Tool calling backend for LLM function calling
LLM function calling and tool use need a real backend: authenticated HTTP endpoints the model can call, isolated execution per tool, secrets kept off the model path, async jobs for tools that take longer than a round-trip, and traces for every invocation.
Last updated: 2026-04-20
Answer first
Direct answer
Tool calling backend for LLM function calling. Each function call definition maps to an Inquir gateway route. The model gets a structured tool manifest; when it requests a call, your orchestrator POSTs to the gateway with an API key. The function handler runs in isolation, injects only the secrets it needs, and returns structured JSON.
When it fits
- Production LLM apps using OpenAI function calling, Anthropic tool use, or Gemini function declarations
- Tools that access private data, call external APIs, or have side effects
Tradeoffs
- Inline tool execution inside the LLM loop: no isolation, no retry, no observability per tool.
- Open HTTP endpoints without auth: any caller can invoke tools, not just your model pipeline.
- Secrets in environment variables shared across all tools: rotating one breaks everything.
Workload and what breaks
Why function calling needs a real backend
LLM function calling (OpenAI, Anthropic, Gemini) lets the model request tool invocations. In demos, these run locally alongside the model loop. In production, tool functions need authentication, secret injection, rate limiting, execution tracing, and async handling for steps that take longer than a round-trip.
Without a real backend, every tool call is an open function running with the same privileges as everything else. A compromised model context can exfiltrate secrets or trigger unintended side effects.
Trade-offs
Common tool calling anti-patterns
Inline tool execution inside the LLM loop: no isolation, no retry, no observability per tool.
Open HTTP endpoints without auth: any caller can invoke tools, not just your model pipeline.
Secrets in environment variables shared across all tools: rotating one breaks everything.
How Inquir helps
Serverless functions as the tool calling layer
Each function call definition maps to an Inquir gateway route. The model gets a structured tool manifest; when it requests a call, your orchestrator POSTs to the gateway with an API key. The function handler runs in isolation, injects only the secrets it needs, and returns structured JSON.
For tools that need async work—web scraping, database bulk reads, ML inference—the HTTP handler returns a job reference immediately and a pipeline continues the work. The orchestrator polls or receives a callback when the tool result is ready.
What you get
Tool calling backend patterns
Synchronous tool calls
Fast tools (lookup, calculate, format) return results inline within the model round-trip window.
Async tool calls with job reference
Slow tools return a jobId; orchestrator polls or waits for callback. Model continues planning while tool works in background.
Tool result caching
Cache deterministic tool results at the gateway or function level—reduce cost and latency for repeated model calls with identical inputs.
Tool call tracing
Every tool invocation creates an execution record: input, output, duration, error. Correlate with model call IDs for end-to-end traces.
What to do next
How to implement a tool calling backend
Define tool schema
Write the OpenAI/Anthropic function definition schema. Each tool name maps to a gateway route.
Implement and deploy handlers
One function per tool. Validate input, call external systems with scoped secrets, return structured JSON.
Wire orchestrator to gateway
Orchestrator POSTs to gateway routes with API key. Gateway enforces auth before handler code runs.
Code example
Tool calling flow: schema to handler
The model sees a function definition; your orchestrator maps it to a gateway route; the gateway enforces auth; the handler runs in isolation.
{ "type": "function", "function": { "name": "search_customer", "description": "Look up a customer by ID or email", "parameters": { "type": "object", "properties": { "query": { "type": "string", "description": "Customer ID or email" } }, "required": ["query"] } } }
export async function handler(event) { // Auth enforced at gateway — API key checked before this code runs const { query } = JSON.parse(event.body || '{}'); if (!query) return { statusCode: 400, body: JSON.stringify({ error: 'query required' }) }; const results = await db.customers.search(query); // DB_URL from workspace secrets return { statusCode: 200, body: JSON.stringify({ customers: results }) }; }
When it fits
When you need a tool calling backend
When this works
- Production LLM apps using OpenAI function calling, Anthropic tool use, or Gemini function declarations
- Tools that access private data, call external APIs, or have side effects
When to skip it
- Purely local tool execution in demos with no auth or secret requirements
FAQ
FAQ
How do I handle tool call errors?
Return a structured error JSON from the tool handler. Pass the error back to the model as a tool result—let the model decide whether to retry, ask for clarification, or give up.
Can tools call other tools?
Yes—one tool handler can trigger another pipeline step. Keep the orchestrator in charge of the overall call graph to avoid infinite loops.