jentic
MCP Serverjenticpublic

jentic tools

MCP server endpoint for agent integration

Repository Info

9
Stars
2
Forks
9
Watchers
2
Issues
Python
Language
Apache License 2.0
License

About This Server

MCP server endpoint for agent integration

Model Context Protocol (MCP) - This server can be integrated with AI applications to provide additional context and capabilities, enabling enhanced AI interactions and functionality.

Documentation

Jentic SDK & MCP Plugin [Beta] !PyPI

Jentic empowers AI-agent builders to discover and integrate external APIs and workflows rapidly—without writing or maintaining any API-specific code.

This mono-repo contains:

  • Jentic SDK – a Python library for searching, loading and executing APIs / workflows, plus helpers for turning those actions into LLM tools.
  • Jentic MCP Plugin – an MCP server that exposes the same capabilities to any MCP-compatible client (Windsurf, Claude Desktop, Cursor, …).

See the dedicated READMEs for full details:

  • python/README.md – SDK usage & API reference
  • mcp/README.md – MCP server setup & configuration

The SDK is backed by the data in the Jentic Public API repository.

Quick start

1. Install Python package

pip install jentic

2. Obtain your Agent API Key

Visit https://jentic.com/register to create an agent and copy the key.

export JENTIC_AGENT_API_KEY=<your-agent-api-key>

3. Use the SDK

import asyncio
from jentic import Jentic, SearchRequest, LoadRequest, ExecutionRequest

async def main():
    client = Jentic()

    # 1️⃣ find a capability
    results = await client.search(SearchRequest(query="send a Discord DM"))
    entity_id = search.results[0].id  # op_... or wf_...

    # 2️⃣ load details (inspect schemas / auth, see inputs for operations)
    resp = await client.load(LoadRequest(ids=[entity_id]))
    inputs = resp.tool_info[entity_id].inputs
    print (inputs)

    # 3️⃣ run it
    result = await client.execute(
        ExecutionRequest(id=entity_id, inputs={"recipient_id": "123", "content": "Hello!"})
    )
    print(result)

asyncio.run(main())

4. Integrate with your LLM agent (optional)

If you need fully-formed tool definitions for Anthropic or OpenAI models, use the runtime helpers:

from jentic.lib.agent_runtime import AgentToolManager

manager = AgentToolManager(format="anthropic")
tools   = manager.generate_tool_definitions()        # pass these to the LLM
result  = await manager.execute_tool("discord_send_message",
                                     {"recipient_id": "123", "content": "Hi"})
print(result)

Using the MCP plugin

To expose the same capabilities via MCP, follow the instructions in mcp/README.md.

uvx --from \
  git+https://github.com/jentic/jentic-sdks.git@main#subdirectory=mcp \
  mcp

Then configure your MCP-compatible client to point at the running server (see the sub-README for sample client configs).

Quick Start

1

Clone the repository

git clone https://github.com/jentic/jentic-tools
2

Install dependencies

cd jentic-tools
npm install
3

Follow the documentation

Check the repository's README.md file for specific installation and usage instructions.

Repository Details

Ownerjentic
Repojentic-tools
LanguagePython
LicenseApache License 2.0
Last fetched8/10/2025

Recommended MCP Servers

💬

Discord MCP

Enable AI assistants to seamlessly interact with Discord servers, channels, and messages.

integrationsdiscordchat
🔗

Knit MCP

Connect AI agents to 200+ SaaS applications and automate workflows.

integrationsautomationsaas
🕷️

Apify MCP Server

Deploy and interact with Apify actors for web scraping and data extraction.

apifycrawlerdata
🌐

BrowserStack MCP

BrowserStack MCP Server for automated testing across multiple browsers.

testingqabrowsers

Zapier MCP

A Zapier server that provides automation capabilities for various apps.

zapierautomation