upstash--context7/docs/agentic-tools/ai-sdk/tools/query-docs.mdx

---
title: "queryDocs"
sidebarTitle: "queryDocs"
description: "Fetch up-to-date documentation for a specific library"
---

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

```typescript
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

```typescript
queryDocs(config?: Context7ToolsConfig)
```

### Parameters

<ParamField path="config" type="Context7ToolsConfig" optional>
  Configuration options for the tool.

  <Expandable title="properties">
    <ParamField path="apiKey" type="string" optional>
      Context7 API key. If not provided, uses the `CONTEXT7_API_KEY` environment variable.
    </ParamField>
  </Expandable>
</ParamField>

### 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:

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

### Input Schema

The tool accepts the following inputs from the AI model:

<ParamField path="libraryId" type="string" required>
  Context7-compatible library ID (e.g., `/reactjs/react.dev`, `/vercel/next.js`)
</ParamField>

<ParamField path="query" type="string" required>
  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".
</ParamField>

### Output Format

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

```
# Server Components

Server Components let you write UI that can be rendered and optionally cached on the server.

## Example

\`\`\`tsx
async function ServerComponent() {
  const data = await fetchData();
  return <div>{data}</div>;
}
\`\`\`

---

# Using Server Components with Client Components

You 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

```typescript
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

```typescript
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:

```typescript
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:

```typescript
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:

```typescript
// 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.

## Related

- [resolveLibraryId](/agentic-tools/ai-sdk/tools/resolve-library-id) - Search for libraries and get their IDs
- [Context7Agent](/agentic-tools/ai-sdk/agents/context7-agent) - Pre-built agent that handles the full workflow