Skip to main content

A composable, ready-to-use MCP toolkit for agents and rapid integration.

Project description

McpStore

One-stop open-source high-quality MCP service management tool, making it easy for AI Agents to use various tools

GitHub stars GitHub forks GitHub issues GitHub license PyPI version Python versions PyPI downloads

English | 简体中文

🚀 Live Demo | 📖 Documentation | 🎯 Quick Start

Quick Start

Installation

pip install mcpstore

Online Experience

Open-source Vue frontend interface, supporting intuitive MCP service management through SDK or API

image-20250721212359929

Quick start backend service:

from mcpstore import MCPStore
prod_store = MCPStore.setup_store()
prod_store.start_api_server(host='0.0.0.0', port=18200)

Intuitive Usage

store = MCPStore.setup_store()
store.for_store().add_service({"name":"mcpstore-wiki","url":"https://mcpstore.wiki/mcp"})
tools = store.for_store().list_tools()
# store.for_store().use_tool(tools[0].name, {"query":'hi!'})

LangChain Integration Example

Simple integration of mcpstore tools into LangChain Agent, here's a ready-to-run code:

from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from mcpstore import MCPStore
# ===
store = MCPStore.setup_store()
store.for_store().add_service({"name":"mcpstore-wiki","url":"https://mcpstore.wiki/mcp"})
tools = store.for_store().for_langchain().list_tools()
# ===
llm = ChatOpenAI(
    temperature=0, model="deepseek-chat",
    openai_api_key="****",
    openai_api_base="https://api.deepseek.com"
)
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are an assistant, respond with emojis"),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])
agent = create_tool_calling_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
# ===
query = "What's the weather like in Beijing?"
print(f"\n   🤔: {query}")
response = agent_executor.invoke({"input": query})
print(f"   🤖 : {response['output']}")

image-20250721212658085

Chain Call Design

MCPStore adopts chain call design, providing clear context isolation:

  • store.for_store() - Global store space
  • store.for_agent("agent_id") - Create isolated space for specified Agent

Multi-Agent Isolation

Assign dedicated toolsets for different functional Agents, actively supporting A2A protocol and quick agent card generation.

# Initialize Store
store = MCPStore.setup_store()

# Assign dedicated Wiki tools for "Knowledge Management Agent"
# This operation is performed in the private context of "knowledge" agent
agent_id1 = "my-knowledge-agent"
knowledge_agent_context = store.for_agent(agent_id1).add_service(
    {"name": "mcpstore-wiki", "url": "http://mcpstore.wiki/mcp"}
)

# Assign dedicated development tools for "Development Support Agent"
# This operation is performed in the private context of "development" agent
agent_id2 = "my-development-agent"
dev_agent_context = store.for_agent(agent_id2).add_service(
    {"name": "mcpstore-demo", "url": "http://mcpstore.wiki/mcp"}
)

# Each Agent's toolset is completely isolated without interference
knowledge_tools = store.for_agent(agent_id1).list_tools()
dev_tools = store.for_agent(agent_id2).list_tools()

Intuitively, you can use almost all functions through store.for_store() and store.for_agent("agent_id")

API Interface

Provides complete RESTful API, start web service with one command:

pip install mcpstore
mcpstore run api

Main API Endpoints

# Service Management
POST /for_store/add_service          # Add service
GET  /for_store/list_services        # Get service list
POST /for_store/delete_service       # Delete service

# Tool Operations
GET  /for_store/list_tools           # Get tool list
POST /for_store/use_tool             # Execute tool

# Monitoring & Statistics
GET  /for_store/get_stats            # System statistics
GET  /for_store/health               # Health check

Contributing

Welcome community contributions:

  • ⭐ Star the project
  • 🐛 Submit Issues to report problems
  • 🔧 Submit Pull Requests to contribute code
  • 💬 Share usage experiences and best practices

Star History

Star History Chart


McpStore is a project under frequent updates, we humbly ask for your stars and guidance

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

mcpstore-1.4.2976.tar.gz (647.0 kB view details)

Uploaded Source

Built Distribution

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

mcpstore-1.4.2976-py3-none-any.whl (743.2 kB view details)

Uploaded Python 3

File details

Details for the file mcpstore-1.4.2976.tar.gz.

File metadata

  • Download URL: mcpstore-1.4.2976.tar.gz
  • Upload date:
  • Size: 647.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.7

File hashes

Hashes for mcpstore-1.4.2976.tar.gz
Algorithm Hash digest
SHA256 add86691f8038c84feb2b070a4dbd4971fa0b05f154dae9dacf80ec944ea89db
MD5 727284843ebb10f6de4d581171a6e4d7
BLAKE2b-256 19c3c5c6cf61b8efcb7872af1c8a7ad89c92f98a4f0bc7370a194f333d6d21ee

See more details on using hashes here.

File details

Details for the file mcpstore-1.4.2976-py3-none-any.whl.

File metadata

  • Download URL: mcpstore-1.4.2976-py3-none-any.whl
  • Upload date:
  • Size: 743.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.7

File hashes

Hashes for mcpstore-1.4.2976-py3-none-any.whl
Algorithm Hash digest
SHA256 b952c895919ab42502976e14757222a5e8a2e63b4512fae847cc8b424e9c019c
MD5 bda994e67183ecc80f912fb1be976310
BLAKE2b-256 4989cc1e07186c3c595ff35f40db29fa96ee0251f10ee8bf71bdd01cfb4148b9

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