Myaiserv MCP Server

Overview

Myaiserv is a fully‑featured Model Context Protocol (MCP) server built on FastAPI that offers developers a standardized, high‑performance interface for connecting large language models (LLMs) to external tools and data sources. By implementing the core MCP concepts—resources, tools, prompts, and sampling—it removes the boilerplate that typically accompanies custom LLM integrations. The server is designed to run as a lightweight microservice, exposing REST, GraphQL, and WebSocket endpoints that can be consumed by any AI assistant capable of speaking MCP.

The server solves the problem of fragmented tool integration. In many AI workflows, developers must write custom adapters for each external service (file systems, weather APIs, text analytics, etc.), leading to duplicated effort and hard‑to‑maintain code. Myaiserv centralizes these adapters behind a single, well‑defined contract. Each tool is registered as an MCP resource, exposing its capabilities through a declarative schema. Clients can discover available tools via the endpoint, request execution with minimal payloads, and receive structured responses that can be fed back into the LLM’s context. This eliminates the need for bespoke SDKs and streamlines the onboarding of new services.

Key features include:

• High‑performance, async API: Built on FastAPI, the server handles thousands of concurrent requests with low latency, making it suitable for real‑time chat or batch processing scenarios.
• Full MCP compliance: Supports resource discovery, tool invocation, prompt templates, and sampling strategies out of the box.
• Multi‑protocol access: REST for simple CRUD, GraphQL for flexible queries, and WebSocket for streaming responses or real‑time collaboration.
• Observability: Integrated Prometheus metrics and Grafana dashboards provide visibility into request rates, latency, and error rates.
• Extensibility: Adding a new tool requires implementing a small Python class that inherits from the base MCP interface; the server automatically registers it.
• Semantic search: Optional Elasticsearch integration allows querying knowledge bases or logs with natural language queries, enriching the assistant’s context.
• Caching: Redis support reduces latency for repeated tool calls and conserves external API usage.

Real‑world use cases span from enterprise automation—where an LLM can read, write, and delete files on a corporate server—to consumer applications that fetch live weather data or perform sentiment analysis. A chatbot could, for example, list recent documents, summarize a user‑uploaded file, and provide a weather forecast—all through a single MCP conversation. Because the server exposes both REST and GraphQL, developers can choose the most convenient interface for their stack.

In practice, an AI workflow would involve deploying Myaiserv as a sidecar or standalone service. The LLM client sends a request to the endpoint, obtains the list of available operations, and then constructs an MCP invocation message. The server executes the requested tool, streams results back via WebSocket if needed, and updates the conversation context. This seamless loop allows developers to focus on higher‑level application logic while relying on Myaiserv for robust, standardized tool integration.