Search results

This feature is eligible for Zero Data Retention (ZDR). When your organization has a ZDR arrangement, data sent through this feature is not stored after the API response is returned.

Search result content blocks enable natural citations with proper source attribution, bringing web search-quality citations to your custom applications. This feature is particularly powerful for RAG (Retrieval-Augmented Generation) applications where you need Claude to cite sources accurately.

The search results feature is available on the following models:

Claude Opus 4.7 (claude-opus-4-7)
Claude Opus 4.6 (claude-opus-4-6)
Claude Sonnet 4.6 (claude-sonnet-4-6)
Claude Sonnet 4.5 (claude-sonnet-4-5-20250929)
Claude Opus 4.5 (claude-opus-4-5-20251101)
Claude Opus 4.1 (claude-opus-4-1-20250805)
Claude Opus 4 (deprecated) (claude-opus-4-20250514)
Claude Sonnet 4 (deprecated) (claude-sonnet-4-20250514)
Claude Sonnet 3.7 (deprecated) (claude-3-7-sonnet-20250219)
Claude Haiku 4.5 (claude-haiku-4-5-20251001)
Claude Haiku 3.5 (deprecated) (claude-3-5-haiku-20241022)

Key benefits

Natural citations: Achieve the same citation quality as web search for any content
Flexible integration: Use in tool returns for dynamic RAG or as top-level content for pre-fetched data
Proper source attribution: Each result includes source and title information for clear attribution
No document workarounds needed: Eliminates the need for document-based workarounds
Consistent citation format: Matches the citation quality and format of Claude's web search functionality

How it works

Search results can be provided in two ways:

From tool calls: Your custom tools return search results, enabling dynamic RAG applications
As top-level content: You provide search results directly in user messages for pre-fetched or cached content

In both cases, Claude can automatically cite information from the search results with proper source attribution.

Search result schema

Search results use the following structure:

{
  "type": "search_result",
  "source": "https://example.com/article", // Required: Source URL or identifier
  "title": "Article Title", // Required: Title of the result
  "content": [
    // Required: Array of text blocks
    {
      "type": "text",
      "text": "The actual content of the search result..."
    }
  ],
  "citations": {
    // Optional: Citation configuration
    "enabled": true // Enable/disable citations for this result
  }
}

Required fields

Field	Type	Description
`type`	string	Must be `"search_result"`
`source`	string	The source URL or identifier for the content
`title`	string	A descriptive title for the search result
`content`	array	An array of text blocks containing the actual content

Optional fields

Field	Type	Description
`citations`	object	Citation configuration with `enabled` boolean field
`cache_control`	object	Cache control settings (e.g., `{"type": "ephemeral"}`)

Each item in the content array must be a text block with:

type: Must be "text"
text: The actual text content (non-empty string)

Method 1: Search results from tool calls

The most powerful use case is returning search results from your custom tools. This enables dynamic RAG applications where tools fetch and return relevant content with automatic citations.

Example: Knowledge base tool

Method 2: Search results as top-level content

You can also provide search results directly in user messages. This is useful for:

Pre-fetched content from your search infrastructure
Cached search results from previous queries
Content from external search services
Testing and development

Example: Direct search results

Claude's response with citations

Regardless of how search results are provided, Claude automatically includes citations when using information from them:

{
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "All API requests must include an API key in the Authorization header. Keys can be generated from the dashboard.",
      "citations": [
        {
          "type": "search_result_location",
          "cited_text": "All API requests must include an API key in the Authorization header. Keys can be generated from the dashboard. Rate limits: 1000 requests per hour for standard tier, 10000 for premium.",
          "source": "https://docs.company.com/api-reference",
          "title": "API Reference - Authentication",
          "search_result_index": 0,
          "start_block_index": 0,
          "end_block_index": 1
        }
      ]
    },
    {
      "type": "text",
      "text": "\n\nTo set this up from scratch, you'll need to "
    },
    {
      "type": "text",
      "text": "sign up for an account, generate an API key from the dashboard, install the SDK using `pip install company-sdk`, and initialize the client with your API key.",
      "citations": [
        {
          "type": "search_result_location",
          "cited_text": "To get started: 1) Sign up for an account, 2) Generate an API key from the dashboard, 3) Install our SDK using pip install company-sdk, 4) Initialize the client with your API key.",
          "source": "https://docs.company.com/quickstart",
          "title": "Getting Started Guide",
          "search_result_index": 1,
          "start_block_index": 0,
          "end_block_index": 1
        }
      ]
    }
  ]
}

Citation fields

Each citation includes:

Field	Type	Description
`type`	string	Always `"search_result_location"` for search result citations
`source`	string	The source from the original search result
`title`	string or null	The title from the original search result
`cited_text`	string	The full text of the cited block(s), concatenated. Equals the contents of `content[start_block_index:end_block_index]` joined together. Not counted toward output tokens.
`search_result_index`	integer	0-based index of the cited search result among all blocks in the request, in the order they appear (across all messages and tool results).

The block indices identify a slice of the search result's content array, and cited_text is the full text of that slice. The text block is the minimal citable unit: Claude cites whole blocks, not substrings within a block. To get finer-grained citations, split your search result content into smaller blocks (see Multiple content blocks).

Multiple content blocks

Search results can contain multiple text blocks in the content array:

{
  "type": "search_result",
  "source": "https://docs.company.com/api-guide",
  "title": "API Documentation",
  "content": [
    {
      "type": "text",
      "text": "Authentication: All API requests require an API key."
    },
    {
      "type": "text",
      "text": "Rate Limits: The API allows 1000 requests per hour per key."
    },
    {
      "type": "text",
      "text": "Error Handling: The API returns standard HTTP status codes."
    }
  ]
}

A citation referencing the rate limits block looks like:

{
  "type": "search_result_location",
  "cited_text": "Rate Limits: The API allows 1000 requests per hour per key.",
  "source": "https://docs.company.com/api-guide",
  "title": "API Documentation",
  "search_result_index": 0,
  "start_block_index": 1,
  "end_block_index": 2
}

When this search result is cited, start_block_index and end_block_index identify which of these blocks the citation covers, and cited_text contains exactly those blocks' text. Splitting content into smaller, focused blocks gives Claude finer citation boundaries; combining content into one block means every citation returns the full text. This is the same model used by custom content documents in the Citations feature.

Advanced usage

Combining both methods

You can use both tool-based and top-level search results in the same conversation:

from anthropic.types import MessageParam, SearchResultBlockParam, TextBlockParam

# First message with top-level search results
messages = [
    MessageParam(
        role="user",
        content=[
            SearchResultBlockParam(
                type="search_result",
                source="https://docs.company.com/overview",
                title="Product Overview",
                content=[
                    TextBlockParam(
                        type="text", text="Our product helps teams collaborate..."
                    )
                ],
                citations={"enabled": True},
            ),
            TextBlockParam(
                type="text",
                text="Tell me about this product and search for pricing information",
            ),
        ],
    )
]

# Claude might respond and call a tool to search for pricing
# Then you provide tool results with more search results

Combining with other content types

Both methods support mixing search results with other content:

from anthropic.types import SearchResultBlockParam, TextBlockParam

# In tool results
tool_result = [
    SearchResultBlockParam(
        type="search_result",
        source="https://docs.company.com/guide",
        title="User Guide",
        content=[TextBlockParam(type="text", text="Configuration details...")],
        citations={"enabled": True},
    ),
    TextBlockParam(
        type="text", text="Additional context: This applies to version 2.0 and later."
    ),
]

# In top-level content
user_content = [
    SearchResultBlockParam(
        type="search_result",
        source="https://research.com/paper",
        title="Research Paper",
        content=[TextBlockParam(type="text", text="Key findings...")],
        citations={"enabled": True},
    ),
    {
        "type": "image",
        "source": {"type": "url", "url": "https://example.com/chart.png"},
    },
    TextBlockParam(
        type="text", text="How does the chart relate to the research findings?"
    ),
]

Cache control

Add cache control for better performance:

{
  "type": "search_result",
  "source": "https://docs.company.com/guide",
  "title": "User Guide",
  "content": [{ "type": "text", "text": "..." }],
  "cache_control": {
    "type": "ephemeral"
  }
}

Citation control

By default, citations are disabled for search results. You can enable citations by explicitly setting the citations configuration:

{
  "type": "search_result",
  "source": "https://docs.company.com/guide",
  "title": "User Guide",
  "content": [{ "type": "text", "text": "Important documentation..." }],
  "citations": {
    "enabled": true // Enable citations for this result
  }
}

When citations.enabled is set to true, Claude includes citation references when using information from the search result. This enables:

Natural citations for your custom RAG applications
Source attribution when interfacing with proprietary knowledge bases
Web search-quality citations for any custom tool that returns search results

Citations are all-or-nothing: either all search results in a request must have citations enabled, or all must have them disabled. Mixing search results with different citation settings results in an error.

Best practices

For tool-based search (Method 1)

Dynamic content: Use for real-time searches and dynamic RAG applications
Error handling: Return appropriate messages when searches fail
Result limits: Return only the most relevant results to avoid context overflow

For top-level search (Method 2)

Pre-fetched content: Use when you already have search results
Batch processing: Ideal for processing multiple search results at once
Testing: Great for testing citation behavior with known content

General best practices

Structure results effectively:
- Use clear, permanent source URLs
- Provide descriptive titles
- Break long content into logical text blocks to give Claude finer citation boundaries
Maintain consistency:
- Use consistent source formats across your application
- Ensure titles accurately reflect content
- Keep formatting consistent

Handle errors gracefully:

def search_with_fallback(query):
    try:
        results = perform_search(query)
        if not results:
            return {"type": "text", "text": "No results found."}
        return format_as_search_results(results)
    except Exception as e:
        return {"type": "text", "text": f"Search error: {str(e)}"}

Limitations

Search result content blocks are available on Claude API, Amazon Bedrock, and Google Cloud's Vertex AI
Only text content is supported within search results (no images or other media)
The content array must contain at least one text block

from anthropic.types import (
    MessageParam,
    TextBlockParam,
    SearchResultBlockParam,
    ToolResultBlockParam,
)

client = Anthropic()

# Define a knowledge base search tool
knowledge_base_tool = {
    "name": "search_knowledge_base",
    "description": "Search the company knowledge base for information",
    "input_schema": {
        "type": "object",
        "properties": {"query": {"type": "string", "description": "The search query"}},
        "required": ["query"],
    },
}


# Function to handle the tool call
def search_knowledge_base(query):
    # Your search logic here
    # Returns search results in the correct format
    return [
        SearchResultBlockParam(
            type="search_result",
            source="https://docs.company.com/product-guide",
            title="Product Configuration Guide",
            content=[
                TextBlockParam(
                    type="text",
                    text="To configure the product, navigate to Settings > Configuration. The default timeout is 30 seconds, but can be adjusted between 10-120 seconds based on your needs.",
                )
            ],
            citations={"enabled": True},
        ),
        SearchResultBlockParam(
            type="search_result",
            source="https://docs.company.com/troubleshooting",
            title="Troubleshooting Guide",
            content=[
                TextBlockParam(
                    type="text",
                    text="If you encounter timeout errors, first check the configuration settings. Common causes include network latency and incorrect timeout values.",
                )
            ],
            citations={"enabled": True},
        ),
    ]


# Create a message with the tool
response = client.messages.create(
    model="claude-opus-4-7",  # Works with all supported models
    max_tokens=1024,
    tools=[knowledge_base_tool],
    messages=[
        MessageParam(role="user", content="How do I configure the timeout settings?")
    ],
)

# When Claude calls the tool, provide the search results
if response.content[0].type == "tool_use":
    tool_result = search_knowledge_base(response.content[0].input["query"])

    # Send the tool result back
    final_response = client.messages.create(
        model="claude-opus-4-7",  # Works with all supported models
        max_tokens=1024,
        messages=[
            MessageParam(
                role="user", content="How do I configure the timeout settings?"
            ),
            MessageParam(role="assistant", content=response.content),
            MessageParam(
                role="user",
                content=[
                    ToolResultBlockParam(
                        type="tool_result",
                        tool_use_id=response.content[0].id,
                        content=tool_result,  # Search results go here
                    )
                ],
            ),
        ],
    )

from anthropic.types import MessageParam, TextBlockParam, SearchResultBlockParam

client = Anthropic()

# Provide search results directly in the user message
response = client.messages.create(
    model="claude-opus-4-7",
    max_tokens=1024,
    messages=[
        MessageParam(
            role="user",
            content=[
                SearchResultBlockParam(
                    type="search_result",
                    source="https://docs.company.com/api-reference",
                    title="API Reference - Authentication",
                    content=[
                        TextBlockParam(
                            type="text",
                            text="All API requests must include an API key in the Authorization header. Keys can be generated from the dashboard. Rate limits: 1000 requests per hour for standard tier, 10000 for premium.",
                        )
                    ],
                    citations={"enabled": True},
                ),
                SearchResultBlockParam(
                    type="search_result",
                    source="https://docs.company.com/quickstart",
                    title="Getting Started Guide",
                    content=[
                        TextBlockParam(
                            type="text",
                            text="To get started: 1) Sign up for an account, 2) Generate an API key from the dashboard, 3) Install our SDK using pip install company-sdk, 4) Initialize the client with your API key.",
                        )
                    ],
                    citations={"enabled": True},
                ),
                TextBlockParam(
                    type="text",
                    text="Based on these search results, how do I authenticate API requests and what are the rate limits?",
                ),
            ],
        )
    ],
)

print(response)

search_result