Unified Function/Tool Calling

⚠️ Pending Update · 2026-08-29 Verification · Content may be outdated, please refer to official docs Updated: 2026-08-29 · Status: Pending Verification

Background

OpenAI uses tools + tool_calls + function as three layers; Anthropic uses tools + tool_use block + tool_result pairing; Gemini uses function_declarations + functionCall + functionResponse. For the same agent code to run across models, the gateway must translate all three into one shape.

Unified Abstraction

• Input normalization: the client sends tools in OpenAI shape; the gateway translates to each vendor's native format.
• Output normalization: Anthropic's tool_use block and Gemini's functionCall both become OpenAI's tool_calls array.
• Multi-turn normalization: regardless of vendor, the second-turn tool role message is translated into the corresponding vendor's "tool result" format (Anthropic tool_result, Gemini functionResponse).
• Degradation for tool-less models: models like llama-3.1-8b that lack native tools can be "simulated" by the gateway via prompt injection + JSON parsing, at the cost of lower accuracy.

Code Example

def openai_tools_to_anthropic(tools):
    return [{
        "name": t["function"]["name"],
        "description": t["function"]["description"],
        "input_schema": t["function"]["parameters"],
    } for t in tools]

def anthropic_response_to_openai(resp):
    tool_calls = []
    for i, block in enumerate(resp["content"]):
        if block["type"] == "tool_use":
            tool_calls.append({
                "id": block["id"],
                "type": "function",
                "function": {
                    "name": block["name"],
                    "arguments": json.dumps(block["input"]),
                },
            })
    return {
        "role": "assistant",
        "content": None,
        "tool_calls": tool_calls,
    }

def openai_tool_result_to_anthropic(tool_call_id, output):
    return {
        "role": "user",
        "content": [{"type": "tool_result",
                     "tool_use_id": tool_call_id,
                     "content": output}],
    }

Schema Evolution and Versioning

The tools protocol evolves: a tool's parameters shift from {"url": string} to {"url": string, "method": string}. The client SDK upgrades, but the model may still describe the old schema. The gateway needs schema version negotiation: each tool carries a schema_version, and when the gateway sees the client-declared version in the request, it auto-selects the matching schema to inject into the tools field. It also maintains a version compatibility table so old-version tool calls still execute under the new schema by filling new fields with defaults.

Best Practices

• Schema validation: validate tools definitions at the gateway ingress with JSON Schema so bad schemas never waste an upstream call.
• Parallel tool calls: both OpenAI and Anthropic support parallel calls, but Gemini allows only one at a time — the gateway must cap to the strictest semantics.
• Error pass-through: when a tool execution fails, return {"error": "..."} to the model rather than raising; the model then gets a chance to retry or pick another tool.
• Fallback prompt template: for models without tools support, serialize the tool list into the system message and parse the model's reply as JSON.
• Tool blocklist: maintain a per-client_id disabled-tool list to block dangerous tools (e.g. file deletion).

Unifying the tools protocol lets an agent framework be written once and run across every model.

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