Speaker Notes

The Creators of Model Context Protocol

The creators of the Model Context Protocol explain how the open-source standard solves the N times M integration problem for AI applications by providing universal primitives for tools, resources, and prompts.

Latent Space · 1:20:21 · YouTube ↗

Podcast episode · 2 hosts + 2 guests  Intermediate · concepts and protocol architecture

Highlights

11 clips · 13 min of 80 min
    • MCP stands for Model Context Protocol, designed to extend and enhance AI applications.
    • It uses a client-server architecture acting as a universal connector, similar to a USB-C port.
    • The focus is on connecting AI applications to an ecosystem of plugins rather than modifying the underlying models.
    • The idea originated from developer frustration managing multiple tools like cloud desktop and IDEs without a unified protocol.
    • Connecting multiple applications to multiple integrations creates a classic N times M scaling problem.
    • A standardized protocol eliminates custom point-to-point integrations.
    • MCP borrows JSON-RPC and birectionality from LSP while innovating on application-specific primitives.
    • Design prioritizes how features manifest in the user interface rather than strict semantic differences.
    • Initial development required building robust SDKs across TypeScript, Python, and Rust.
    • Tool calling alone is insufficient for rich application development and agent workflows.
    • Resources provide bits of data or context that can be application-controlled or user-selected via UI affordances.
    • Prompts act as user-initiated macros or slash commands to structure context before hitting the LLM.
    • Tool results return raw text, images, or messages directly rather than strictly structured JSON schemas.
    • Returning a rich jungle of data allows LLMs to sort through and extract the exact information they need.
    • Over-specifying return formats can limit scalability as underlying LLM capabilities improve.
    • Tool results should pass through raw data directly from underlying APIs.
    • LLMs excel at sifting through unstructured information and extracting what they need.
    • Over-specifying schemas creates rigid wrappers that fail to scale as models improve.
    • OpenAPI specs are too granular and lack AI-specific primitives like resources and prompts.
    • MCP embraces statefulness to better support advanced multimodal interactions.
    • OpenAPI and MCP are ultimately complementary rather than competing standards.
    • MCP servers can simultaneously act as clients to consume other servers.
    • This architecture enables recursive DAG patterns and complex composable workflows.
    • SDKs still require additional niceties to make multi-plexing and recursive setups seamless.
    • Client applications and users must ultimately remain in full control of all MCP actions.
    • Tools are designed to be model-invoked rather than forced by rigid UI affordances.
    • Clients can inspect, filter, or enrich tool descriptions provided by servers.
    • Registries offering download counts and reviews create a false sense of security.
    • Any trusted package can be compromised in a subsequent update.
    • Using MCP Inspector to review actual traffic provides better security than relying on social proof.
    • Long-lived SSE connections are difficult to operate at massive scale.
    • Streamable HTTP starts as simple REST endpoints and gradually enhances to streaming.
    • Session resumption allows clients and servers to handle network disconnects gracefully.
    • High volumes of community discussion require scalable filtering strategies.
    • Maintainers favor contributors who write actual code and build SDK extensions.
    • Pure opinions without practical implementation work are typically ignored.

Questions this video answers

10 · every answer cites the moment it was said
01 How did Model Context Protocol originate? 2 sources · 3:55

The creators developed MCP internally at Anthropic out of frustration with siloed development tools like cloud desktop and IDEs. They borrowed architectural patterns and bi-directional communication from the Language Server Protocol to solve the M times N integration problem.

02 Why does MCP use different primitives like tools, resources, and prompts instead of just tool calling? 2 sources · 9:59

The creators designed separate primitives to give application developers specific UI affordances and control over how features manifest. While tool calling is model-driven, resources and prompts allow for user-initiated data pulling and application-controlled workflows that better fit different user experiences.

03 How should I structure tool results when building an MCP server? 2 sources · 25:50

Tool results should not be over-specified into rigid structured JSON schemas to ensure they scale as LLMs improve. Instead, servers should return raw text, images, or messages directly passed through from the underlying API. This lets the LLM sort through the data and extract what it needs, leveraging its natural strengths.

04 Why is Model Context Protocol preferred over OpenAPI specifications for AI apps? 2 sources · 29:59

OpenAPI specifications are too granular and fail to express higher-level, AI-specific concepts needed for rich LLM interactions. MCP provides purpose-built primitives like tools, resources, and prompts that models benefit from more than a raw REST API. Additionally, MCP supports stateful interactions which are increasingly important for modern multimodal AI apps.

05 How can I quickly start building my own MCP server? 2 sources · 33:34

You should pick your preferred language and its SDK, then build a simple tool for something that matters to you personally. Avoid overthinking the descriptions initially and use standard IO protocol transport to test it in an application. You can even feed the SDK code and MCP spec to an LLM to generate the server for you in about half an hour.

06 Can an MCP server also act as an MCP client? 2 sources · 41:42

Yes, MCP servers can simultaneously act as MCP clients to create recursive compositions and rich workflows. This allows developers to chain servers together into directed acyclic graphs where an MCP server can utilize the broader ecosystem of other servers. Such patterns enable modular architectures, though the SDKs still need minor niceties to make execution simpler.

07 How do you handle authentication for remote MCP servers? 2 sources · 1:04:23

The protocol uses OAuth 2.1 for user-to-server authorization, which avoids requiring users to manually paste API keys. This approach fits well into remote server architectures while keeping the specification minimal and extensible.

08 Why did the team move away from pure SSE towards a streamable HTTP transport? 2 sources · 1:02:52

While the team believes future AI applications will be stateful, requiring a long-lived persistent SSE connection creates significant operational complexity at scale. The new streamable HTTP transport combines standard HTTP requests with optional streaming and session resumption to balance statefulness and operational scalability.

09 How does the project handle open source governance and community contributions? 2 sources · 1:10:30

Governance is strictly merit-driven, meaning contributors must submit practical work and SDK contributions rather than just opinions to gain commit access. The maintainers prioritize moving fast and avoiding slow standardization bodies over traditional foundation-heavy governance models.

10 What is the security strategy regarding third-party MCP registries? 2 sources · 53:26

The creators view centralized registries as brittle because social proof and download counts do not prevent supply chain compromises in updated packages. Instead of building a heavily vetted app store, they encourage developers to use the MCP inspector tool to examine traffic and verify security.

Section-by-section summary

16 parts

Universal connector for AI applications

Model Context Protocol provides a standardized way for AI applications to integrate with external plugins and tools. Operating via a client-server architecture, it functions as a universal connector akin to a hardware USB-C port.

Solving internal developer tool frustration

The protocol originated from internal developer tooling frustrations at Anthropic when trying to bridge cloud desktop interfaces and local IDEs. Realizing that custom integrations lead to an N times M scaling problem, creators built a unified communication layer.

Borrowing architectural patterns from LSP

MCP draws heavy inspiration from the Language Server Protocol by adopting JSON-RPC and birectionality while innovating on specific primitives. Choosing non-controversial foundational tech allows developers to focus entirely on application-facing abstractions.

Expanding beyond tool calling with resources and prompts

While tool calling handles model-initiated actions, MCP introduces resources and prompts to support user-driven workflows and UI affordances. Resources expose application data via URIs, whereas prompts provide reusable text templates and command macros.

Returning unstructured data for LLM consumption

Tool results intentionally avoid rigid JSON schemas in favor of rich, unstructured text or messages. This approach leverages the model's native ability to parse and extract relevant details from large payloads without brittle over-specification.

Trust LLMs with raw data instead of rigid schemas

Software engineers must unlearn decades of traditional API design by passing raw, unformatted data directly to models. Because LLMs excel at processing unstructured information, avoiding over-specified schemas prevents scaling bottlenecks as AI models advance.

MCP addresses higher-level AI concepts than OpenAPI

OpenAPI specifications lack purpose-built AI primitives like prompts, tools, and resources, making them too granular for LLM workflows. While OpenAPI assumes statelessness, MCP supports stateful interactions to accommodate richer multi-modal use cases.

Build simple MCP servers quickly using LLMs

Developers can bootstrap functional MCP servers within minutes using their preferred language and standard I/O transport. Feeding SDK code into an LLM enables one-shot generation of working servers that can be iteratively refined.

Servers can act as clients for recursive composition

MCP servers are capable of acting as clients, allowing developers to chain services together in directed acyclic graphs. This recursive property unlocks powerful multi-step workflows and composable architectures across multiple servers.

Manage tool scale using filtering and clear naming

While models like Claude can handle hundreds of tools simultaneously, overlapping descriptions cause tool confusion and degraded results. Client applications can mitigate this by filtering tool sets or using proxy servers to narrow down relevant capabilities.

Registries introduce supply chain vulnerability risks

Centralized registries are prone to supply chain attacks where a trusted package is compromised in an update. Developers are encouraged to use inspection tools like the MCP Inspector to verify actual traffic instead of relying solely on social proof.

Official core servers solve immediate developer frustrations

Special servers like memory and filesystem originated from internal hackathons to address immediate developer pain points. The filesystem server, for instance, enables Claude to safely interact with local codebases and edit files directly.

Streamable HTTP balances statefulness with operational scale

While long-lived SSE connections are operationally difficult at scale, pure statelessness loses important agentic capabilities. The new streamable HTTP transport allows gradual enhancement from basic endpoints to full bidirectional sessions with resume support.

OAuth 2.1 specification handles remote server authorization

MCP incorporates OAuth 2.1 user-to-server authorization to replace raw API keys, especially as remote servers become more common. This maintains a minimal protocol footprint while fitting standard web authentication flows.

Open source governance requires merit-driven contribution

Maintainers prioritize contributors who provide practical examples and SDK work over those who only voice opinions. This prevents project bloat and ensures the core architecture remains stable and well-implemented.

A multi-company ecosystem drives protocol adoption

Major SDKs are actively maintained by external companies like Microsoft, JetBrains, and Spring AI, proving MCP is a true multi-company standard. Future wishlist items include robust sampling clients and deeper game engine integrations.

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k play/pause · f fullscreen · m mute
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< > slower/faster · / search what was said
  • Model Context Protocol63× · first 0:27

    A universal open-source standard designed to help AI applications extend themselves and integrate with plugins.

  • Language Server Protocol7× · first 4:08

    An architectural standard providing bi-directional communication and client-server patterns that inspired MCP.

  • OpenAPI4× · first 29:42

    An API specification format that is too granular for expressing higher-level AI specific concepts.

  • Streamable HTTP transport2× · first 1:03:15

    A transport mechanism that combines standard HTTP endpoints with optional streaming and session resumption capabilities.

  • OAuth 2.11× · first 1:04:29

    An authorization specification used in the protocol to handle secure user-to-server authentication without manual API keys.