy # required by CLI but unused by Lynkr

Then run the CLI normally inside your repo.

Everything—tool calls, chat, diffs, navigation—flows through your proxy.

🔍 Example: calling a tool
curl http://localhost:8080/v1/messages \
-H "Content-Type: application/json" \
-d '{
"model": "claude-proxy",
"messages": [{ "role": "user", "content": "Rebuild the repo index." }],
"tools": [{
"name": "workspace_index_rebuild",
"type": "function",
"input_schema": { "type": "object" }
}],
"tool_choice": {
"type": "function",
"function": { "name": "workspace_index_rebuild" }
}
}'

🐛 Troubleshooting Highlights

Missing path → check your tool arguments

Git commands blocked → check POLICY_GIT_ALLOW_PUSH

MCP server not discovered → check manifest locations

Prompt cache not working → ensure no tools are used in the request

Web fetch returns HTML scaffolding → JS execution is not supported (use JSON APIs)

🗺️ Roadmap

Coming next:

per-file threaded diff comments

risk scoring on diffs

LSP bridging for deeper language understanding

declarative “skills” layer

historical coverage and test dashboards

🎯 Why I Built This

I love the Claude Code UX, but I wanted the ability to:

run everything locally

plug in Databricks and Azure Anthropic

add my own tools and MCP servers

see and debug all internal behavior

experiment quickly without platform constraints

If you’re exploring AI-assisted development on Databricks or Azure—and want more control over your backend—Lynkr might be useful.

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

⭐ Contributions, ideas, and issues welcome.