mcp-toolkit-server

A custom Model Context Protocol (MCP) server exposing a small, composable registry of tools, resources, and prompts — the pattern behind "wrap it once, every agent gets access" enterprise tool integration.

Why this exists

MCP is the standardized layer that lets an agent framework (LangGraph, Claude Agent SDK, a custom orchestrator) discover and call tools without bespoke integration code per agent. I've built MCP server implementations against internal enterprise systems (knowledge bases, policy document APIs, compliance tools) in production; this project is a small, self-contained MCP server built from scratch to show the same pattern — a tool/resource/prompt registry with real JSON Schema contracts — in a form that's inspectable end to end.

Related MCP server: docsray-mcp

What it exposes

MCP defines three primitive types. This server implements all three:

Installation

git clone https://github.com/varunram3232-glitch/mcp-toolkit-server.git
cd mcp-toolkit-server
python3 -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

Running the server

Over stdio (the transport Claude Desktop and most local MCP clients use):

mcp-toolkit-server

With the MCP Inspector, for interactive development:

mcp dev src/mcp_toolkit/server.py

Connecting it to Claude Desktop — add to your claude_desktop_config.json:

{
  "mcpServers": {
    "toolkit": {
      "command": "/absolute/path/to/.venv/bin/mcp-toolkit-server"
    }
  }
}

Example: calling it programmatically

Tools are callable through the FastMCP server object directly (useful for testing, or for embedding this server's logic in another Python process without a subprocess transport):

import asyncio
from mcp_toolkit.server import mcp

async def main():
    result = await mcp.call_tool("calculate", {"expression": "2 * (3 + 4) / 7"})
    print(result[0].text)  # "2.0"

    docs = await mcp.call_tool("search_knowledge_base", {"query": "tool schema"})
    print(docs[0].text)

asyncio.run(main())

Design decisions

• A real AST walk for calculate, never eval. Tool arguments come from a language model's interpretation of a user prompt — treating that as trusted input to eval() is a textbook injection risk. calculator.py parses the expression into an AST and only evaluates a fixed whitelist of numeric operators; anything else (__import__, attribute access, comprehensions, name lookups) is rejected before it ever executes.

• The description field is the real interface. A tool's JSON Schema tells a model what arguments are valid; the natural-language description is what tells it when to call the tool at all. Every tool and the server's top-level instructions are written to be specific about that ("use calculate instead of computing it yourself") rather than a generic one-liner.

• Resources vs. tools, used for what each is for. The knowledge base is exposed as a resource (kb://document/{name}) so a client can attach a specific document to context deliberately (like a file picker), separately from search_knowledge_base, which is a tool the model decides to invoke based on the conversation. Collapsing these into one mechanism is a common MCP design mistake this repo deliberately avoids.

• Dependency-free knowledge base. Search here is keyword overlap, not embeddings — this repo is about the MCP server/tool-registry pattern, not retrieval quality. See agentic-rag-assistant for a real embedding-based RAG pipeline that a production version of this tool would call into.

Testing

pip install -e ".[dev]"
pytest -v
ruff check src tests

39 tests, split across two layers:

• Unit tests for the pure logic (test_calculator.py, test_knowledge_base.py, test_text_stats.py) — including a dedicated set of injection-attempt expressions (__import__, open(...), list comprehensions) that the calculator must reject.

• Protocol-level integration tests (test_server_integration.py) that call the real FastMCP server object's list_tools / call_tool / list_resources / read_resource / list_prompts / get_prompt — verifying the MCP contract itself, not just the functions behind it.

Project structure

src/mcp_toolkit/
├── server.py              # FastMCP instance — tool/resource/prompt registration
├── tools/
│   ├── calculator.py        # AST-walking safe expression evaluator
│   ├── knowledge_base.py    # In-memory document store + keyword search
│   └── text_stats.py        # Text analysis
└── data/                    # Bundled knowledge-base documents

Roadmap

• Streamable HTTP transport for remote deployment

• Auth middleware example (API key / OAuth) for a non-stdio deployment

• A tool that calls out to agentic-rag-assistant for embedding-based search

License

MIT — see LICENSE.