Datascript

The Datascript MCP is a Clojure-based MCP server that exposes a local DataScript database to any MCP client.

It acts as a translator that lets your AI assistants run complex queries against your in-memory DataScript data.

This can be useful for AI-driven workflows where you need to ground a model’s responses with specific, structured data you have on your machine.

Features

  • 🕸️ Graph Visualization: Search graph paths and visualize the results by connecting to a visualization server.
  • 📂 Hierarchical Queries: Easily query parent-child relationships within your datasets.
  • 📊 On-the-fly Computations: Load example data and calculate totals directly through queries.
  • 🧾 Entity Inspection: Retrieve specific properties from any entity in your database.

Use Cases

  • AI-Assisted Data Analysis: Pose natural language questions to an AI client about a local DataScript database. For instance, you could ask, “Show all active projects and their due dates,” and get a structured answer directly from your data.
  • Interactive Knowledge Graphs: Model a personal knowledge base in DataScript and use this server with a visualization tool. This setup lets you visually explore connections and relationships between your notes and ideas.
  • Rapid Prototyping: Quickly test application ideas that depend on complex data queries without the overhead of a full database and API stack. It’s an effective way to work with graph-like data on your local machine.

How to Use It

1. To get started, make sure you have the Clojure command-line interface installed.

2. Run the MCP server:

clojure -X:mcp

3. If you are working from an Emacs REPL, you can start the server with:

(mcp-server)

4. You can check if the server is running correctly by sending it a JSON-RPC request. This command pipes an initialize message to the server, which communicates over standard input and output (STDIO).

echo '{"jsonrpc": "2.0", "id": 1, "method": "initialize", "params": {"protocolVersion": "2024-11-05", "capabilities": {}, "clientInfo": {"name": "test", "version": "1.0"}}}' \
| /usr/local/bin/clojure -M -m datascript-mcp.core

A successful response confirms the server is active and returns its capabilities.

5. To connect a MCP client like Claude Desktop, you need to add a configuration that tells the application how to launch the server. You can do this by editing the claude_desktop_config.json file.

Here is an example configuration:

{
  "datascript-mcp": {
    "command": "/bin/bash",
    "args": [
      "-c",
      "cd /Users/clojure/Desktop/datascript-mcp-server && /usr/local/bin/clojure -M -m datascript-mcp.core"
    ]
  }
}

This JSON object instructs Claude Desktop to change to the server’s directory and then execute the launch command. After restarting the desktop application, the server’s tools should become available.

FAQs

Q: Does the server store my data permanently?
A: No, the Datascript MCP Server operates on an in-memory DataScript database. Data must be loaded for each session.

Q: What does “STDIO transport” mean?
A: It means the server communicates using Standard Input (STDIN) for receiving commands and Standard Output (STDOUT) for sending responses. This is a straightforward method for local inter-process communication.

Q: Can this be used with clients other than Claude Desktop?
A: Yes. Any MCP client that can execute a shell command and communicate over STDIN/STDOUT using the JSON-RPC format should be compatible.

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FAQs

Q: What exactly is the Model Context Protocol (MCP)?

A: MCP is an open standard, like a common language, that lets AI applications (clients) and external data sources or tools (servers) talk to each other. It helps AI models get the context (data, instructions, tools) they need from outside systems to give more accurate and relevant responses. Think of it as a universal adapter for AI connections.

Q: How is MCP different from OpenAI's function calling or plugins?

A: While OpenAI's tools allow models to use specific external functions, MCP is a broader, open standard. It covers not just tool use, but also providing structured data (Resources) and instruction templates (Prompts) as context. Being an open standard means it's not tied to one company's models or platform. OpenAI has even started adopting MCP in its Agents SDK.

Q: Can I use MCP with frameworks like LangChain?

A: Yes, MCP is designed to complement frameworks like LangChain or LlamaIndex. Instead of relying solely on custom connectors within these frameworks, you can use MCP as a standardized bridge to connect to various tools and data sources. There's potential for interoperability, like converting MCP tools into LangChain tools.

Q: Why was MCP created? What problem does it solve?

A: It was created because large language models often lack real-time information and connecting them to external data/tools required custom, complex integrations for each pair. MCP solves this by providing a standard way to connect, reducing development time, complexity, and cost, and enabling better interoperability between different AI models and tools.

Q: Is MCP secure? What are the main risks?

A: Security is a major consideration. While MCP includes principles like user consent and control, risks exist. These include potential server compromises leading to token theft, indirect prompt injection attacks, excessive permissions, context data leakage, session hijacking, and vulnerabilities in server implementations. Implementing robust security measures like OAuth 2.1, TLS, strict permissions, and monitoring is crucial.

Q: Who is behind MCP?

A: MCP was initially developed and open-sourced by Anthropic. However, it's an open standard with active contributions from the community, including companies like Microsoft and VMware Tanzu who maintain official SDKs.

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