@ai-sdk/mcp
Enables AI SDK applications to access external tools and data sources through a standardized protocol, reducing integration friction and expanding LLM capabi…
The **Model Context Protocol (MCP) client** for the [AI SDK](https://ai-sdk.dev/docs) lets you connect to MCP servers and use their tools with AI SDK functions like `generateText` and `streamText`.
- Connect Claude to MCP servers and execute remote tools within your application code
- Integrate external tool capabilities into AI SDK text generation workflows automatically
- Build multi-tool AI systems where Claude accesses specialized services via MCP protocols
Enables AI SDK applications to access external tools and data sources through a standardized protocol, reducing integration friction and expanding LLM capabilities without rewriting agent logic.
Development teams building agentic applications that need to connect AI models to external APIs, databases, or custom services via MCP servers.
https://www.npmjs.com/package/@ai-sdk/mcp
By GitHub Actions
How to Get It
npx -y @ai-sdk/mcp
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
Once it’s connected, paste this into Claude:
Help me connect Claude to MCP servers and execute remote tools within my application code
Trust Signals Auto-scanned
Data & Access
Community Pulse New
- Direct OS level API to control computers using AI (MCP server/client template) — Hacker News · 2 pts
- Add MCP Apps to Your AI SDK Application — Hacker News · 1 pts
- Addressing security and quality issues with MCP tools in AI Agents — Hacker News · 1 pts
3 mentions across 1 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 25,740 GitHub stars; contributors unknown; last commit 0d ago; license Apache-2.0.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
How to evaluate tools before deploying →
Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
Scored from trust signals (evidence-eval-v1): 25,740 GitHub stars; contributors unknown; last commit 0d ago; license Apache-2.0.