# Jentic

Jentic MCP empowers developers to discover and integrate external APIs and workflows rapidly—without the need to write or maintain any API-specific code. By leveraging the MCP protocol and Jentic’s agentic runtime, developers can:

• Search for APIs and workflows by capability, not by vendor or implementation.
• Instantly generate integration code samples that are agnostic to specific API details.
• Avoid boilerplate and reduce maintenance by relying on standardized, declarative integration.

Installation Instructions

README: https://github.com/jentic/jentic-tools

Jentic SDK & MCP Plugin [Beta]

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).