Skip to main content

AI-powered, safe database access layer for MCP clients (Cursor, VS Code, Claude Desktop)

Project description

Bollard MCP 🛡️

An AI-powered, safe database access gateway built on the Model Context Protocol (MCP).

Bollard acts as an intelligent execution proxy between your AI code assistant (Cursor, VS Code, Windsurf, Claude Desktop, OpenAI Codex) and your physical databases — providing safe query execution, dynamic schema discovery, cost-based risk parsing, data leak prevention, and session memory.


How It Works

Bollard sits as a transparent intermediary layer between your AI development client and your target database. When the client executes database tools, the requests are statically evaluated, authorized, and structured to optimize context window tokens before the database engine sees them.

Before Bollard vs. With Bollard

Workflow Aspect Before Bollard (Direct SQL Assistant) With Bollard (Safe Database Gateway)
Schema Context Relies on manually pasted schema blocks, leading to hallucinated queries on outdated schemas. Inspects schemas dynamically, caching metadata and profiles to feed the LLM accurate context.
Execution Safety AI directly runs generated queries. High risk of accidental data modification, deletion, or drops. Risk levels (LOW to EXTREME) are computed statically. Destructive operations are safely blocked.
Human-in-the-Loop None. Large batch updates or structural migrations execute immediately without warnings. Write queries require double confirmation (confirming query matching phrases and typing local PINs).
Data Leak Prevention AI can query any table, including sensitive tables (e.g., password hashes, user secrets, API keys). Access control lists block sensitive tables via connection-level blocklist wildcards.
Token & Context Usage Large queries return massive raw rows, flooding the context window and wasting thousands of tokens. Large queries are compressed into structured summaries with a 10-row preview and column stats (up to 97% token savings).
Correction Loop No memory of past mistakes. AI repeats the same syntax/query errors in new sessions. Custom fixes and deprecated field overrides are persisted and auto-injected as agent instructions.

Getting Started

1. Installation

Bollard is written in Python and is available on PyPI:

  • Global Installation (Recommended for CLI / standalone runtimes):
    pipx install bollard-mcp
    
  • Virtual Environment Installation:
    pip install bollard-mcp
    

2. Client Configurations

Cursor

  1. Go to Cursor Settings > Features > MCP.
  2. Click + Add New MCP Server.
  3. Set the following fields:
    • Name: bollard
    • Type: command
    • Command: bollard-mcp (or the absolute path to bollard-mcp or python inside your virtualenv, e.g., python -m bollard_mcp.server)

Or edit your Cursor config file directly (~/.cursor/mcp.json):

{
  "mcpServers": {
    "bollard": {
      "command": "bollard-mcp"
    }
  }
}

VS Code (Cline / Roo Code)

Add the configuration to your Cline settings file (located at %APPDATA%\Code\User\globalStorage\saoudrizwan.claude-dev\settings\cline_mcp_settings.json on Windows):

{
  "mcpServers": {
    "bollard": {
      "command": "bollard-mcp"
    }
  }
}

Windsurf

Add the configuration under your global Windsurf MCP configuration file (~/.codeium/windsurf/mcp_config.json):

{
  "mcpServers": {
    "bollard": {
      "command": "bollard-mcp"
    }
  }
}

Claude Desktop

Add the configuration to your Claude Desktop config file (%APPDATA%\Claude\claude_desktop_config.json on Windows):

{
  "mcpServers": {
    "bollard": {
      "command": "bollard-mcp"
    }
  }
}

OpenAI Codex

Add the server block under the [mcp_servers] table in your Codex configuration file (~/.codex/config.toml):

[mcp_servers.bollard]
command = "python" # or "bollard-mcp" if installed globally via pipx
args = ["-m", "bollard_mcp.server"]
cwd = "/path/to/your/bollard-mcp"

💡 Write Operations in Standalone Clients: In standalone clients (like OpenAI Codex or Claude Desktop) that do not run the editor helper extension, Bollard automatically falls back to an In-Chat PIN Gate. When the AI attempts a write query, the server will block execution, generate a local 4-digit verification PIN, and print it directly in the chat. You simply need to copy-paste this PIN back into the chat prompt to authorize and execute the write query safely.


Database Connection Reference

Once Bollard is registered, prompt your AI agent inside your chat client to connect.

Connection Prompts

  • General Connect:

    "Connect to my local database with alias local_postgres and connection string postgresql://postgres:postgres@localhost:5432/dbcopilot"

  • Connect with a security blocklist:

    "Connect to local database with alias local_postgres at postgresql://postgres:postgres@localhost:5432/dbcopilot and forbid access to user_secrets"

Local Docker Testing & Seeding

If you are testing database safety gates locally using a Docker PostgreSQL container:

  1. Make sure your local Docker container is running: docker ps
  2. Seed the database tables (users, orders, user_secrets) by running our seeding script:
    python examples/create_postgres_test_db.py
    
  3. Prompt the agent in chat to connect and query (e.g. query user_secrets to verify the blocklist intercepts the request).

License

Bollard is dual-licensed under:

  • AGPL-3.0-only for open-source non-commercial use. See LICENSE for details.
  • Commercial License for commercial production use. If you need custom SLAs, Okta/SSO integrations, or compliance audit log exporting, please contact sales at pavakstudio@gmail.com.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

bollard_mcp-0.1.2.tar.gz (50.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

bollard_mcp-0.1.2-py3-none-any.whl (58.4 kB view details)

Uploaded Python 3

File details

Details for the file bollard_mcp-0.1.2.tar.gz.

File metadata

  • Download URL: bollard_mcp-0.1.2.tar.gz
  • Upload date:
  • Size: 50.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.10

File hashes

Hashes for bollard_mcp-0.1.2.tar.gz
Algorithm Hash digest
SHA256 9c3223d2697c6fbff485c799518b9b86e3b46455719926fc2831addd4fb6bbb2
MD5 9c1d87f7ac29fe8a5362b95c4043dec9
BLAKE2b-256 059b9a6e0aa6b3915ec2d83f5fe3a64748d751a4eb9b3e2b6341cccae0577968

See more details on using hashes here.

File details

Details for the file bollard_mcp-0.1.2-py3-none-any.whl.

File metadata

  • Download URL: bollard_mcp-0.1.2-py3-none-any.whl
  • Upload date:
  • Size: 58.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.10

File hashes

Hashes for bollard_mcp-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 4354aeb2f79c044ee211c0415e3e5603adebbab878a761c948f423c7b4d55f18
MD5 336b0e58ce3a1b0ae142bee22966fb85
BLAKE2b-256 b5d120bac40f4470b4b207926ac38018803476b75fe9be1a03751c183bb927d4

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page