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Symfony MCP Server

MCP Server

Build AI agents directly in your Symfony applications

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Updated 11 days ago

About

A Symfony package that implements the Model Context Protocol, enabling developers to create intelligent, context-aware AI agents. It provides tools, prompts, resources, sampling, and real‑time streaming for secure, scalable AI integration.

Capabilities

Resources
Access data sources
Tools
Execute functions
Prompts
Pre-built templates
Sampling
AI model interactions

Overview

Symfony MCP Server is a dedicated Symfony package that turns any Symfony application into a fully‑featured Model Context Protocol (MCP) server. By exposing the MCP interface, your application can host intelligent AI agents that reason, make decisions, and interact with business logic through a standardized protocol. This capability eliminates the need for custom integration layers between large language models (LLMs) and your services, allowing developers to focus on business value rather than plumbing.

The server solves a common pain point for AI‑enabled applications: seamless, secure, and scalable communication between an LLM and application logic. Traditional approaches often rely on ad‑hoc HTTP endpoints, STDIO pipes, or proprietary SDKs, which can be fragile and hard to maintain. Symfony MCP Server leverages StreamableHTTP and Server‑Sent Events (SSE) for production‑ready transports, ensuring that even long‑running agent tasks can stream progress and results back to the client without blocking resources. The built‑in authentication, authorization, and fine‑grained access controls keep internal systems safe while still exposing powerful AI capabilities.

Key features are delivered through a clean, adapter‑based architecture that supports tools, prompts, resources, sampling, and progress streaming:

  • Tools – Define executable functions that LLMs can invoke. Each tool encapsulates a specific piece of business logic, such as querying a database or sending an email.
  • Prompts – Pre‑configured conversation starters and templates guide the LLM’s behavior, ensuring consistent tone and context.
  • Resources – Structured data or documents are exposed to the agent, enabling it to read and reason over domain knowledge.
  • Sampling – During tool execution, the agent can consult an LLM to make real‑time decisions or refine its approach.
  • Progress Streaming – Long tasks emit incremental updates via SSE, giving users instant feedback on status and intermediate results.
  • Multi‑Modal Results – Tools may return text, images, audio, or structured resources, allowing agents to produce rich outputs.

In practice, the server empowers developers to build agent‑first workflows. For example, a support ticket system can expose a “Create Ticket” tool; an agent, guided by prompts, can ask clarifying questions, validate input via resources, and submit the ticket while streaming progress to the user. Another scenario is an e‑commerce recommendation engine where a sampling‑enabled tool queries product data, consults the LLM for personalization, and streams personalized suggestions in real time.

What sets Symfony MCP Server apart is its enterprise‑grade security model and scalable architecture. By default, all communication is protected, and the Pub/Sub messaging pattern allows horizontal scaling of agent instances without coupling to a single process. This makes it ideal for organizations that need robust, auditable AI interactions while maintaining strict compliance requirements.

Overall, Symfony MCP Server provides a turnkey, standards‑compliant foundation for building sophisticated AI agents that can interact naturally with Symfony applications, enabling developers to accelerate innovation while keeping control over data, security, and performance.