ContextBridge

by tijuthomas5

308 downloads

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GitHub

About

Local-first code retrieval for AI agents — cuts codebase context from tens of thousands of tokens to a few hundred, with zero hallucinated file paths.

Details

Author: tijuthomas5
Downloads: 308
Categories: Developer Tools, Knowledge Base

- Keyword-first hybrid retrieval with guarded vector assist
- Optional local AI analysis that validates and re-ranks results
- Swappable profile plugins for project-specific ranking
- MCP tools: search_context_hybrid, find_code_locations, get_module_summary, record_outcome, and more
- Rerunnable safe setup that never overwrites existing configs

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name ContextBridge

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "contextbridge": {
            "context_bridge": {
                "url": "http://127.0.0.1:8755/sse"
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "contextbridge": {
            "context_bridge": {
                "url": "http://127.0.0.1:8755/sse"
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "contextbridge": {
            "context_bridge": {
                "url": "http://127.0.0.1:8755/sse"
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "contextbridge": {
            "context_bridge": {
                "url": "http://127.0.0.1:8755/sse"
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "contextbridge": {
            "context_bridge": {
                "url": "http://127.0.0.1:8755/sse"
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "contextbridge": {
            "context_bridge": {
                "url": "http://127.0.0.1:8755/sse"
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "contextbridge": {
            "context_bridge": {
                "url": "http://127.0.0.1:8755/sse"
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "contextbridge": {
            "context_bridge": {
                "url": "http://127.0.0.1:8755/sse"
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "contextbridge": {
            "context_bridge": {
                "url": "http://127.0.0.1:8755/sse"
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "contextbridge": {
            "context_bridge": {
                "url": "http://127.0.0.1:8755/sse"
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "contextbridge": {
            "context_bridge": {
                "url": "http://127.0.0.1:8755/sse"
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "contextbridge": {
            "context_bridge": {
                "url": "http://127.0.0.1:8755/sse"
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "contextbridge": {
            "context_bridge": {
                "url": "http://127.0.0.1:8755/sse"
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "contextbridge": {
            "context_bridge": {
                "url": "http://127.0.0.1:8755/sse"
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "contextbridge": {
            "context_bridge": {
                "url": "http://127.0.0.1:8755/sse"
            }
        }
    }
}

From the repository

Setup runs via platform-specific batch scripts (setup_context_bridge.bat on Windows, equivalents in setup/mac/ or setup/linux/). Edit the generated config.hybrid.json to point settings.discovery.* at your source folders, re-run setup, then start the server. By default ContextBridge runs SSE at http://127.0.0.1:8755/sse; set CONTEXT_BRIDGE_TRANSPORT=stdio for stdio transport. A dashboard is available at http://127.0.0.1:8795.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "contextbridge": {
            "context_bridge": {
                "url": "http://127.0.0.1:8755/sse"
            }
        }
    }
}

McpServers

{
    "context_bridge": {
        "url": "http://127.0.0.1:8755/sse"
    }
}

Without CB, an AI coding agent either guesses which files are relevant, or you paste entire source files into the chat — burning thousands of input tokens on code that isn't needed.

With CB, the AI calls a single MCP tool and gets back acompact, ranked result: the owner file, related files, key symbols, and a dependency summary — typically a few hundred tokens instead of tens of thousands of lines of raw source.

This isn't limited to bug investigations — the same tool answers general questions about how an existing feature or workflow is implemented.

The optional local AI analysis stage further compresses the result before it reaches your cloud AI — so you pay even less.

Scope note:ContextBridge is a codebase routing and retrieval tool, not a reasoning engine — it finds the right files, symbols, and connections, but does not prove causality or choose the fix for you. SeeIntended Scopefor the full boundary.

💡 New here?Don't want to read everything? Ask your AI assistant (Claude, ChatGPT, Gemini, etc.) to read the docs/folder and guide you through setup for your OS and project.

A local-first code retrieval layer for AI coding agents. ContextBridge indexes your codebase (via Graphifyoutput), then exposes MCP tools that any AI client (Claude Code, Codex, Cursor, Antigravity, …) can call to getranked files, symbols, and dependency chains— optionally validated and re-ranked by a local LLM before the answer reaches your cloud AI.

Your prompt ─► ContextBridge (keyword + vector retrieval) ─► Local AI (optional: validates, re-ranks, fills gaps) ─► Your AI agent (implements, grounded in real files)

The engine isgeneric. All project-specific ranking lives in a swappableprofile plugin, so the same tool works for any codebase.

How your code flows through ContextBridge to your AI agent:

ContextBridge ships with a local dashboard for monitoring retrieval quality, index health, and config — no cloud dependency.

Overview — retrieval quality, token savings, and search-mode breakdown

Settings — tune pipeline mode, RAG weights, and model config live

Token savings — per-query breakdown of what CB delivered vs. full-file cost

📖 Before you start — read the docs.The docs/folder contains everything you need for full setup, configuration, pipeline, and profile creation. Start with docs/0. README.mdfor a guided index of all documentation.

:: 1. Install deps + build the index + scaffold config files context_bridge\setup\windows\setup_context_bridge.bat :: 2. Point the config at YOUR source folders :: edit config.hybrid.json -> settings.discovery. (replace your_backend / your_frontend) :: 3. Re-run setup to index your code context_bridge\setup\windows\setup_context_bridge.bat :: 4. Start the server + dashboard (pick Hybrid / Semantic / Keyword) context_bridge\setup\windows\1. start_Context_Bridge.bat

Mac/Linux:usecontext_bridge/setup/mac/orcontext_bridge/setup/linux/equivalents.

Setup isrerunnable and safe: it creates config/start files from the.exampletemplates only if missing (never overwrites your edits), and rebuilds the index each run. Runsetup_context_bridge.bat --forceto reset configs back to the templates.

The MCP server runs SSE by default athttp://127.0.0.1:8755/sse— point your AI client there. Stdio transport is also supported (setCONTEXT_BRIDGE_TRANSPORT=stdiobefore starting) for clients that don't support SSE; SSE is recommended since it lets multiple AI clients share one running server instead of each spawning its own process. Dashboard:http://127.0.0.1:8795. Live stats can lag up to ~15 seconds behind the latest activity, and history lists (recent events, missed files, failed queries) show the most recent 1000 entries rather than the full lifetime log — both are intentional performance tradeoffs, not data loss.

Chosen at startup (the start script picks the matching config file):

Which tools appear is controlled by config — if a tool is registered, it is safe to call.

The generic engine asks aprofilefor project-specific ranking at every step. With no profile (project_profile: "default") you get pure generic scoring.
- Copyrules/projects/example_profile.py→rules/projects/<yourapp>_profile.py
- Implement the hooks you need (every hook is optional — skipped hooks fall back to no-op)
- Activate it: setCONTEXT_BRIDGE_PROFILE=<yourapp>in your start script, orproject_profile: "<yourapp>"in your config

See docs/for extended guides on setup, pipeline, profile creation, and debug commands.

ContextBridge indexesGraphify output(graph.json,GRAPH_REPORT.md,source-files.txt,scope-summary.md,manifest.json) plus/behavior/docs — not raw source. Generate Graphify for your project, pointsettings.discovery.*at those folders, and run setup. Re-run setup after each Graphify update to refresh the index.

Configure a local model underpipeline.analysis_stage(providerollamaby default, oranthropic/openai/openrouter). When enabled, it validates and re-ranks CB results, decomposes multi-topic prompts, and triggers gap re-searches — then passes a compact, grounded result to your cloud AI. Swap models by changingmodelonly; the prompts are model-agnostic.

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Setting up with Highlight

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