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Anthropic

dateflagforproviderfields
2024-07-31[prompt-caching-2024-07-31]Prompt caching breakpointsA,B,V,F*.cache_control, usage.cache_creation_input_tokens, usage.cache_read_input_tokens
2024-09-24message-batches-2024-09-24Batch message processingArequests[*].custom_id, requests[*].params, processing_status, request_counts.*, results_url
2024-09-25pdfs-2024-09-25PDF document supportA,B,V,Fmessages[*].content[*].source.media_type, messages[*].content[*].source.data, messages[*].content[*].source.type
2024-10-22computer-use-2024-10-22Computer use tools (v1)A,B,V,Ftools[*].type:"computer_20241022", tools[*].display_width_px, tools[*].display_height_px, tools[*].display_number
2024-11-01token-counting-2024-11-01Token counting endpointA,B,V,Finput_tokens, cache_creation_input_tokens, cache_read_input_tokens
2025-01-24computer-use-2025-01-24Computer use tools (v2)A,B,V,Ftools[*].type:"computer_20250124", tools[*].display_width_px, tools[*].display_height_px
2025-02-19token-efficient-tools-2025-02-19Reduce tool definition tokensA,B,V,Ftools[*].defer_loading, tools[*].strict
2025-02-19output-128k-2025-02-19Extend max output to 128kA,B,V,Fmax_tokens (up to 128000)
2025-04-04mcp-client-2025-04-04MCP server integration (v1)A,B,V,Fmcp_servers[*].url, mcp_servers[*].tool_configuration, content[*].type:"mcp_tool_use", content[*].type:"mcp_tool_result"
2025-04-11extended-cache-ttl-2025-04-11Extended cache TTL to 1hA,B,V,F*.cache_control.ttl:"1h"
2025-04-14files-api-2025-04-14File upload/download APIA,B,V,Fsource.type:"file", source.file_id
2025-05-14dev-full-thinking-2025-05-14Full thinking content (dev)A,B,V,Fthinking.type:"enabled", thinking.budget_tokens, content[*].type:"thinking"
2025-05-14interleaved-thinking-2025-05-14Thinking interleaved with tool useA,B,V,Fcontent[*].type:"thinking" interleaved with content[*].type:"tool_use"
2025-05-22code-execution-2025-05-22Code execution sandboxA,B,V,Ftools[*].type:"code_execution_20250522", tools[*].allowed_callers, content[*].caller, content[*].type:"code_execution_tool_result"
2025-06-27[context-management-2025-06-27]Auto context managementA,B,V,Fcontext_management.edits[*], context_management.edits[*].trigger, context_management.edits[*].keep
2025-08-07[context-1m-2025-08-07]1M token context windowA,B,V,Fmax_tokens (model context extended to 1M)
2025-08-26[model-context-window-exceeded-2025-08-26]Context window exceeded stop reasonA,B,V,Fstop_reason:"model_context_window_exceeded"
2025-10-02[skills-2025-10-02]Skills/container supportA,B,V,Fcontainer.skills[*].id, container.skills[*].type, container.id
2025-11-20[mcp-client-2025-11-20]MCP server integration (v2)A,B,V,Fmcp_servers[*].url, mcp_servers[*].tool_configuration
2026-02-01fast-mode-2026-02-01Fast inference modeAspeed:"fast"

A=Anthropic API, B=Bedrock, V=Vertex AI, F=Foundry

  • prompt-caching-2024-07-31
    • 5m 缓存
    • 不再需要，默认启用，cache_control 控制
    • cache_control: {type: "ephemeral"}
  • extended-cache-ttl-2025-04-11
    • 1h 缓存
    • cache_control: {type: "ephemeral", ttl: "1h"}
anthropic-beta: A,B
# SSE, 去掉 data: [DONE], named event
anthropic-version: 2023-06-01
# 第一个版本
anthropic-version: 2023-01-01

output-128k-2025-02-19​

允许输出 128K

extended-cache-ttl-2025-04-11​

  • messages[*].content[*].cache_control.ephemeral.ttl

code-execution-2025-05-22​

curl https://api.anthropic.com/v1/messages \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "content-type: application/json" \
  -H "anthropic-version: 2023-06-01" \
  -H "anthropic-beta: code-execution-2025-05-22" \
  -d '{
      "model": "claude-sonnet-4-20250514",
      "max_tokens": 4096,
      "tools": [
        {
          "type": "code_execution_20250522",
          "name": "code_execution"
        }
      ],
      "messages": [
        {
          "role": "user",
          "content": "Calculate the first 20 Fibonacci numbers"
        }
      ]
    }'
curl https://api.anthropic.com/v1/messages \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "content-type: application/json" \
  -H "anthropic-version: 2023-06-01" \
  -H "anthropic-beta: code-execution-2025-05-22" \
  -d '{
      "model": "claude-sonnet-4-20250514",
      "max_tokens": 4096,
      "tools": [
        {
          "type": "code_execution_20250522",
          "name": "code_execution"
        },
        {
          "name": "get_stock_price",
          "description": "Get the current stock price for a given ticker symbol",
          "allowed_callers": ["direct", "code_execution_20250825"],
          "input_schema": {
            "type": "object",
            "properties": {
              "ticker": { "type": "string" }
            },
            "required": ["ticker"]
          }
        }
      ],
      "messages": [
        {
          "role": "user",
          "content": "Write Python code to fetch AAPL and GOOGL stock prices and calculate which one is more expensive"
        },
        {
          "role": "assistant",
          "content": [
            { "type": "text", "text": "I will write code to compare the stock prices." },
            {
              "type": "tool_use",
              "id": "toolu_code_01",
              "name": "code_execution",
              "input": { "code": "aapl = tool.get_stock_price(ticker='AAPL')" }
            },
            {
              "type": "tool_use",
              "id": "toolu_stock_01",
              "name": "get_stock_price",
              "input": { "ticker": "AAPL" },
              "caller": {
                "type": "code_execution_20250825",
                "tool_id": "toolu_code_01"
              }
            }
          ]
        },
        {
          "role": "user",
          "content": [
            {
              "type": "tool_result",
              "tool_use_id": "toolu_stock_01",
              "content": "{\"price\": 195.50, \"currency\": \"USD\"}"
            }
          ]
        }
      ]
    }'
用户请求
  ↓
模型发起 code_execution (toolu_code_01)
  ↓
sandbox 代码调用 get_stock_price → 产生 tool_use + caller
  ↓                                         ↑
  ↓                              caller.tool_id = "toolu_code_01"
  ↓                              caller.type = "code_execution_20250825"
  ↓
API 返回 stop_reason: "tool_use"，你处理 tool_result 回传
  ↓
模型拿到结果，sandbox 继续执行（或请求下一个工具）
  ↓
最终输出结果
  • allowed_callers：在 tools 定义中，控制谁可以调用 → 请求侧
  • caller：在 tool_use block 中，标识谁实际触发了调用 → 响应侧
  • 如果去掉 "code_execution_20250825" 只保留 ["direct"]，sandbox 代码就无权调用 get_stock_price，也不会出现带 caller 的 tool_use

context-management-2025-06-27​

mcp-client-2025-11-20​

  • mcp_toolset
  • default_config
{
  "model": "claude-opus-4-6",
  "max_tokens": 2048,
  "mcp_servers": [
    {
      "type": "url",
      "name": "database-server",
      "url": "https://mcp-db.example.com"
    }
  ],
  "tools": [
    {
      "type": "tool_search_tool_regex_20251119",
      "name": "tool_search_tool_regex"
    },
    {
      "type": "mcp_toolset",
      "mcp_server_name": "database-server",
      "default_config": {
        "defer_loading": true
      },
      "configs": {
        "search_events": {
          "defer_loading": false
        }
      }
    }
  ],
  "messages": [
    {
      "role": "user",
      "content": "What events are in my database?"
    }
  ]
}

advanced-tool-use-2025-11-20​

  • Claude API, Microsoft Foundry, 所有模型
  • 模型按需取搜索 tool
  • 工具可以定义 defer_loading: true
    • 只保留 name, description
    • 本质是 SDK 去做 RAG 相关逻辑，然后自动提供相关的检索
    • 有点类似 SKILL 的概念
    • 渐进式披露
  • API 返回 3-5 个 tool_reference
    • tool_search_tool_search_result
  • server_tool_use
    • 表示使用服务端 tool search
  • 也可以客户端实现 search_tools
  • 实现上千工具的使用
    • 高效准确的工具搜索
    • 字符匹配、语义、分类
    • 可用于支持 mcp-client-2025-11-20
      • 服务端动态发现工具来使用

请求

{
  "tools": [
    { "type": "tool_search_tool_regex_20251119", "name": "tool_search_tool_regex" },
    {
      "name": "github.createPullRequest",
      "description": "Create a pull request",
      "input_schema": {},
      "defer_loading": true
    }
  ]
}

响应

{
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "I'll search for tools to help with the weather information."
    },
    {
      "type": "server_tool_use",
      "id": "srvtoolu_01ABC123",
      "name": "tool_search_tool_regex",
      "input": {
        "query": "weather"
      }
    },
    {
      "type": "tool_search_tool_result",
      "tool_use_id": "srvtoolu_01ABC123",
      "content": {
        "type": "tool_search_tool_search_result",
        "tool_references": [{ "type": "tool_reference", "tool_name": "get_weather" }]
      }
    },
    {
      "type": "text",
      "text": "I found a weather tool. Let me get the weather for San Francisco."
    },
    {
      "type": "tool_use",
      "id": "toolu_01XYZ789",
      "name": "get_weather",
      "input": { "location": "San Francisco", "unit": "fahrenheit" }
    }
  ],
  "stop_reason": "tool_use"
}

tool-examples-2025-10-29​

  • Opus 4.5+
  • Vertex AI, Amazon Bedrock
  • input_examples 字段

fast-mode-2026-02-01​

  • 支持情况
    • Claude Opus 4.6
    • 2.5x 速度
    • 6x 价格
  • fast: boolean
curl https://api.anthropic.com/v1/messages \
  --header "x-api-key: $ANTHROPIC_API_KEY" \
  --header "anthropic-version: 2023-06-01" \
  --header "anthropic-beta: fast-mode-2026-02-01" \
  --header "content-type: application/json" \
  --data '{
        "model": "claude-opus-4-6",
        "max_tokens": 4096,
        "speed": "fast",
        "messages": [{
            "role": "user",
            "content": "Refactor this module to use dependency injection"
        }]
    }'

US-Only Inference​

  • 1.1x 价格
  • inference_geo: us

effort​

  • output: { effort: 'high' }
  • 支持 Opus 4.5, Opus 4.6
  • max, high, medium, low
  • 默认 high
  • Opus 4.6+ 替代以前的 budget_tokens

adaptive thinking​

  • Opus 4.6+
  • thinking.type=adaptive
  • 弃用 thinking: {type: "enabled", budget_tokens: N}
curl https://api.anthropic.com/v1/messages \
  --header "x-api-key: $ANTHROPIC_API_KEY" \
  --header "anthropic-version: 2023-06-01" \
  --header "content-type: application/json" \
  --data \
  '{
    "model": "claude-opus-4-6",
    "max_tokens": 16000,
    "thinking": {
        "type": "adaptive"
    },
    "messages": [
        {
            "role": "user",
            "content": "Explain why the sum of two even numbers is always even."
        }
    ]
}'
adaptive thinking is not supported on this model

Tool Use (Anthropic vs AWS Bedrock)​

1. tool_choice 参数格式差异​

  • Anthropic 原生 API：在根级别使用 tool_choice 对象配置，包含 type: "auto" | "any" | "tool"，开启时还需要 name 字段（如果 type 是 tool）。
    • 最近还引入了 tool_choice: {"type": "none"} 来彻底禁止走工具路径。
  • AWS Bedrock Converse API：参数名包裹在更加庞大的 toolConfig 对象内。它的名字变成了 toolConfig.toolChoice。
    • toolChoice 可选项对应也是 auto / any / tool。
    • 注意：Bedrock 历史上长期不支持 none，并且在同时开启 Thinking 模型模式（需要更多的上下文自由度）强行指定 tool 时，曾爆出 ValidationException（原生 API 目前在开启 Thinking 时仅支持 auto 甚至会忽略 specific tool choice）。

2. tool_reference (Tool Search 特性)​

参考 Beta 特性 advanced-tool-use-2025-11-20 (也即后期的 Tool Search 集成等)

你提到的 tool_reference 是 Anthropic 官方新推的 服务器端工具搜索 (Server-side Tool Search) / 延迟加载工具 (Deferred Loading) 特型。

  • 工作原理：当通过 API 传入上百数千个工具时，为了节约 token 并不全量载入，而是先将工具设置为 defer_loading: true。模型在思考时，第一步会去内部触发搜索这些工具，然后作为辅助的中间件向内容块（Content block）中吐出 type: "tool_reference" 块（包含了指代待用工具的索引，随后被系统填充转化为完整的 tool_use 供后续进行下层调用）。
  • 兼容性重灾区 (Bedrock 上的缺失)：
    • AWS Bedrock 的 Converse API 不原生支持 响应体里解析或发出这种特有的 tool_reference block。
    • 从开源的 claude-code 与 Bedrock 的兼容性反馈 issue 中能看见：如果在 Bedrock 开启这个高级工具特性跑 Claude，当模型返回 tool_reference 时，由于 Bedrock Converse API 严格的 JSON Schema 白名单限制校验，这个 block 会直接引发 API 级别的拒绝错误。
    • 虽然 Bedrock 的低级 InvokeModel API 可能通过透传绕过一些验证，但在标准 Converse API 工作流下，原生高级工具搜索（Tool Reference）是不被支持的。