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mcp-use is a software framework for building MCP servers, apps and tool-using AI agents. Its current TypeScript documentation centers on typed server tools connected to interactive React Views, while its Python package focuses on connecting language models to MCP servers and building agents, clients and servers. The two implementations serve related goals, but their documented workflows and APIs are not identical.

What is mcp-use?

The mcp-use project describes itself as a full-stack framework for developing MCP Apps and MCP servers for AI agents. Its TypeScript v2 materials highlight typed tool-to-UI contracts, Views, a stateless runtime, an Inspector, screenshot verification, CLI workflows and deployment. The wider project also includes packages for servers, clients, agents, Inspector, tunneling and app scaffolding, alongside a Python implementation. See the mcp-use repository for the project and package entry points.

In this context, an MCP server exposes capabilities—such as tools—to an MCP client or agent. An MCP App can connect a tool’s output to an interactive interface. The TypeScript documentation illustrates a workflow in which a server tool declares Zod input and output schemas, binds to a named View, and returns text and structured content; a React component reads the tool context and renders the result. This is the project’s documented approach, not an independent verification of runtime behavior.

How the TypeScript workflow fits together

Start with the scaffold

For a new TypeScript app, the repository currently directs developers to run:

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npx -y create-mcp-use-app@latest

The generated project is documented as including a server, TypeScript configuration, scripts, an Inspector and a React View pipeline. Run the generated project’s development script and use its local Inspector route to inspect the app. Because scaffolding commands and generated scripts can change, check the repository’s current setup instructions before starting a new project.

Connect a server tool to a View

The documented TypeScript pattern is to define a tool with typed schemas, associate it with a named View, and make its output available to that View. The React component then uses the provided tool context to display the result. This ties the interaction contract to the UI rather than treating the server and widget as entirely separate pieces.

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The project positions this approach for MCP Apps that run inside ChatGPT and Claude, as well as for MCP servers and agents. Its TypeScript materials also describe a stateless runtime, Inspector, screenshot verification and deployment tooling. Consult the current TypeScript documentation for the specific APIs and integration steps; package versions and labels are subject to change.

What the Python package offers

The Python README frames mcp-use as a way to connect LLMs to MCP servers and build tool-using agents. It also documents client and server creation. The README lists protocol primitives including tools, resources, prompts, sampling, elicitation, roots and authentication, and transports including stdio, SSE and Streamable HTTP. Installation begins with:

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pip install mcp-use

Provider integrations may require additional LangChain packages, and the selected model must support tool calling. Check the current Python package documentation for provider-specific dependencies and usage, since these details can change.

TypeScript or Python: which workflow fits?

Decision point TypeScript materials Python materials
Documented emphasis Server framework, MCP Apps, interactive Views, agents and clients LLM connections to MCP servers, tool-using agents, clients and server creation
UI approach established in the current materials React Views connected to typed server tools No equivalent UI pipeline is established by the Python README
Model integration Not characterized by the cited TypeScript summary as a particular provider integration LangChain provider integrations may require extra packages; the model must support tool calling
Protocol features and transports listed Not stated in the cited TypeScript summary Tools, resources, prompts, sampling, elicitation, roots and authentication; stdio, SSE and Streamable HTTP

Choose based on the deliverable and the current language-specific documentation, not on an assumption that the two packages have matching features or APIs. If the central requirement is a React interface bound to a server tool, the TypeScript documentation directly describes that workflow. If the work is a Python agent, client or server that uses a tool-calling model, the Python README is the more relevant starting point.

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How to read the project’s benchmark figures

The mcp-use repository publishes a comparison table with throughput and MCP App development stack-size figures. The values below are project-reported; the retrieved comparison does not state a publication year or provide enough methodology to assess workload, setup or repeatability independently.

Project in comparison Reported throughput Reported MCP App development stack size
mcp-use v2 10,982 ops/s 74.4 MiB
FastMCP TS 6,628 ops/s 122.5 MiB
Official SDK v2 8,050 ops/s 99.0 MiB
xmcp 6,585 ops/s 121.9 MiB
Skybridge 8,116 ops/s 137.5 MiB
mcp-handler 6,324 ops/s 388.0 MiB

These are figures reported by the project in its repository comparison, not independently verified results. Without the test conditions and methodology, they are not enough to establish that one framework will be faster or smaller in a particular application. Treat them as a reason to inspect the project’s benchmark details, not as a substitute for testing your own workload.

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What to verify before adopting mcp-use

  • Package and API versions: Confirm the current release and version alignment for the packages your project needs.
  • Language-specific capabilities: Check the relevant TypeScript or Python documentation instead of assuming features are shared.
  • Model and provider requirements: For Python integrations, verify tool-calling support and any extra provider dependencies.
  • Protocol and transport needs: Confirm that the implementation and transport you plan to use meet your application’s requirements.
  • Deployment details: Follow current project guidance and check the terms of any hosting or deployment service independently.

Older pages at docs.mcp-use.io describe earlier client-oriented workflows, including web research and data analysis. They are useful as historical context, but the current repository and its language-specific READMEs are the better starting points for present-day implementation details.

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