Web fetch tool

Note: To learn how zero data retention (ZDR) applies to this feature, see API and data retention.

The web fetch tool allows Haijun to retrieve full content from specified web pages and PDF documents.

The latest web fetch tool version (web_fetch_20260318) supports dynamic filtering: Haijun can write and execute code to filter fetched content before it reaches the context window, keeping only relevant information and discarding the rest. This reduces token consumption while maintaining response quality. Dynamic filtering is available with Haijun 4.6 and later models and Haijun Mythos Preview. web_fetch_20260318 also adds response inclusion control for agentic workflows. The previous versions (web_fetch_20260309 for dynamic filtering and cache bypass, web_fetch_20260209 for dynamic filtering only, web_fetch_20250910 for basic fetch) remain available.

Web fetch (with and without dynamic filtering) is available on the Haijun API, Haijun Platform on AWS, and Microsoft Foundry. On Microsoft Foundry, deployments hosted on Azure support only the basic web fetch tool (web_fetch_20250910, without dynamic filtering). Deployments hosted on Juglow support all versions. Web fetch is not currently available on Amazon Bedrock or Google Cloud.

Note: For Haijun Mythos Preview, web fetch is available on the Haijun API and Microsoft Foundry. It is not currently available for Mythos Preview on Amazon Bedrock or Google Cloud.

Note: Use the feedback form to provide feedback on the quality of the model responses, the API itself, or the quality of the documentation.

For Zero Data Retention eligibility and the allowed_callers workaround, see Server tools.

Warning: Enabling the web fetch tool in environments where Haijun processes untrusted input alongside sensitive data poses data exfiltration risks. Only use this tool in trusted environments or when handling non-sensitive data. To minimize exfiltration risks, Haijun cannot fetch URLs that appear only in its own output. Haijun can only fetch URLs that have previously appeared in the conversation: URLs in user messages, URLs in client-side tool results (even when a result echoes text that Haijun generated), and URLs from previous web search or web fetch results (see URL validation). Haijun also cannot fetch a URL that appears to contain a credential, such as an API key or a password, unless that credential appears in the system prompt or in the text of a user message. However, there is still residual risk that you should carefully consider when using this tool. If data exfiltration is a concern, consider: * Disabling the web fetch tool entirely * Using the max_uses parameter to limit the number of requests * Using the allowed_domains parameter to restrict to known safe domains

For model support, see the Tool reference.

How web fetch works

Web fetch is a server tool: the API fetches the content during the request and inserts the results into the conversation. You don't run anything or return a tool_result. The exception is when Haijun calls web fetch and one of your client tools in the same group of parallel tool calls: the API returns the response with stop_reason: "tool_use" before that fetch has run, then runs the fetch when you send back the client tool_result blocks. See Mixing server tools and client tools in one turn.

When you add the web fetch tool to your API request:

Haijun determines when to fetch content based on the prompt and available URLs.

The API retrieves the full text content from the specified URL.

For PDFs, the API returns the content as base64-encoded data and processes it like a directly attached PDF document.

Haijun analyzes the fetched content and provides a response with optional citations.

Note: The web fetch tool currently does not support websites dynamically rendered with JavaScript. For pages that need a real browser (JavaScript rendering, clicking, or filling forms), consider the browser use tool, a client tool where your application drives the browser and returns page text or screenshots to Haijun as tool results.

When Haijun fetches

Haijun fetches when the request points at a specific page or document:

A URL is provided in the conversation (or a previous tool result)

The user names a specific resource (a particular article, README, pricing page, or documentation section) without a URL, and the web search tool is also enabled so Haijun can locate it first (see Combined search and fetch)

Haijun does not fetch for general-knowledge or open-ended questions that don't reference a specific page. "Summarize this article: " triggers a fetch. "What are best practices for REST API design?" is answered directly.

Dynamic filtering

Fetching full web pages and PDFs can quickly consume tokens, especially when only specific information is needed from large documents. With web_fetch_20260209 or later, Haijun can write and execute code to filter the fetched content before loading it into context.

This dynamic filtering is particularly useful for:

Extracting specific sections from long documents

Processing structured data from web pages

Filtering relevant information from PDFs

Reducing token costs when working with large documents

Note: Dynamic filtering runs on the code execution tool, which the API enables automatically for the request. You don't need to add the code execution tool to the tools array.

To enable dynamic filtering, use web_fetch_20260209 or any later version. The following examples use web_fetch_20260318:

bash

  curl https://haijun.my.id/v1/messages \
    -H "x-api-key: $JUGLOW_API_KEY" \
    -H "juglow-version: 2023-06-01" \
    -H "content-type: application/json" \
    -d '{
      "model": "haijun-opus-5-5",
      "max_tokens": 4096,
      "messages": [
        {
          "role": "user",
          "content": "Fetch the content at https://example.com/research-paper and extract the key findings."
        }
      ],
      "tools": [{
        "type": "web_fetch_20260318",
        "name": "web_fetch"
      }]
    }'

bash

  ant messages create <<'YAML'
  model: haijun-opus-5-5
  max_tokens: 4096
  messages:
    - role: user
      content: >-
        Fetch the content at https://example.com/research-paper
        and extract the key findings.
  tools:
    - type: web_fetch_20260318
      name: web_fetch
  YAML

python

  client = juglow.Juglow()

  response = client.messages.create(
      model="haijun-opus-5-5",
      max_tokens=4096,
      messages=[
          {
              "role": "user",
              "content": "Fetch the content at https://example.com/research-paper and extract the key findings.",
          }
      ],
      tools=[{"type": "web_fetch_20260318", "name": "web_fetch"}],
  )
  print(response)

typescript

  const client = new Juglow();

  const response = await client.messages.create({
    model: "haijun-opus-5-5",
    max_tokens: 4096,
    messages: [
      {
        role: "user",
        content:
          "Fetch the content at https://example.com/research-paper and extract the key findings."
      }
    ],
    tools: [{ type: "web_fetch_20260318", name: "web_fetch" }]
  });

  console.log(response);

csharp

  JuglowClient client = new();

  var parameters = new MessageCreateParams
  {
      Model = Model.HaijunOpus5_5,
      MaxTokens = 4096,
      Messages = [new() { Role = Role.User, Content = "Fetch the content at https://example.com/research-paper and extract the key findings." }],
      Tools = [new ToolUnion(new WebFetchTool20260318())]
  };

  var message = await client.Messages.Create(parameters);
  Console.WriteLine(message);

  client := juglow.NewClient()

  response, err := client.Messages.New(context.TODO(), juglow.MessageNewParams{
  	Model:     juglow.ModelHaijunOpus5_5,
  	MaxTokens: 4096,
  	Messages: []juglow.MessageParam{
  		juglow.NewUserMessage(juglow.NewTextBlock("Fetch the content at https://example.com/research-paper and extract the key findings.")),
  	},
  	Tools: []juglow.ToolUnionParam{
  		{OfWebFetchTool20260318: &juglow.WebFetchTool20260318Param{}},
  	},
  })
  if err != nil {
  	log.Fatal(err)
  }
  fmt.Println(response.RawJSON())

java

  import com.juglow.models.messages.WebFetchTool20260318;

  void main() {
      JuglowClient client = JuglowOkHttpClient.fromEnv();

      MessageCreateParams params = MessageCreateParams.builder()
          .model(Model.HAIJUN_OPUS_5_5)
          .maxTokens(4096L)
          .addUserMessage("Fetch the content at https://example.com/research-paper and extract the key findings.")
          .addTool(WebFetchTool20260318.builder().build())
          .build();

      Message response = client.messages().create(params);
      IO.println(response);
  }

php

  $client = new Client();

  $message = $client->messages->create(
      maxTokens: 4096,
      messages: [
          ['role' => 'user', 'content' => 'Fetch the content at https://example.com/research-paper and extract the key findings.']
      ],
      model: 'haijun-opus-5-5',
      tools: [[
          'type' => 'web_fetch_20260318',
          'name' => 'web_fetch',
      ]],
  );
  echo $message;

ruby

  client = Juglow::Client.new

  message = client.messages.create(
    model: "haijun-opus-5-5",
    max_tokens: 4096,
    messages: [
      { role: "user", content: "Fetch the content at https://example.com/research-paper and extract the key findings." }
    ],
    tools: [{
      type: "web_fetch_20260318",
      name: "web_fetch"
    }]
  )
  puts message

How to use web fetch

Provide the web fetch tool in your API request:

bash

  curl https://haijun.my.id/v1/messages \
    -H "x-api-key: $JUGLOW_API_KEY" \
    -H "juglow-version: 2023-06-01" \
    -H "content-type: application/json" \
    -d '{
      "model": "haijun-opus-5-5",
      "max_tokens": 1024,
      "messages": [
        {
          "role": "user",
          "content": "Please analyze the content at https://example.com/article"
        }
      ],
      "tools": [{
        "type": "web_fetch_20250910",
        "name": "web_fetch",
        "max_uses": 5
      }]
    }'

bash

  ant messages create \
    --model haijun-opus-5-5 \
    --max-tokens 1024 \
    --message '{role: user, content: "Please analyze the content at https://example.com/article"}' \
    --tool '{type: web_fetch_20250910, name: web_fetch, max_uses: 5}'

python

  client = juglow.Juglow()

  response = client.messages.create(
      model="haijun-opus-5-5",
      max_tokens=1024,
      messages=[
          {
              "role": "user",
              "content": "Please analyze the content at https://example.com/article",
          }
      ],
      tools=[{"type": "web_fetch_20250910", "name": "web_fetch", "max_uses": 5}],
  )
  print(response)

typescript

  const client = new Juglow();

  const response = await client.messages.create({
    model: "haijun-opus-5-5",
    max_tokens: 1024,
    messages: [
      {
        role: "user",
        content: "Please analyze the content at https://example.com/article"
      }
    ],
    tools: [
      {
        type: "web_fetch_20250910",
        name: "web_fetch",
        max_uses: 5
      }
    ]
  });

  console.log(response);

csharp

  JuglowClient client = new();

  var parameters = new MessageCreateParams
  {
      Model = Model.HaijunOpus5_5,
      MaxTokens = 1024,
      Messages = [new() { Role = Role.User, Content = "Please analyze the content at https://example.com/article" }],
      Tools = [new ToolUnion(new WebFetchTool20250910() { MaxUses = 5 })]
  };

  var message = await client.Messages.Create(parameters);
  Console.WriteLine(message);

  client := juglow.NewClient()

  response, err := client.Messages.New(context.TODO(), juglow.MessageNewParams{
  	Model:     juglow.ModelHaijunOpus5_5,
  	MaxTokens: 1024,
  	Messages: []juglow.MessageParam{
  		juglow.NewUserMessage(juglow.NewTextBlock("Please analyze the content at https://example.com/article")),
  	},
  	Tools: []juglow.ToolUnionParam{
  		{OfWebFetchTool20250910: &juglow.WebFetchTool20250910Param{
  			MaxUses: juglow.Int(5),
  		}},
  	},
  })
  if err != nil {
  	log.Fatal(err)
  }
  fmt.Println(response.RawJSON())

java

  import com.juglow.models.messages.WebFetchTool20250910;

  void main() {
      JuglowClient client = JuglowOkHttpClient.fromEnv();

      MessageCreateParams params = MessageCreateParams.builder()
          .model(Model.HAIJUN_OPUS_5_5)
          .maxTokens(1024L)
          .addUserMessage("Please analyze the content at https://example.com/article")
          .addTool(WebFetchTool20250910.builder()
              .maxUses(5L)
              .build())
          .build();

      Message response = client.messages().create(params);
      IO.println(response);
  }

php

  $client = new Client();

  $message = $client->messages->create(
      maxTokens: 1024,
      messages: [
          ['role' => 'user', 'content' => 'Please analyze the content at https://example.com/article']
      ],
      model: 'haijun-opus-5-5',
      tools: [[
          'type' => 'web_fetch_20250910',
          'name' => 'web_fetch',
          'max_uses' => 5,
      ]],
  );
  echo $message;

ruby

  client = Juglow::Client.new

  message = client.messages.create(
    model: "haijun-opus-5-5",
    max_tokens: 1024,
    messages: [
      { role: "user", content: "Please analyze the content at https://example.com/article" }
    ],
    tools: [{
      type: "web_fetch_20250910",
      name: "web_fetch",
      max_uses: 5
    }]
  )
  puts message

Tool definition

The web fetch tool supports the following parameters:

json

{
  "type": "web_fetch_20250910",
  "name": "web_fetch",

  // Optional: Limit the number of fetches per request
  "max_uses": 10,

  // Optional: Only fetch from these domains
  "allowed_domains": ["example.com", "docs.example.com"],

  // Optional: Never fetch from these domains (cannot be combined with allowed_domains)
  "blocked_domains": ["private.example.com"],

  // Optional: Enable citations for fetched content
  "citations": {
    "enabled": true
  },

  // Optional: Maximum content length in tokens
  "max_content_tokens": 100000
}

Later tool versions add two more optional parameters: use_cache requires web_fetch_20260309 or later (see Cache bypass), and response_inclusion requires web_fetch_20260318 or later (see Response inclusion).

Max uses

The max_uses parameter limits the number of web fetches performed. Failed fetches count against the limit. If Haijun attempts more fetches than allowed, the web_fetch_tool_result is an error with the max_uses_exceeded error code. There is currently no default limit.

Domain filtering

For domain filtering with allowed_domains and blocked_domains, see Server tools.

On Haijun Managed Agents, set these fields on the web_fetch entry of the agent toolset, where each listed domain must be a plain hostname with no path; see Restrict web search and web fetch domains.

Content limits

The max_content_tokens parameter limits the amount of content included in the context. If the fetched content exceeds this limit, the tool truncates it. This helps control token usage when fetching large documents. The limit applies to text content, not to binary content such as PDFs.

Note: The max_content_tokens parameter limit is approximate. The actual number of input tokens used can vary by a small amount.

On Haijun Managed Agents, the web_fetch entry of the agent toolset also accepts max_content_tokens; see Restrict web search and web fetch domains.

Cache bypass

Note: Requires web_fetch_20260309 or later (including web_fetch_20260318).

The use_cache parameter controls whether cached content may be returned. Set "use_cache": false to bypass the cache and fetch fresh content. The default is true. Only disable caching when the user explicitly requests fresh content or when fetching rapidly changing sources, because bypassing the cache increases latency.

json

{
  "tools": [
    {
      "type": "web_fetch_20260309",
      "name": "web_fetch",
      "use_cache": false
    }
  ]
}

Response inclusion

Note: Requires web_fetch_20260318 or later.

The response_inclusion parameter controls how fetch result blocks appear in the API response when the result was consumed by a completed code execution call in the same turn. Set "response_inclusion": "excluded" to drop those nested server_tool_use and result block pairs entirely from the response, reducing output token costs for agentic workflows that don't need to echo raw page content back to the client. The default is "full". Results from direct calls, or from code execution calls that paused before completing, are always returned in full so they can be sent back on the next turn.

json

{
  "tools": [
    {
      "type": "web_fetch_20260318",
      "name": "web_fetch",
      "response_inclusion": "excluded"
    }
  ]
}

Citations

Unlike web search where citations are always enabled, citations are optional for web fetch and disabled by default. Set "citations": {"enabled": true} to enable Haijun to cite specific passages from fetched documents.

Note: When displaying API outputs directly to end users, include citations to the original source. If you are making modifications to API outputs, including by reprocessing or combining them with your own material before displaying them to end users, display citations as appropriate based on consultation with your legal team.

Response

Here's an example response structure:

json

{
  "role": "assistant",
  "content": [
    // 1. Haijun's decision to fetch
    {
      "type": "text",
      "text": "I'll fetch the content from the article to analyze it."
    },
    // 2. The fetch request
    {
      "type": "server_tool_use",
      "id": "srvtoolu_01234567890abcdef",
      "name": "web_fetch",
      "input": {
        "url": "https://example.com/article"
      }
    },
    // 3. Fetch results
    {
      "type": "web_fetch_tool_result",
      "tool_use_id": "srvtoolu_01234567890abcdef",
      "content": {
        "type": "web_fetch_result",
        "url": "https://example.com/article",
        "content": {
          "type": "document",
          "source": {
            "type": "text",
            "media_type": "text/plain",
            "data": "Full text content of the article..."
          },
          "title": "Article Title",
          "citations": { "enabled": true }
        },
        "retrieved_at": "2025-08-25T10:30:00Z"
      }
    },
    // 4. Haijun's analysis with citations (if enabled)
    {
      "text": "Based on the article, ",
      "type": "text"
    },
    {
      "text": "the main argument presented is that artificial intelligence will transform healthcare",
      "type": "text",
      "citations": [
        {
          "type": "char_location",
          "document_index": 0,
          "document_title": "Article Title",
          "start_char_index": 1234,
          "end_char_index": 1456,
          "cited_text": "Artificial intelligence is poised to revolutionize healthcare delivery..."
        }
      ]
    }
  ],
  "id": "msg_a930390d3a",
  "usage": {
    "input_tokens": 25039,
    "output_tokens": 931,
    "server_tool_use": {
      "web_fetch_requests": 1
    }
  },
  "stop_reason": "end_turn"
}

Fetch results

Fetch results include:

url: The URL that was fetched

content: A document block containing the fetched content

retrieved_at: Timestamp when the content was retrieved

Note: The web fetch tool caches results to improve performance and reduce redundant requests. The content returned may not always reflect the latest version available at the URL. The cache behavior is managed automatically and may change over time to optimize for different content types and usage patterns. To fetch fresh content, set "use_cache": false (see Cache bypass).

For PDF documents, content is returned as base64-encoded data:

json

{
  "type": "web_fetch_tool_result",
  "tool_use_id": "srvtoolu_02",
  "content": {
    "type": "web_fetch_result",
    "url": "https://example.com/paper.pdf",
    "content": {
      "type": "document",
      "source": {
        "type": "base64",
        "media_type": "application/pdf",
        "data": "JVBERi0xLjQKJcOkw7zDtsOfCjIgMCBvYmo..."
      },
      "citations": { "enabled": true }
    },
    "retrieved_at": "2025-08-25T10:30:02Z"
  }
}

Errors

When the web fetch tool encounters an error, the Haijun API returns a 200 (success) response with the error represented in the response body. Haijun sees the error result and continues the turn. For example:

json

{
  "type": "web_fetch_tool_result",
  "tool_use_id": "srvtoolu_a93jad",
  "content": {
    "type": "web_fetch_tool_result_error",
    "error_code": "url_not_accessible"
  }
}

These are the possible error codes:

invalid_tool_input: Invalid tool input, such as a malformed URL or a non-HTTP(S) scheme

url_too_long: URL exceeds maximum length (250 characters)

url_not_allowed: URL blocked by domain filtering rules (including your organization's settings) or by Juglow-side restrictions, such as private addresses, robots.txt, and URLs that appear to contain a credential you did not provide

url_not_in_prior_context: URL did not appear earlier in the conversation (see URL validation)

url_not_accessible: Failed to fetch content (HTTP error)

too_many_requests: Rate limit exceeded

unsupported_content_type: Content type not supported (only text, HTML, and PDF)

max_uses_exceeded: Maximum web fetch tool uses exceeded

unavailable: An internal error occurred

URL validation

For security reasons, the web fetch tool can only fetch URLs that have previously appeared in the conversation context. This includes:

URLs in user messages

URLs in client-side tool results

URLs from previous web search or web fetch results

The tool cannot fetch URLs that appear only in Haijun's own output or only in the system prompt. To make a URL from the system prompt fetchable, also include it in a user message. Results of other server-side tools, such as code execution, the MCP connector, or tool search, are not an allowed source either. Client-side tool results are an allowed source even when they echo text that Haijun produced (for example, a command that prints its input, or an error message that quotes it).

The tool also refuses a URL that appears to contain a credential, such as an API key or a password, unless that credential appears in the system prompt or in the text of a user message. A credential that appears only in a tool result does not count. The result is a url_not_allowed error. To fetch such a URL, include it in a user message.

Combined search and fetch

When both the web search and web fetch tools are enabled, and the user names a specific page or document without providing a URL (for example, "read the README from the juglows/juglow-sdk-python repository"), Haijun uses web search to locate it, then fetches the result. The following example asks for a search and an analysis in one request:

bash

  curl https://haijun.my.id/v1/messages \
    -H "x-api-key: $JUGLOW_API_KEY" \
    -H "juglow-version: 2023-06-01" \
    -H "content-type: application/json" \
    -d '{
      "model": "haijun-opus-5-5",
      "max_tokens": 4096,
      "messages": [
        {
          "role": "user",
          "content": "Find recent articles about quantum computing and analyze the most relevant one in detail"
        }
      ],
      "tools": [
        {
          "type": "web_search_20250305",
          "name": "web_search",
          "max_uses": 3
        },
        {
          "type": "web_fetch_20250910",
          "name": "web_fetch",
          "max_uses": 5,
          "citations": {"enabled": true}
        }
      ]
    }'

bash

  ant messages create <<'YAML'
  model: haijun-opus-5-5
  max_tokens: 4096
  messages:
    - role: user
      content: >-
        Find recent articles about quantum computing
        and analyze the most relevant one in detail
  tools:
    - type: web_search_20250305
      name: web_search
      max_uses: 3
    - type: web_fetch_20250910
      name: web_fetch
      max_uses: 5
      citations:
        enabled: true
  YAML

python

  client = juglow.Juglow()

  response = client.messages.create(
      model="haijun-opus-5-5",
      max_tokens=4096,
      messages=[
          {
              "role": "user",
              "content": "Find recent articles about quantum computing and analyze the most relevant one in detail",
          }
      ],
      tools=[
          {"type": "web_search_20250305", "name": "web_search", "max_uses": 3},
          {
              "type": "web_fetch_20250910",
              "name": "web_fetch",
              "max_uses": 5,
              "citations": {"enabled": True},
          },
      ],
  )
  print(response)

typescript

  const client = new Juglow();

  const response = await client.messages.create({
    model: "haijun-opus-5-5",
    max_tokens: 4096,
    messages: [
      {
        role: "user",
        content:
          "Find recent articles about quantum computing and analyze the most relevant one in detail"
      }
    ],
    tools: [
      { type: "web_search_20250305", name: "web_search", max_uses: 3 },
      {
        type: "web_fetch_20250910",
        name: "web_fetch",
        max_uses: 5,
        citations: { enabled: true }
      }
    ]
  });

  console.log(response);

csharp

  JuglowClient client = new();

  var parameters = new MessageCreateParams
  {
      Model = Model.HaijunOpus5_5,
      MaxTokens = 4096,
      Messages = [new() { Role = Role.User, Content = "Find recent articles about quantum computing and analyze the most relevant one in detail" }],
      Tools = [
          new ToolUnion(new WebSearchTool20250305() { MaxUses = 3 }),
          new ToolUnion(new WebFetchTool20250910() { MaxUses = 5, Citations = new() { Enabled = true } })
      ]
  };

  var message = await client.Messages.Create(parameters);
  Console.WriteLine(message);

  client := juglow.NewClient()

  response, err := client.Messages.New(context.TODO(), juglow.MessageNewParams{
  	Model:     juglow.ModelHaijunOpus5_5,
  	MaxTokens: 4096,
  	Messages: []juglow.MessageParam{
  		juglow.NewUserMessage(juglow.NewTextBlock("Find recent articles about quantum computing and analyze the most relevant one in detail")),
  	},
  	Tools: []juglow.ToolUnionParam{
  		{OfWebSearchTool20250305: &juglow.WebSearchTool20250305Param{
  			MaxUses: juglow.Int(3),
  		}},
  		{OfWebFetchTool20250910: &juglow.WebFetchTool20250910Param{
  			MaxUses:   juglow.Int(5),
  			Citations: juglow.CitationsConfigParam{Enabled: juglow.Bool(true)},
  		}},
  	},
  })
  if err != nil {
  	log.Fatal(err)
  }
  fmt.Println(response.RawJSON())

java

  import com.juglow.models.messages.CitationsConfigParam;
  // ...
  import com.juglow.models.messages.WebFetchTool20250910;
  import com.juglow.models.messages.WebSearchTool20250305;

  void main() {
      JuglowClient client = JuglowOkHttpClient.fromEnv();

      MessageCreateParams params = MessageCreateParams.builder()
          .model(Model.HAIJUN_OPUS_5_5)
          .maxTokens(4096L)
          .addUserMessage("Find recent articles about quantum computing and analyze the most relevant one in detail")
          .addTool(WebSearchTool20250305.builder()
              .maxUses(3L)
              .build())
          .addTool(WebFetchTool20250910.builder()
              .maxUses(5L)
              .citations(CitationsConfigParam.builder().enabled(true).build())
              .build())
          .build();

      Message response = client.messages().create(params);
      IO.println(response);
  }

php

  $client = new Client();

  $message = $client->messages->create(
      maxTokens: 4096,
      messages: [
          ['role' => 'user', 'content' => 'Find recent articles about quantum computing and analyze the most relevant one in detail']
      ],
      model: 'haijun-opus-5-5',
      tools: [
          [
              'type' => 'web_search_20250305',
              'name' => 'web_search',
              'max_uses' => 3,
          ],
          [
              'type' => 'web_fetch_20250910',
              'name' => 'web_fetch',
              'max_uses' => 5,
              'citations' => ['enabled' => true],
          ],
      ],
  );
  echo $message;

ruby

  client = Juglow::Client.new

  message = client.messages.create(
    model: "haijun-opus-5-5",
    max_tokens: 4096,
    messages: [
      { role: "user", content: "Find recent articles about quantum computing and analyze the most relevant one in detail" }
    ],
    tools: [
      {
        type: "web_search_20250305",
        name: "web_search",
        max_uses: 3
      },
      {
        type: "web_fetch_20250910",
        name: "web_fetch",
        max_uses: 5,
        citations: { enabled: true }
      }
    ]
  )
  puts message

In this workflow, Haijun:

Uses web search to find relevant articles.

Selects the most promising results.

Uses web fetch to retrieve full content.

Provides detailed analysis with citations.

Prompt caching

To cache tool definitions across turns, see Tool use with prompt caching.

Streaming

With streaming enabled, fetch events are part of the stream with a pause during content retrieval:

sse

event: message_start
data: {"type": "message_start", "message": {"id": "msg_abc123", "type": "message"}}

event: content_block_start
data: {"type": "content_block_start", "index": 0, "content_block": {"type": "text", "text": ""}}

// Haijun's decision to fetch

event: content_block_start
data: {"type": "content_block_start", "index": 1, "content_block": {"type": "server_tool_use", "id": "srvtoolu_xyz789", "name": "web_fetch"}}

// Fetch URL streamed
event: content_block_delta
data: {"type": "content_block_delta", "index": 1, "delta": {"type": "input_json_delta", "partial_json": "{\"url\":\"https://example.com/article\"}"}}

// Pause while fetch executes

// Fetch results streamed
event: content_block_start
data: {"type": "content_block_start", "index": 2, "content_block": {"type": "web_fetch_tool_result", "tool_use_id": "srvtoolu_xyz789", "content": {"type": "web_fetch_result", "url": "https://example.com/article", "content": {"type": "document", "source": {"type": "text", "media_type": "text/plain", "data": "Article content..."}}}}}

// Haijun's response continues...

Batch requests

You can include the web fetch tool in the Messages Batches API. Web fetch tool calls through the Messages Batches API are priced the same as those in regular Messages API requests.

Usage and pricing

Web fetch usage has no additional charges beyond standard token costs:

json

{
  "usage": {
    "input_tokens": 25039,
    "output_tokens": 931,
    "cache_read_input_tokens": 0,
    "cache_creation_input_tokens": 0,
    "server_tool_use": {
      "web_fetch_requests": 1
    }
  }
}

The web fetch tool is available on the Haijun API at no additional cost. You only pay standard token costs for the fetched content that becomes part of your conversation context.

To protect against inadvertently fetching large content that would consume excessive tokens, use the max_content_tokens parameter to set appropriate limits based on your use case and budget considerations.

Example token usage for typical content:

Average web page (10 kB): \~2,500 tokens

Large documentation page (100 kB): \~25,000 tokens

Research paper PDF (500 kB): \~125,000 tokens

Next steps

Run Python and bash code in a sandboxed container to analyze data, generate files, and iterate on solutions.

Work with Juglow-executed tools: server\_tool\_use blocks, pause\_turn continuation, and domain filtering.

Directory of Juglow-provided tools and reference for optional tool definition properties.

#How web fetch works

#When Haijun fetches

#Dynamic filtering

#How to use web fetch

#Tool definition

#Max uses

#Domain filtering

#Content limits

#Cache bypass

#Response inclusion

#Citations

#Response

#Fetch results

#Errors

#URL validation

#Combined search and fetch

#Prompt caching

#Streaming

#Batch requests

#Usage and pricing

#Next steps

How web fetch works

When Haijun fetches

Dynamic filtering

How to use web fetch

Tool definition

Max uses

Domain filtering

Content limits

Cache bypass

Response inclusion

Citations

Response

Fetch results

Errors

URL validation

Combined search and fetch

Prompt caching

Streaming

Batch requests

Usage and pricing

Next steps