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AI research and products that put safety at the frontier

AI will have a vast impact on the world. Anthropic is a public benefit corporation dedicated to securing its benefits and mitigating its risks.

Project Glasswing

Securing critical software for the AI era

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Latest releases

Claude Opus 4.7

Introducing a smarter, more capable Opus for coding, agents, vision, and complex professional work. 

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  • Date
    April 16, 2026
  • Category
    Announcements
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Claude is a space to think

No ads. No sponsored content. Just genuinely helpful conversations.

  • Date
    February 4, 2026
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    Announcements
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Claude on Mars

The first AI-assisted drive on another planet. Claude helped NASA’s Perseverance rover travel four hundred meters on Mars.

  • Date
    January 30, 2026
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At Anthropic, we build AI to serve humanity’s long-term well-being.

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  • Core views on AI safety

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  • Anthropic’s Responsible Scaling Policy

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  • Anthropic Academy: Build and Learn with Claude

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  • Anthropic’s Economic Index

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+ + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs_intro.txt b/docs_intro.txt new file mode 100644 index 0000000..990d20b --- /dev/null +++ b/docs_intro.txt @@ -0,0 +1,472 @@ +What is the Model Context Protocol (MCP)? - Model Context Protocol
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Documentation Index

Fetch the complete documentation index at: https://modelcontextprotocol.io/llms.txt

Use this file to discover all available pages before exploring further.

MCP (Model Context Protocol) is an open-source standard for connecting AI applications to external systems. +Using MCP, AI applications like Claude or ChatGPT can connect to data sources (e.g. local files, databases), tools (e.g. search engines, calculators) and workflows (e.g. specialized prompts)—enabling them to access key information and perform tasks. +Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect electronic devices, MCP provides a standardized way to connect AI applications to external systems. +
+

​
What can MCP enable?

+
    +
  • Agents can access your Google Calendar and Notion, acting as a more personalized AI assistant.
    • +
  • Claude Code can generate an entire web app using a Figma design.
    • +
  • Enterprise chatbots can connect to multiple databases across an organization, empowering users to analyze data using chat.
    • +
  • AI models can create 3D designs on Blender and print them out using a 3D printer.
    • +
+

​
Why does MCP matter?

+Depending on where you sit in the ecosystem, MCP can have a range of benefits. +
    +
  • Developers: MCP reduces development time and complexity when building, or integrating with, an AI application or agent.
    • +
  • AI applications or agents: MCP provides access to an ecosystem of data sources, tools and apps which will enhance capabilities and improve the end-user experience.
    • +
  • End-users: MCP results in more capable AI applications or agents which can access your data and take actions on your behalf when necessary.
    • +
+

​
Broad ecosystem support

+MCP is an open protocol supported across a wide range of clients and servers. AI assistants like Claude and ChatGPT, development tools like Visual Studio Code, Cursor, MCPJam, and many others all support MCP — making it easy to build once and integrate everywhere. +

​
Start Building

+

Build servers

Create MCP servers to expose your data and tools

Build clients

Develop applications that connect to MCP servers

Build MCP Apps

Build interactive apps that run inside AI clients
+

​
Learn more

+

Understand concepts

Learn the core concepts and architecture of MCP
\ No newline at end of file diff --git a/profile/README.md b/profile/README.md index 10daaee..bfb6eea 100644 --- a/profile/README.md +++ b/profile/README.md @@ -13,8 +13,8 @@

- Documentation | - Specification | + Documentation | + Specification | Discussions

@@ -22,8 +22,8 @@ The Model Context Protocol (MCP) is an open protocol that enables seamless integ ## Getting Started -- 📚 Read the [Documentation](https://modelcontextprotocol.io) for guides and tutorials -- 🔍 Review the [Specification](https://spec.modelcontextprotocol.io) for protocol details +- 📚 Read the [Documentation](https://modelcontextprotocol.io/docs/getting-started/intro) for guides and tutorials +- 🔍 Review the [Specification](https://modelcontextprotocol.io/specification) for protocol details - 💻 Use our SDKs to start building: - [TypeScript SDK](https://github.com/modelcontextprotocol/typescript-sdk) - [Python SDK](https://github.com/modelcontextprotocol/python-sdk) @@ -51,4 +51,4 @@ Have questions? Join the discussion in our [community forum](https://github.com/ ## About -The Model Context Protocol is an open source project run by [Anthropic, PBC.](https://anthropic.com) and open to contributions from the entire community. +The Model Context Protocol is an open source project run by [Anthropic, PBC.](https://www.anthropic.com/) and open to contributions from the entire community. diff --git a/spec_output.txt b/spec_output.txt new file mode 100644 index 0000000..1fdf489 --- /dev/null +++ b/spec_output.txt @@ -0,0 +1,576 @@ +Specification - Model Context Protocol
Skip to main content

Documentation Index

Fetch the complete documentation index at: https://modelcontextprotocol.io/llms.txt

Use this file to discover all available pages before exploring further.

+Model Context Protocol (MCP) is an open protocol that +enables seamless integration between LLM applications and external data sources and +tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating +custom AI workflows, MCP provides a standardized way to connect LLMs with the context +they need. +This specification defines the authoritative protocol requirements, based on the +TypeScript schema in +schema.ts. +For implementation guides and examples, visit +modelcontextprotocol.io. +The key words “MUST”, “MUST NOT”, “REQUIRED”, “SHALL”, “SHALL NOT”, “SHOULD”, “SHOULD +NOT”, “RECOMMENDED”, “NOT RECOMMENDED”, “MAY”, and “OPTIONAL” in this document are to be +interpreted as described in BCP 14 +[RFC2119] +[RFC8174] when, and only when, they +appear in all capitals, as shown here. +

​
Overview

+MCP provides a standardized way for applications to: +
    +
  • Share contextual information with language models
    • +
  • Expose tools and capabilities to AI systems
    • +
  • Build composable integrations and workflows
    • +
+The protocol uses JSON-RPC 2.0 messages to establish +communication between: +
    +
  • Hosts: LLM applications that initiate connections
    • +
  • Clients: Connectors within the host application
    • +
  • Servers: Services that provide context and capabilities
    • +
+MCP takes some inspiration from the +Language Server Protocol, which +standardizes how to add support for programming languages across a whole ecosystem of +development tools. In a similar way, MCP standardizes how to integrate additional context +and tools into the ecosystem of AI applications. +

​
Key Details

+

​
Base Protocol

+
    +
  • JSON-RPC message format
    • +
  • Stateful connections
    • +
  • Server and client capability negotiation
    • +
+

​
Features

+Servers offer any of the following features to clients: +
    +
  • Resources: Context and data, for the user or the AI model to use
    • +
  • Prompts: Templated messages and workflows for users
    • +
  • Tools: Functions for the AI model to execute
    • +
+Clients may offer the following features to servers: +
    +
  • Sampling: Server-initiated agentic behaviors and recursive LLM interactions
    • +
  • Roots: Server-initiated inquiries into URI or filesystem boundaries to operate in
    • +
  • Elicitation: Server-initiated requests for additional information from users
    • +
+

​
Additional Utilities

+
    +
  • Configuration
    • +
  • Progress tracking
    • +
  • Cancellation
    • +
  • Error reporting
    • +
  • Logging
    • +
+

​
Security and Trust & Safety

+The Model Context Protocol enables powerful capabilities through arbitrary data access +and code execution paths. With this power comes important security and trust +considerations that all implementors must carefully address. +

​
Key Principles

+
    +
  1. +User Consent and Control +
      +
    • Users must explicitly consent to and understand all data access and operations
      • +
    • Users must retain control over what data is shared and what actions are taken
      • +
    • Implementors should provide clear UIs for reviewing and authorizing activities
      • +
    +
    2. +
  2. +Data Privacy +
      +
    • Hosts must obtain explicit user consent before exposing user data to servers
      • +
    • Hosts must not transmit resource data elsewhere without user consent
      • +
    • User data should be protected with appropriate access controls
      • +
    +
    3. +
  3. +Tool Safety +
      +
    • Tools represent arbitrary code execution and must be treated with appropriate +caution. +
        +
      • In particular, descriptions of tool behavior such as annotations should be +considered untrusted, unless obtained from a trusted server.
        • +
      +
      • +
    • Hosts must obtain explicit user consent before invoking any tool
      • +
    • Users should understand what each tool does before authorizing its use
      • +
    +
    4. +
  4. +LLM Sampling Controls +
      +
    • Users must explicitly approve any LLM sampling requests
      • +
    • Users should control: +
        +
      • Whether sampling occurs at all
        • +
      • The actual prompt that will be sent
        • +
      • What results the server can see
        • +
      +
      • +
    • The protocol intentionally limits server visibility into prompts
      • +
    +
    5. +
+

​
Implementation Guidelines

+While MCP itself cannot enforce these security principles at the protocol level, +implementors SHOULD: +
    +
  1. Build robust consent and authorization flows into their applications
    2. +
  2. Provide clear documentation of security implications
    3. +
  3. Implement appropriate access controls and data protections
    4. +
  4. Follow security best practices in their integrations
    5. +
  5. Consider privacy implications in their feature designs
    6. +
+

​
Learn More

+Explore the detailed specification for each protocol component: +

Architecture

Base Protocol

Server Features

Client Features

Contributing

\ No newline at end of file diff --git a/spec_page.txt b/spec_page.txt new file mode 100644 index 0000000..1fdf489 --- /dev/null +++ b/spec_page.txt @@ -0,0 +1,576 @@ +Specification - Model Context Protocol
Skip to main content

Documentation Index

Fetch the complete documentation index at: https://modelcontextprotocol.io/llms.txt

Use this file to discover all available pages before exploring further.

+Model Context Protocol (MCP) is an open protocol that +enables seamless integration between LLM applications and external data sources and +tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating +custom AI workflows, MCP provides a standardized way to connect LLMs with the context +they need. +This specification defines the authoritative protocol requirements, based on the +TypeScript schema in +schema.ts. +For implementation guides and examples, visit +modelcontextprotocol.io. +The key words “MUST”, “MUST NOT”, “REQUIRED”, “SHALL”, “SHALL NOT”, “SHOULD”, “SHOULD +NOT”, “RECOMMENDED”, “NOT RECOMMENDED”, “MAY”, and “OPTIONAL” in this document are to be +interpreted as described in BCP 14 +[RFC2119] +[RFC8174] when, and only when, they +appear in all capitals, as shown here. +

​
Overview

+MCP provides a standardized way for applications to: +
    +
  • Share contextual information with language models
    • +
  • Expose tools and capabilities to AI systems
    • +
  • Build composable integrations and workflows
    • +
+The protocol uses JSON-RPC 2.0 messages to establish +communication between: +
    +
  • Hosts: LLM applications that initiate connections
    • +
  • Clients: Connectors within the host application
    • +
  • Servers: Services that provide context and capabilities
    • +
+MCP takes some inspiration from the +Language Server Protocol, which +standardizes how to add support for programming languages across a whole ecosystem of +development tools. In a similar way, MCP standardizes how to integrate additional context +and tools into the ecosystem of AI applications. +

​
Key Details

+

​
Base Protocol

+
    +
  • JSON-RPC message format
    • +
  • Stateful connections
    • +
  • Server and client capability negotiation
    • +
+

​
Features

+Servers offer any of the following features to clients: +
    +
  • Resources: Context and data, for the user or the AI model to use
    • +
  • Prompts: Templated messages and workflows for users
    • +
  • Tools: Functions for the AI model to execute
    • +
+Clients may offer the following features to servers: +
    +
  • Sampling: Server-initiated agentic behaviors and recursive LLM interactions
    • +
  • Roots: Server-initiated inquiries into URI or filesystem boundaries to operate in
    • +
  • Elicitation: Server-initiated requests for additional information from users
    • +
+

​
Additional Utilities

+
    +
  • Configuration
    • +
  • Progress tracking
    • +
  • Cancellation
    • +
  • Error reporting
    • +
  • Logging
    • +
+

​
Security and Trust & Safety

+The Model Context Protocol enables powerful capabilities through arbitrary data access +and code execution paths. With this power comes important security and trust +considerations that all implementors must carefully address. +

​
Key Principles

+
    +
  1. +User Consent and Control +
      +
    • Users must explicitly consent to and understand all data access and operations
      • +
    • Users must retain control over what data is shared and what actions are taken
      • +
    • Implementors should provide clear UIs for reviewing and authorizing activities
      • +
    +
    2. +
  2. +Data Privacy +
      +
    • Hosts must obtain explicit user consent before exposing user data to servers
      • +
    • Hosts must not transmit resource data elsewhere without user consent
      • +
    • User data should be protected with appropriate access controls
      • +
    +
    3. +
  3. +Tool Safety +
      +
    • Tools represent arbitrary code execution and must be treated with appropriate +caution. +
        +
      • In particular, descriptions of tool behavior such as annotations should be +considered untrusted, unless obtained from a trusted server.
        • +
      +
      • +
    • Hosts must obtain explicit user consent before invoking any tool
      • +
    • Users should understand what each tool does before authorizing its use
      • +
    +
    4. +
  4. +LLM Sampling Controls +
      +
    • Users must explicitly approve any LLM sampling requests
      • +
    • Users should control: +
        +
      • Whether sampling occurs at all
        • +
      • The actual prompt that will be sent
        • +
      • What results the server can see
        • +
      +
      • +
    • The protocol intentionally limits server visibility into prompts
      • +
    +
    5. +
+

​
Implementation Guidelines

+While MCP itself cannot enforce these security principles at the protocol level, +implementors SHOULD: +
    +
  1. Build robust consent and authorization flows into their applications
    2. +
  2. Provide clear documentation of security implications
    3. +
  3. Implement appropriate access controls and data protections
    4. +
  4. Follow security best practices in their integrations
    5. +
  5. Consider privacy implications in their feature designs
    6. +
+

​
Learn More

+Explore the detailed specification for each protocol component: +

Architecture

Base Protocol

Server Features

Client Features

Contributing

\ No newline at end of file