ai-mcp
Host-side Model Context Protocol (MCP) client for TanStack AI: connect to external MCP servers, discover and run their tools inside any adapter's chat() loop, read resources and prompts, generate TypeScript types (typed tool names/pool keys) with the bundled CLI, and manage lifecycle with close()/await using.
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Low
Low-risk findings worth noting
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Low-risk findings.
2 low severity findings. Worth noting, but not necessarily harmful.
W011: Third-party content exposure detected (indirect prompt injection risk).
What this means
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
Why it was flagged
Runtime path: the required workflow calls an OUTSIDER MCP server over HTTP/SSE (public/untrusted server) and forwards that server’s tool JSON schema/prompts/resources into `chat()`/LLM context (e.g., `chat({ tools: await client.tools() })` and `client.readResource(...)`, plus `client.prompts()` → `mcpPromptToMessages`).
W012: Unverifiable external dependency detected (runtime URL that controls agent).
What this means
The skill fetches instructions or code from an external URL at runtime, and the fetched content directly controls the agent’s prompts or executes code. This dynamic dependency allows the external source to modify the agent’s behavior without any changes to the skill itself.
Why it was flagged
The skill actively connects at runtime to external MCP servers (e.g., https://mcp.example.com/mcp, https://mcp.github.com/mcp) and uses client.getPrompt / readResource to fetch prompts/resources which are converted into chat messages and thus directly control model input.
- Repository
- TanStack/ai
- Commit
4ab149f
- Audited
- Security analysis
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