Runware MCP Server

Runware MCP Server is a lightweight, production‑ready implementation of the Model Context Protocol (MCP) that lets AI assistants such as Claude connect to external services, data stores, and custom tooling. The core problem it addresses is the disconnect between an AI’s conversational logic and the real‑world data or actions that a developer needs to access. By exposing a unified MCP endpoint, the server lets assistants query databases, trigger workflows, and retrieve contextual information without embedding proprietary logic into the model itself.

At its heart, the server implements the MCP specification for resources, tools, prompts, and sampling. It serves a catalog of resources—structured data objects that can be queried or updated—and tools, which are callable functions that perform side‑effects like sending emails, invoking third‑party APIs, or running internal scripts. The server also hosts prompts that can be injected into the model’s context to steer its behavior, and it supports sampling controls so developers can fine‑tune response length or creativity. All of these capabilities are accessible through a simple HTTP API, allowing any MCP‑compliant client to discover and interact with the server’s services in a standardized way.

Key features include:

• Declarative resource schema: Define tables or documents with type information, enabling the assistant to perform typed queries and receive structured results.
• Tool execution sandbox: Safely run arbitrary functions with controlled input/output, making it possible to perform actions like updating a CRM record or posting a tweet directly from the assistant.
• Prompt injection: Store reusable prompt fragments that can be dynamically composed, ensuring consistent tone or domain knowledge across sessions.
• Sampling configuration: Expose temperature, top‑p, and max tokens settings to the client, giving developers precise control over response variability.
• Security & authentication: Integrate with OAuth or API keys to protect resources and restrict tool usage to authorized users.

Real‑world use cases span from customer support bots that pull ticket data and close issues to internal productivity assistants that schedule meetings, generate reports, or trigger CI/CD pipelines. In each scenario the MCP server acts as a bridge: the AI assistant receives user intent, queries the Runware MCP Server for relevant data or actions, and returns a coherent, context‑aware response. Because the server follows the MCP spec, developers can swap in different assistants or update underlying tools without changing client code, making it a flexible backbone for AI‑powered workflows.