https://github.com/tadata-org/fastapi_mcp A zero-configuration tool for automatically exposing FastAPI endpoints as Model Context Protocol (MCP) tools: Exposing FastAPI Endpoints as MCP Tools: - Automatically converts existing FastAPI endpoints into MCP-compatible tools.

    Preserves request and response schemas, ensuring seamless interaction between APIs and AI systems.

  • Documentation and Schema Preservation: Maintains Swagger/OpenAPI documentation for endpoints, making it easier for developers and AI agents to understand and utilize the APIs.

  • Flexible Deployment: Allows mounting the MCP server directly within the FastAPI app or deploying it as a standalone service, catering to different architectural needs.

  • AI Application Development: Facilitates the creation of AI applications that require structured API interactions, such as conversational documentation, internal automation, and data querying agents2.

  • Multi-Agent Orchestration: Supports collaboration between AI agents across services using standardized APIs.

Think of how websites use a common standard for authentication. For example, by visiting a URL like https://{website_url}/.well-known/openid-configuration you instantly receive a host of login and security details—the magic behind the OpenID Connect Discovery mechanism.

Now, imagine a similar approach for AI agents. Instead of authentication details, you’d check https://{website_url}/.well-known/agents.json and find a list of available AI agents, along with what they can do. This means your AI agent could simply visit that endpoint on any site and immediately understand which other agents are ready to work together.

While no one is publicly exposing this metadata yet, Google has rolled out a Python framework that supports this exact protocol. It’s a practical step toward a future where AI agents can be easily discovered and communicate with each other—no more one-off integrations or guesswork.

For anyone building AI agents behind the scenes, this is a turning point. It makes it possible to publicly share and integrate your agents, paving the way for broader, more dynamic collaboration across different services.

MCP and A2A protocols are, in my opinion, equally important as AI Agents !

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Sam Altman in a recent Reddit AMA emphasized the popularity of AI Agents.
(Learn more here: https://lnkd.in/gzTFADZM)

These AI Agents can clearly accelerate the development of different fields.

However, it is certain that their architecture will greatly differ from our current architecture.

Though we cannot exactly predict the future architecture.

Here is my take on a possible architectural change within AI agents:

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1️⃣ Input Layer Sophistication:

  • Multimodal data processing (images, video, text)
  • Real-time data integration capabilities
  • Dynamic user feedback loops
  • Adaptive data handling mechanisms

2️⃣ Agent Orchestration Excellence:

  • Dynamic task allocation for optimal resource usage
  • Sophisticated inter-agent communication protocols
  • Advanced monitoring and observability features
  • Real-time performance optimization

3️⃣ AI Agents Core Capabilities:

  • Strategic planning and decision-making
  • Self-reflection and improvement mechanisms
  • Intelligent tool selection and utilization
  • Continuous learning loops for perpetual enhancement
  • Multiple specialized models working in harmony (Model 1 .. Model X)

4️⃣ Data Architecture Innovation:

  • Unified storage for structured and unstructured data
  • Advanced vector stores for efficient retrieval
  • Knowledge graphs for complex relationship mapping
  • Scalable and adaptable data management

5️⃣ Output Layer Sophistication:

  • Customizable output formats
  • Multi-channel delivery systems
  • Automated insight generation
  • Adaptive response mechanisms

📌 What truly sets this architecture apart is its focus on:

  1. Safety & Control: Ensuring reliable and secure operations
  2. Ethics & Responsible AI: Building trust through ethical principles
  3. Regulatory Compliance: Futureproofing against evolving regulations
  4. Interoperability: Seamless integration capabilities
  5. Versioning & Evolution: Systematic improvement tracking
  6. Human-AI Collaboration: Maintaining human-centric development

PhD Students – Use these 4 AI tools to 10x your research progress.

Researcher.Life offers a package of AI tools that covers almost every phase of your research.

Link: https://bit.ly/3JPx9Lq

Although the pack has several tools, the following 4 are my favorite.

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R Discovery Tutorial: https://lnkd.in/gURqGAUw

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-     AI writing tool specifically designed for scientific writing.
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How can it help you?

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How can it help you?

-     Easily draw figures for your papers/presentations/graphical abstracts.
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