ft_mcp
A Python MCP server implemented from scratch, providing tool execution capabilities like system time and file hashing via stdio or HTTP transport.
README
ft_mcp
A Model Context Protocol (MCP) server implemented from scratch in Python, without an MCP SDK.
MCP is an open, JSON-RPC 2.0 protocol that lets an AI host — such as Claude Desktop or the MCP Inspector — discover and invoke tools exposed by an external server over a standard transport. This project implements the server side of that protocol directly: the stdio framing, the JSON-RPC routing, the lifecycle handshake, schema validation, and the tool layer.
The implementation deliberately avoids an MCP SDK so the protocol mechanics stay explicit
rather than hidden behind a library. The core (stdio transport) depends only on the Python
standard library; aiohttp is needed only for the optional HTTP/SSE transport.
Scope
- Transports: stdio (primary); Streamable HTTP + SSE (optional).
- Lifecycle:
initialize/notifications/initializedwith protocol-version negotiation and capability advertisement. - Tools: registry,
tools/list,tools/call, JSON Schema (draft 2020-12) validation. Two reference tools:get_system_timeandcalculate_file_hash. - Additional primitives:
prompts/list,prompts/get,resources/list,resources/read. - Diagnostics: structured logging and per-method timing, written to stderr only.
Supported protocol versions: 2025-11-25 and 2025-03-26.
Requirements
Python 3.10 or newer. The stdio server needs nothing else.
Installation
git clone https://github.com/alsaeed3/ft_mcp.git
cd ft_mcp
pip install -e . # core (stdio) only
pip install -e ".[http]" # add the optional HTTP/SSE transport
pip install -e ".[dev]" # add pytest
Installation is optional; the server also runs directly from the source tree with
python -m ft_mcp.
Usage
The server reads JSON-RPC messages from stdin, writes responses to stdout, and logs to stderr.
python -m ft_mcp
A full session — handshake, tool discovery, and a tool call — driven by hand:
printf '%s\n' \
'{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-11-25"}}' \
'{"jsonrpc":"2.0","method":"notifications/initialized"}' \
'{"jsonrpc":"2.0","id":2,"method":"tools/list"}' \
'{"jsonrpc":"2.0","id":3,"method":"tools/call","params":{"name":"get_system_time","arguments":{}}}' \
| python -m ft_mcp
Connecting a host
MCP Inspector:
npx @modelcontextprotocol/inspector python -m ft_mcp
Claude Desktop — add the server to claude_desktop_config.json:
{
"mcpServers": {
"ft_mcp": {
"command": "python",
"args": ["-m", "ft_mcp"],
"cwd": "/absolute/path/to/ft_mcp"
}
}
}
Architecture
The server is organized into three layers, kept independent of one another so each can be reasoned about and tested on its own. The same router serves both transports unchanged.
┌──────────────────────────────────────────────┐
stdin ─────▶ │ Transport (ft_mcp/transport) │
│ newline-delimited framing; flush-after-write │
stdout ◀───── │ stdout carries protocol bytes only │
└───────────────────────┬──────────────────────┘
│ one UTF-8 line per message
▼
┌──────────────────────────────────────────────┐
│ JSON-RPC 2.0 router (ft_mcp/router) │
│ parse → classify → dispatch │
│ lifecycle gate; error taxonomy │
└───────────────────────┬──────────────────────┘
│ validated params
▼
┌──────────────────────────────────────────────┐
│ Business logic (ft_mcp/tools) │
│ tool registry, schemas, prompts, resources │
└──────────────────────────────────────────────┘
Two constraints shape the transport layer. First, stdout is the protocol channel, so it carries nothing but newline-terminated JSON-RPC messages, flushed after each write; all diagnostics go to stderr. Second, stdin is read without blocking the event loop, so a long-running tool does not stall message intake.
A more detailed description — the lifecycle state machine, message classification, the error taxonomy, and the security model — is in docs/ARCHITECTURE.md.
Tools
| Tool | Arguments | Description |
|---|---|---|
get_system_time |
(none) | Returns the current UTC time as an ISO 8601 string. Its schema sets additionalProperties: false so callers are not led to invent arguments. |
calculate_file_hash |
path (string), algorithm (md5 | sha1 | sha256) |
Streams a file in 64 KiB chunks and returns its hex digest. The path is canonicalized and confined to an allowed root. |
Each tool declares a JSON Schema (draft 2020-12) inputSchema. The router validates
tools/call arguments against it and returns -32602 on a mismatch before the handler
runs.
Error handling
The server distinguishes protocol errors from tool errors, because a host treats them differently.
| Category | Example | Response |
|---|---|---|
| Protocol | truncated JSON | error -32700, id: null |
| Protocol | missing "jsonrpc":"2.0" |
error -32600, id: null |
| Protocol | unknown method or tool | error -32601 |
| Protocol | schema-invalid params | error -32602 |
| Protocol | unexpected handler exception | error -32603 |
| Tool logic | hashing a missing file | success result with isError: true and an error message |
Tool failures are returned as successful responses carrying isError: true rather than
JSON-RPC errors, so the model receives the message as feedback. Application-defined codes
(for example the HTTP transport's invalid-session -32000) stay outside the reserved
-32768..-32000 range.
Security
Tool inputs are treated as untrusted. calculate_file_hash resolves paths with
os.path.realpath (so symlinks are followed to their target before the containment
check), performs a boundary-aware check against an allowed root, and opens the file with
O_NOFOLLOW to avoid a time-of-check/time-of-use swap. The HTTP transport binds to
loopback, validates the Origin header against loopback for browser clients
(DNS-rebinding protection), and echoes only the validated origin in CORS headers.
Optional HTTP/SSE transport
pip install -e ".[http]"
python -m ft_mcp --http --host 127.0.0.1 --port 8080
GET /mcp/sse opens an event stream and announces a POST endpoint; POST /mcp/messages
accepts JSON-RPC and correlates each reply back onto the matching SSE session.
Testing
pip install -e ".[dev]"
python -m pytest -q
The suite in tests/test_protocol.py runs the server as a
subprocess and asserts its responses at the protocol boundary — parse and invalid-request
errors, the initialize handshake, tools/list and tools/call, tool errors surfacing as
isError, stdout purity, and clean shutdown on EOF.
Project layout
ft_mcp/
├── __main__.py # entry point and handler wiring (stdio / --http)
├── log.py # stderr-only logger
├── transport/
│ ├── stdio.py # async stdin reader, flush-after-write writer
│ └── http.py # optional HTTP + SSE transport
├── router/
│ ├── dispatch.py # JSON-RPC parse, classify, dispatch, validation
│ ├── lifecycle.py # session state machine
│ └── errors.py # error codes and helper
└── tools/
├── registry.py # tool dataclass and registry
├── system_time.py # get_system_time
├── file_hash.py # calculate_file_hash
├── prompts.py # prompts and resources
└── telemetry.py # per-method timing → stderr
tests/test_protocol.py # protocol compliance suite
License
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.