Claude Tool Use 🛠️

Anthropic’s Claude is a closed, hosted model family; there are no released weights and therefore no --tool-call-parser flag to set. The canonical tool-calling convention is the Messages API (POST /v1/messages, header anthropic-version: 2023-06-01): tools are advertised in a top-level tools array, the model returns structured tool_use content blocks with stop_reason: "tool_use", and you feed results back as tool_result content blocks inside a user message. Tool use is “enabled” simply by including the tools parameter (optionally with tool_choice); the API then injects a tool-use system prompt and parses the model’s output back into JSON blocks for you. This applies to all current models (Claude Opus / Sonnet / Haiku 3.x, 4, 4.x) and is mirrored by gateways such as LiteLLM and by third-party Claude-compatible servers.

Under the hood the model is trained to emit an XML function-call syntax (<function_calls> / <invoke> / <parameter>); the API serializes your JSON-Schema tools into a system prompt and converts the model’s XML output into JSON tool_use blocks. That underlying format is documented as the secondary convention below, together with the older, now-retired prompt-based legacy XML format (<tool_name> / <parameters> / <function_results>) that pre-dates the Messages API and still surfaces when you do tool use purely through prompting.

The primary, authoritative shape for any parser/renderer is the JSON content-block format. The XML is informational (and the only thing visible if you reconstruct prompts at the token level).

Content-block types & stop reasons

Anthropic has no token-level tool delimiters in the public API. The unit is the content block: every message.content is an array of typed blocks. Tool calling adds two block types and one stop reason; streaming adds a delta type.

Item	Where	Shape / meaning
`text` block	assistant & user	`{"type":"text","text":"..."}`. Plain prose. Assistant may emit text before its tool calls.
`tool_use` block	assistant	`{"type":"tool_use","id":"toolu_...","name":"<tool>","input":{...}}`. The function call. `input` is a nested JSON object (already parsed), conforming to the tool’s `input_schema`.
`tool_result` block	user	`{"type":"tool_result","tool_use_id":"toolu_...","content":<string \| block[]>,"is_error":<bool?>}`. The executed result, sent back in a `user` message.
`server_tool_use` block	assistant	`{"type":"server_tool_use","id":"srvtoolu_...","name":"web_search","input":{...}}`. Emitted for Anthropic-executed server tools; you do not return a `tool_result` for these.
`web_search_tool_result` (and similar)	assistant	Server-tool output, injected by Anthropic inline in the assistant turn.
`thinking` / `redacted_thinking` block	assistant	Extended-thinking reasoning blocks; carry a `signature`. Must be preserved verbatim across turns when thinking + tools are combined.
`stop_reason: "tool_use"`	response top level	The model invoked one or more tools and is waiting for results. Drives the agentic loop.
`stop_reason: "end_turn"`	response top level	Natural completion (no tool call); the loop exits.
Other `stop_reason`	response top level	`"max_tokens"`, `"stop_sequence"`, `"pause_turn"` (long server-tool turn, resend as-is to continue), `"refusal"`, `"sensitive"` (output flagged by safety filters), `"model_context_window_exceeded"` (output truncated at the context window, treated like `max_tokens`).
`id` prefixes	—	Messages `msg_…`; client tool calls `toolu_…`; server tool calls `srvtoolu_…`.

Streaming adds these SSE events / delta types (full list under Roles / channels and Tool-call format):

Streaming item	Shape / meaning
`message_start`	Carries a `Message` skeleton with empty `content`, `stop_reason: null`.
`content_block_start`	Opens a block at `index`. For a tool call: `content_block.{type:"tool_use",id,name,input:{}}` — `input` starts as an empty object.
`content_block_delta` / `input_json_delta`	`{"type":"input_json_delta","partial_json":"<chunk>"}` — a partial JSON string fragment of `tool_use.input`.
`content_block_delta` / `text_delta`	`{"type":"text_delta","text":"..."}`.
`content_block_delta` / `thinking_delta`, `signature_delta`	Extended-thinking content / signature.
`content_block_stop`	Closes the block at `index`; this is when accumulated `partial_json` is complete and safe to `JSON.parse`.
`message_delta`	Top-level updates; carries the final `delta.stop_reason` (e.g. `"tool_use"`) and cumulative `usage`.
`message_stop`	End of stream.
`ping` / `error`	Keep-alive; `error` (e.g. `overloaded_error`) may appear mid-stream.

Legacy XML tags (prompt-based, pre-Messages-API)

The retired prompt-based format used these tags. They are nested-element tags (no attributes), distinct from the modern attribute form (<invoke name="…">). Verified against Anthropic’s archived “Legacy tool use” doc (see Sources).

Tag	Role	Notes
`<tools>` … `</tools>`	tool advertising	Container in the system prompt wrapping all `<tool_description>` entries.
`<tool_description>`	tool advertising	One per tool: holds `<tool_name>`, `<description>`, `<parameters>`.
`<tool_name>`	both	Function name (used in definitions, calls, and results).
`<parameters>` / `<parameter>`	definition	`<parameters>` wraps `<parameter>` entries, each with `<name>`, `<type>`, `<description>`.
`<function_calls>`	model output	Wraps one or more `<invoke>` blocks.
`<invoke>`	model output	One function call; contains `<tool_name>` + a `<parameters>` block of `<paramName>value</paramName>` child tags.
`<function_results>`	tool result (fed back)	Wraps `<result>` (success) or `<error>` (failure).
`<result>` / `<stdout>`	tool result	`<result>` holds `<tool_name>` + `<stdout>`; the output text goes in `<stdout>`.
`<error>`	tool result	Replaces `<result>` when the function raised.
`</function_calls>`	stop sequence	Passed as `stop_sequence` so generation halts after a call.
`<scratchpad>` / `<answer>`	model output	Conventionally used for chain-of-thought and final answer in legacy prompts.

Roles / channels / turn structure

The Messages API uses primarily two conversational roles, user and assistant, alternating. There is no dedicated tool/function role, and the standard system prompt is a separate top-level system parameter (string or text-block array) — not a message role. (Claude Opus 4.8+ and the Fable/Mythos 5 generation additionally accept an opt-in mid-conversation system message role, gated behind the mid-conversation-system-2026-04-07 beta; otherwise only user/assistant are valid.) Tool data rides inside the normal roles:

assistant messages contain AI-generated text, thinking, and tool_use (and server_tool_use) blocks.
user messages contain your text/image/document content and tool_result blocks.

There are no named “channels”. The closest analogue to a reasoning channel is the extended-thinking thinking content block (a first-class block with a cryptographic signature), kept separate from the user-visible text block. When thinking is enabled alongside tools, the thinking block(s) from a tool-calling turn must be passed back unmodified in the follow-up request.

The agentic loop is keyed on stop_reason:

Send tools + the user message.
Claude responds with stop_reason: "tool_use" and one or more tool_use blocks (optionally preceded by a text block).
Execute each tool; build a tool_result block per call.
Append the assistant message and a user message carrying all tool_result blocks; resend.
Repeat while stop_reason == "tool_use"; exit on end_turn (or another terminal reason).

Strict ordering rules (a 400 otherwise):

tool_result blocks must come first in the user message’s content array (any text after them).
The tool_result user message must immediately follow the assistant tool_use message — nothing in between.
Every tool_use.id must be answered by a tool_result.tool_use_id in that next message.

Tool definitions

Tools are passed in the top-level tools array. Each user-defined (client) tool is a flat object — no {"type":"function", "function":{…}} wrapper (that wrapper is OpenAI’s). Fields:

name — matches ^[a-zA-Z0-9_-]{1,64}$.
description — detailed plaintext (the single biggest driver of tool-call quality).
input_schema — a JSON Schema object (not parameters) describing the input the model must produce.
Optional: cache_control (prompt-cache breakpoint), strict (structured-outputs beta), eager_input_streaming (fine-grained tool-streaming beta).

{
  "name": "get_weather",
  "description": "Get the current weather in a given location",
  "input_schema": {
    "type": "object",
    "properties": {
      "location": {
        "type": "string",
        "description": "The city and state, e.g. San Francisco, CA"
      },
      "unit": {
        "type": "string",
        "enum": ["celsius", "fahrenheit"],
        "description": "The unit of temperature, either 'celsius' or 'fahrenheit'"
      }
    },
    "required": ["location"]
  }
}

Anthropic-schema client tools (bash, text_editor, computer, memory) and server tools (web_search, web_fetch, code_execution, tool_search) instead carry a versioned type, e.g. {"type": "web_search_20250305", "name": "web_search"}.

tool_choice controls invocation (four options):

{"type":"auto"} — model decides (default when tools present).
{"type":"any"} — must call some tool.
{"type":"tool","name":"get_weather"} — must call that specific tool.
{"type":"none"} — no tools (default when no tools).

With any or tool the API prefills the assistant turn, so no leading natural-language text precedes the tool_use block. Add "disable_parallel_tool_use": true inside tool_choice to cap at one tool per turn. (Extended thinking only supports auto/none.)

How the API turns this into a prompt (the bridge to XML)

When tools is present, the API constructs a tool-use system prompt with this skeleton (verified from “Define tools”):

In this environment you have access to a set of tools you can use to answer the user's question.
{{ FORMATTING INSTRUCTIONS }}
String and scalar parameters should be specified as is, while lists and objects should use JSON format. Note that spaces for string values are not stripped. The output is not expected to be valid XML and is parsed with regular expressions.
Here are the functions available in JSONSchema format:
{{ TOOL DEFINITIONS IN JSON SCHEMA }}
{{ USER SYSTEM PROMPT }}
{{ TOOL CONFIGURATION }}

{{ TOOL DEFINITIONS IN JSON SCHEMA }} is your tools array serialized to JSON Schema. {{ FORMATTING INSTRUCTIONS }} is the (unpublished) block teaching the model the XML syntax with antml: namespace prefixes (shown under Tool-call format → underlying XML). The note “parsed with regular expressions” is why output need not be well-formed XML.

Tool-call format

The wire format your application consumes is JSON. A single call is one tool_use content block in the assistant message, with stop_reason: "tool_use" at the top level:

{
  "id": "msg_01Aq9w938a90dw8q",
  "type": "message",
  "role": "assistant",
  "model": "claude-opus-4-8",
  "content": [
    {
      "type": "text",
      "text": "I'll check the current weather in San Francisco for you."
    },
    {
      "type": "tool_use",
      "id": "toolu_01A09q90qw90lq917835lq9",
      "name": "get_weather",
      "input": { "location": "San Francisco, CA", "unit": "celsius" }
    }
  ],
  "stop_reason": "tool_use",
  "stop_sequence": null,
  "usage": { "input_tokens": 472, "output_tokens": 65 }
}

Key facts for a parser:

tool_use.input is an already-parsed object, never a JSON string.
A leading text block is optional and informational; do not rely on its wording.
Match calls to results by id → tool_use_id.

Underlying XML (modern attribute form with antml: namespace)

Before the API converts it, the model literally emits an XML block. The current (Claude 3+) form is attribute-based:

<function_calls>
<invoke name="get_weather">
<parameter name="location">San Francisco, CA</parameter>
<parameter name="unit">celsius</parameter>
</invoke>
</function_calls>

Current Claude models prefix these tags with an antml: XML namespace prefix (e.g. antml:function_calls, antml:invoke name="…", antml:parameter name="…"). The API strips all of this and exposes only the JSON tool_use block; integrators should target the JSON, not the XML.

Multiple / parallel tool calls

Parallel calls are the default. Claude emits multiple tool_use blocks in a single assistant message:

{
  "role": "assistant",
  "content": [
    { "type": "text", "text": "Let me check both cities." },
    {
      "type": "tool_use",
      "id": "toolu_01weather_sf",
      "name": "get_weather",
      "input": { "location": "San Francisco, CA" }
    },
    {
      "type": "tool_use",
      "id": "toolu_02weather_nyc",
      "name": "get_weather",
      "input": { "location": "New York, NY" }
    }
  ]
}

You return all results in one user message, one tool_result per call, results first:

{
  "role": "user",
  "content": [
    {
      "type": "tool_result",
      "tool_use_id": "toolu_01weather_sf",
      "content": "San Francisco: 68F, partly cloudy"
    },
    {
      "type": "tool_result",
      "tool_use_id": "toolu_02weather_nyc",
      "content": "New York: 45F, clear skies"
    }
  ]
}

Calls in one turn are unordered and may be run concurrently. If two batched calls turn out to depend on each other, return the natural error in a tool_result with "is_error": true; Claude reissues the dependent call on a later turn. (In the legacy XML format, parallelism is multiple <invoke> blocks inside one <function_calls>.)

Tool-result format

A result is a tool_result block inside a user message:

tool_use_id (required) — the id of the tool_use it answers.
content (optional) — a string, or an array of text/image/document blocks. Omit for an empty result.
is_error (optional) — true for execution failures; put a useful message in content.

{
  "role": "user",
  "content": [
    {
      "type": "tool_result",
      "tool_use_id": "toolu_01A09q90qw90lq917835lq9",
      "content": "15 degrees"
    }
  ]
}

Error result:

{
  "role": "user",
  "content": [
    {
      "type": "tool_result",
      "tool_use_id": "toolu_01A09q90qw90lq917835lq9",
      "content": "ConnectionError: the weather service API is not available (HTTP 500)",
      "is_error": true
    }
  ]
}

Rich result (text + image blocks):

{
  "role": "user",
  "content": [
    {
      "type": "tool_result",
      "tool_use_id": "toolu_01A09q90qw90lq917835lq9",
      "content": [
        { "type": "text", "text": "15 degrees" },
        {
          "type": "image",
          "source": { "type": "base64", "media_type": "image/jpeg", "data": "/9j/4AAQSkZJRg..." }
        }
      ]
    }
  ]
}

Server tools require no tool_result from you — Anthropic executes them and injects the result inline in the assistant turn. (Legacy XML feeds results back as <function_results><result><tool_name>…</tool_name><stdout>…</stdout></result></function_results>, or <error>…</error> on failure.)

End-to-end example

A complete multi-turn weather exchange. All JSON is valid.

Request 1 — system + tools + user question:

{
  "model": "claude-opus-4-8",
  "max_tokens": 1024,
  "system": "You are a helpful weather assistant. Use the provided tools to answer.",
  "tools": [
    {
      "name": "get_weather",
      "description": "Get the current weather in a given location",
      "input_schema": {
        "type": "object",
        "properties": {
          "location": {
            "type": "string",
            "description": "The city and state, e.g. San Francisco, CA"
          },
          "unit": {
            "type": "string",
            "enum": ["celsius", "fahrenheit"],
            "description": "Unit for the temperature"
          }
        },
        "required": ["location"]
      }
    }
  ],
  "messages": [{ "role": "user", "content": "What's the weather in San Francisco?" }]
}

Response 1 — assistant requests the tool (stop_reason: "tool_use"):

{
  "id": "msg_01Aq9w938a90dw8q",
  "type": "message",
  "role": "assistant",
  "model": "claude-opus-4-8",
  "content": [
    { "type": "text", "text": "I'll check the current weather in San Francisco for you." },
    {
      "type": "tool_use",
      "id": "toolu_01A09q90qw90lq917835lq9",
      "name": "get_weather",
      "input": { "location": "San Francisco, CA", "unit": "celsius" }
    }
  ],
  "stop_reason": "tool_use",
  "stop_sequence": null,
  "usage": { "input_tokens": 472, "output_tokens": 65 }
}

Request 2 — replay history, append the assistant turn and the tool_result:

{
  "model": "claude-opus-4-8",
  "max_tokens": 1024,
  "system": "You are a helpful weather assistant. Use the provided tools to answer.",
  "tools": [
    {
      "name": "get_weather",
      "description": "Get the current weather in a given location",
      "input_schema": {
        "type": "object",
        "properties": {
          "location": {
            "type": "string",
            "description": "The city and state, e.g. San Francisco, CA"
          },
          "unit": {
            "type": "string",
            "enum": ["celsius", "fahrenheit"],
            "description": "Unit for the temperature"
          }
        },
        "required": ["location"]
      }
    }
  ],
  "messages": [
    { "role": "user", "content": "What's the weather in San Francisco?" },
    {
      "role": "assistant",
      "content": [
        { "type": "text", "text": "I'll check the current weather in San Francisco for you." },
        {
          "type": "tool_use",
          "id": "toolu_01A09q90qw90lq917835lq9",
          "name": "get_weather",
          "input": { "location": "San Francisco, CA", "unit": "celsius" }
        }
      ]
    },
    {
      "role": "user",
      "content": [
        {
          "type": "tool_result",
          "tool_use_id": "toolu_01A09q90qw90lq917835lq9",
          "content": "15 degrees Celsius, partly cloudy"
        }
      ]
    }
  ]
}

Response 2 — assistant’s final answer (stop_reason: "end_turn"):

{
  "id": "msg_01EeFG3hijk2lmno4PqrSt",
  "type": "message",
  "role": "assistant",
  "model": "claude-opus-4-8",
  "content": [
    {
      "type": "text",
      "text": "It's currently 15 degrees Celsius and partly cloudy in San Francisco."
    }
  ],
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "usage": { "input_tokens": 530, "output_tokens": 18 }
}

Streaming (SSE) shape of the tool call

The same tool call, streamed. Note tool_use opens with an empty input, the arguments arrive as input_json_delta.partial_json fragments, and the final stop_reason lands in message_delta. This block is reproduced verbatim from Anthropic’s streaming docs:

event: message_start
data: {"type":"message_start","message":{"id":"msg_014p7gG3wDgGV9EUtLvnow3U","type":"message","role":"assistant","model":"claude-opus-4-8","stop_sequence":null,"usage":{"input_tokens":472,"output_tokens":2},"content":[],"stop_reason":null}}
 
event: content_block_start
data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""}}
 
event: ping
data: {"type": "ping"}
 
event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"Okay"}}
 
event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":" let"}}
 
event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"'s"}}
 
event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":" check"}}
 
event: content_block_stop
data: {"type":"content_block_stop","index":0}
 
event: content_block_start
data: {"type":"content_block_start","index":1,"content_block":{"type":"tool_use","id":"toolu_01T1x1fJ34qAmk2tNTrN7Up6","name":"get_weather","input":{}}}
 
event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":""}}
 
event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":"{\"location\":"}}
 
event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":" \"San"}}
 
event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":" Francisc"}}
 
event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":"o,"}}
 
event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":" CA\"}"}}
 
event: content_block_stop
data: {"type":"content_block_stop","index":1}
 
event: message_delta
data: {"type":"message_delta","delta":{"stop_reason":"tool_use","stop_sequence":null},"usage":{"output_tokens":89}}
 
event: message_stop
data: {"type":"message_stop"}

Reassembly: concatenate every partial_json for a given index ("" + "{\"location\":" + " \"San" + " Francisc" + "o," + " CA\"}" → {"location": "San Francisco, CA"}), then JSON.parse at that block’s content_block_stop. Tool use also supports fine-grained streaming (eager_input_streaming per tool) for finer partial_json chunking.

OpenAI-compatible API mapping

Anthropic integrates tools into the user/assistant message structure rather than using OpenAI’s separate tool role and function wrapper. Field-by-field:

Concept	Anthropic Messages API	OpenAI Chat Completions
Tool definition wrapper	flat `{"name","description","input_schema"}` in `tools[]`	`{"type":"function","function":{"name","description","parameters"}}` in `tools[]`
Tool schema key	`input_schema` (JSON Schema)	`parameters` (JSON Schema)
“Must call a tool”	`tool_choice:{"type":"any"}` / `{"type":"tool","name":…}`	`tool_choice:"required"` / `{"type":"function","function":{"name":…}}`
Disable parallel calls	`tool_choice:{…,"disable_parallel_tool_use":true}`	`parallel_tool_calls:false` (top level)
Assistant call container	`tool_use` content block in `content[]`	`tool_calls[]` on the assistant `message`
Call id	`tool_use.id` = `toolu_…`	`tool_calls[].id` = `call_…`
Function name	`tool_use.name`	`tool_calls[].function.name`
Function arguments	`tool_use.input` = nested JSON object (parsed)	`tool_calls[].function.arguments` = JSON string (must `JSON.parse`)
“Tools were called” signal	`stop_reason:"tool_use"`	`finish_reason:"tool_calls"`
Result message role	`user` message containing `tool_result` block(s)	dedicated `{"role":"tool",…}` message(s)
Result ↔ call linkage	`tool_result.tool_use_id`	`tool` message `tool_call_id`
Result payload	`tool_result.content` = string or block array (text/image/document)	`tool` message `content` = string
Error result	`tool_result` with `is_error:true`	no dedicated flag; encode in `content`
System prompt	top-level `system` param (no `system` role)	`{"role":"system",…}` message
Streamed args	`input_json_delta.partial_json` fragments	`tool_calls[].function.arguments` string deltas

Conversion gotchas:

Object vs string: to emit OpenAI shape, JSON.stringify(tool_use.input); to consume OpenAI shape into Anthropic, JSON.parse(arguments).
Role reshaping: collapse N OpenAI tool messages into one Anthropic user message of N tool_result blocks (order them before any text), and vice-versa.
No type:"function" wrapper on Anthropic custom tools; add/remove it when translating.
Id prefixes differ (toolu_ vs call_); never assume one format’s id is valid in the other.

Parsing notes & gotchas

input is an object, not a string. Unlike OpenAI’s arguments, do not JSON.parse tool_use.input from a non-streamed response — it is already an object. Only the streaming partial_json fragments are strings.
Streaming tool args need reassembly. content_block_start for a tool_use always has input: {}. Buffer partial_json per index and parse only at content_block_stop; mid-stream fragments are not valid JSON on their own (e.g. {"location":). Current models emit one complete key/value at a time, so expect bursts and gaps.
stop_reason placement. In streaming, stop_reason is null in message_start and final value ("tool_use"/"end_turn") arrives in message_delta, not message_stop. usage in message_delta is cumulative.
Ordering is enforced. tool_result blocks must be first in their user message and must immediately follow the assistant tool_use message; every tool_use.id needs a matching tool_result.tool_use_id, or you get HTTP 400 (“tool_use ids were found without tool_result blocks immediately after”).
tool_choice:any/tool suppress preamble. The API prefills the assistant turn, so no leading text block appears before tool_use — don’t write a parser that expects explanatory text.
Parallel results in one message. Splitting parallel tool_results across multiple user messages breaks the contract; send them together.
Treat result content as untrusted. Tool results can carry indirect prompt injection; keep them inside tool_result blocks, never promote to system/user text.
Server tools differ. server_tool_use / web_search_tool_result blocks are produced and consumed by Anthropic; never synthesize tool_result for them. stop_reason:"pause_turn" means resend the response as-is to let a long server-tool turn continue.
Extended thinking + tools. Preserve thinking/redacted_thinking blocks (with their signature) verbatim across turns; forced tool_choice (any/tool) is rejected when thinking is on.
Output is not valid XML. The underlying model output is parsed by Anthropic with regular expressions, not an XML parser (“The output is not expected to be valid XML”). If you reconstruct prompts at token level, do not assume well-formedness; rely on the JSON the API returns.
Legacy vs modern XML are different tag sets. Legacy: <invoke> + child <tool_name> + <parameters> with per-name child tags; results in <function_results>/<result>/<stdout>. Modern: <invoke name="…"> + <parameter name="…">. Mixing them up will misparse. The legacy format also required passing </function_calls> as a stop_sequence and is not optimized for Claude 3+.

Legacy XML format (secondary, prompt-based — fully verified, now retired)

Before the Messages API, tools were defined and called entirely in the prompt. Anthropic’s archived “Legacy tool use” doc specifies it verbatim.

Tool definition (inside a <tools> block in the system prompt):

<tool_description>
<tool_name>get_weather</tool_name>
<description>
Retrieves the current weather for a specified location.
Returns a dictionary with two fields:
- temperature: float, the current temperature in Fahrenheit
- conditions: string, a brief description of the current weather conditions
Raises ValueError if the provided location cannot be found.
</description>
<parameters>
<parameter>
<name>location</name>
<type>string</type>
<description>The city and state, e.g. San Francisco, CA</description>
</parameter>
</parameters>
</tool_description>

Model-emitted call (multiple <invoke> for parallel calls; pass </function_calls> as a stop_sequence):

<function_calls>
<invoke>
<tool_name>get_weather</tool_name>
<parameters>
<location>San Francisco, CA</location>
</parameters>
</invoke>
</function_calls>

Result fed back into the next user turn:

<function_results>
<result>
<tool_name>get_weather</tool_name>
<stdout>
59 degrees Fahrenheit, partly cloudy
</stdout>
</result>
</function_results>