MCP Server TESS

Overview

The MCP‑Server‑TESS is a dedicated Model Context Protocol server that bridges conversational AI assistants with the TESS platform. By exposing a suite of HTTP‑based tools, it lets developers retrieve and manipulate TESS agents, files, and executions directly from within an LLM’s workflow. This removes the need for custom integration code on the client side, allowing AI models to treat TESS as a first‑class data source and action engine.

The server solves the problem of fragmented tool access: many organizations have a TESS instance running on-premises or in the cloud, but their LLMs cannot natively call its REST API. MCP‑Server‑TESS implements the standard MCP contract, so any LLM that understands MCP can enumerate its capabilities (), check health (), and invoke tools such as . Developers can therefore build end‑to‑end solutions where the model selects an agent, supplies context messages, and receives the execution result—all without writing custom adapters.

Key features include:

• Agent management: list all agents, fetch details, and execute them with custom message payloads.
• File handling: list, upload, download, and delete files in TESS; associate or disassociate files with specific agents.
• Tool discovery: the endpoint provides a machine‑readable inventory of all available operations, enabling dynamic UI generation or automated tool selection by the model.
• Secure configuration: a single environment variable () protects all requests, and the server can be run locally or via Docker for quick prototyping.

Real‑world use cases span across automated customer support, data labeling pipelines, and knowledge base updates. For example, a chatbot can query TESS for the latest policy documents (via ), attach them to a conversation, and then trigger an agent that summarizes the content. In a data‑science workflow, a model can orchestrate TESS agents to preprocess datasets and then retrieve the processed files for further analysis.

Integration with AI workflows is straightforward: once the MCP server is registered in a platform like Smithery.ai, any compatible LLM can automatically expose all TESS tools as callable actions. The model’s prompt can include a directive such as “use the tool to run agent X with these messages,” and the MCP middleware will translate that into an HTTP request, stream the response back to the model, and close the loop. This tight coupling enables developers to compose complex pipelines—fetch data → process with TESS agents → feed results back into the conversation—without leaving the LLM environment.

In summary, MCP‑Server‑TESS offers a turnkey, protocol‑compliant gateway that transforms TESS into an AI‑friendly service. It abstracts away API intricacies, provides a rich set of tools for agent and file management, and fits seamlessly into modern LLM pipelines, giving developers a powerful yet simple way to extend AI assistants with external business logic.