queryDocs - Context7 MCP

The queryDocs tool fetches documentation for a library using its Context7-compatible library ID and a query. This tool is typically called after resolveLibraryId has identified the correct library.

Usage#

import { resolveLibraryId, queryDocs } from "@upstash/context7-tools-ai-sdk";import { generateText, stepCountIs } from "ai";import { openai } from "@ai-sdk/openai";const { text } = await generateText({  model: openai("gpt-5.2"),  prompt: "How do I use React Server Components?",  tools: {    resolveLibraryId: resolveLibraryId(),    queryDocs: queryDocs(),  },  stopWhen: stepCountIs(5),});

Configuration#

queryDocs(config?: Context7ToolsConfig)

Parameters#

configContext7ToolsConfig#

Configuration options for the tool.

Show properties

apiKeystring#

Context7 API key. If not provided, uses the CONTEXT7_API_KEY environment variable.

Returns#

Returns an AI SDK tool that can be used with generateText, streamText, or agents.

Tool Behavior#

When the AI model calls this tool, it:

Takes a library ID and query from the model
Fetches documentation from Context7's API
Returns the documentation content

Input Schema#

The tool accepts the following inputs from the AI model:

libraryIdstringrequired#

Context7-compatible library ID (e.g., /reactjs/react.dev, /vercel/next.js)

querystringrequired#

The question or task you need help with, scoped to a single concept. Be specific and include relevant details, but keep each query to one topic — if the user's question spans multiple distinct concepts, make a separate call per concept instead of combining them, unless the question is about how the concepts interact. Good: "How to set up authentication with JWT in Express.js" or "React useEffect cleanup function examples". Bad (too vague): "auth" or "hooks". Bad (too broad): "routing and auth and caching in Next.js".

Output Format#

On success, the tool returns the documentation as plain text, formatted for easy consumption by the AI model:

# Server ComponentsServer Components let you write UI that can be rendered and optionally cached on the server.## Example\`\`\`tsxasync function ServerComponent() {  const data = await fetchData();  return <div>{data}</div>;}\`\`\`---# Using Server Components with Client ComponentsYou can import Server Components into Client Components...

On Failure#

No documentation found for library "/invalid/library". This might have happened because you used an invalid Context7-compatible library ID. Use 'resolveLibraryId' to get a valid ID.

Examples#

Basic Usage with Both Tools#

import { resolveLibraryId, queryDocs } from "@upstash/context7-tools-ai-sdk";import { generateText, stepCountIs } from "ai";import { openai } from "@ai-sdk/openai";const { text } = await generateText({  model: openai("gpt-5.2"),  prompt: "Show me how to set up routing in Next.js App Router",  tools: {    resolveLibraryId: resolveLibraryId(),    queryDocs: queryDocs(),  },  stopWhen: stepCountIs(5),});// The model will:// 1. Call resolveLibraryId to get the library ID// 2. Call queryDocs({ libraryId: "/vercel/next.js", query: "routing in App Router" })// 3. Generate a response using the fetched documentation

With Custom Configuration#

import { queryDocs } from "@upstash/context7-tools-ai-sdk";const tool = queryDocs({  apiKey: process.env.CONTEXT7_API_KEY,});

Direct Library ID (Skip resolveLibraryId)#

If the user provides a library ID directly, the model can skip the resolution step:

import { queryDocs } from "@upstash/context7-tools-ai-sdk";import { generateText, stepCountIs } from "ai";import { openai } from "@ai-sdk/openai";const { text } = await generateText({  model: openai("gpt-5.2"),  prompt: "Using /vercel/next.js, explain middleware",  tools: {    queryDocs: queryDocs(),  },  stopWhen: stepCountIs(3),});// The model recognizes the /org/project format and calls queryDocs directly

Multi-Step Documentation Lookup#

For comprehensive documentation, the model can make multiple queries:

import { resolveLibraryId, queryDocs } from "@upstash/context7-tools-ai-sdk";import { generateText, stepCountIs } from "ai";import { anthropic } from "@ai-sdk/anthropic";const { text } = await generateText({  model: anthropic("claude-sonnet-4-20250514"),  prompt: "Give me a comprehensive guide to Supabase authentication",  tools: {    resolveLibraryId: resolveLibraryId(),    queryDocs: queryDocs(),  },  stopWhen: stepCountIs(8), // Allow more steps for multiple queries});// The model may call queryDocs multiple times with different queries// to gather comprehensive documentation

Version-Specific Documentation#

Library IDs can include version specifiers:

// Latest version"/vercel/next.js";// Specific version"/vercel/next.js/v14.3.0-canary.87";

The model can request documentation for specific versions when the user asks about a particular version.

resolveLibraryId - Search for libraries and get their IDs
Context7Agent - Pre-built agent that handles the full workflow