
jentic tools
MCP server endpoint for agent integration
Repository Info
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 referencemcp/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
Clone the repository
git clone https://github.com/jentic/jentic-toolsInstall dependencies
cd jentic-tools
npm installFollow the documentation
Check the repository's README.md file for specific installation and usage instructions.
Repository Details
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