A strange thing happens when software starts writing its own requests.
The first time you watch an LLM autonomously call your API, constructing a structured payload, firing it off, and merging the response back into it’s reasoning you realise the interface was built for humans: flexible, forgiving, and full of “you probably meant” heuristics.
Agents don’t guess. They don’t skim or retry with a teak. They operate in a world of precision, where one malformed parameter breaks the chain of thought. The LLM stack is shifting from “autocomplete for humans” to a cognitive layer that expects every integration to be lossless, deterministic, and token-aligned.
At the heart of this shift is tool calling an LLM’s built in instinct to delegate. When it encounters a task outside its scope (real-time data, math, transactions), it emits a structured function call, awaits the result, and threads it into it’s reasoning - all in a single flow.
That’s not a new interface pattern; it’s a new runtime contract.
In this post, we’ll show why traditional APIs crumble under autonomous usage, how tool calling reframes interface design, and how Valyu v2 makes your endpoints machine-native. From schema design to execution loops, we’ll unpack what it takes to be truly agent-ready.
What is Tool Calling?
Tool calling is an LLM’s way of offloading, any task that text prediction can’t solve deterministically.
If a prompt needs real-time data- e.g. “What’s Tesla’s stock price right now?” the model doesn’t guess. It emits a schema-perfect function call get_stock_price({"ticker":"TSLA"}) and pauses. A market-data API returns the exact price in a compact JSON payload, which the model immediately folds back into its answer before continuing.
The cycle is quick and repeatable:
- Detection — The LLM spots that free-text generation would risk hallucination.
- Delegation — It produces a deterministic API request, free of ambiguity.
- Retrieval — The downstream service responds with machine-readable facts.
- Synthesis — The model stitches those facts into its reasoning stream.
Why does this matter? Tool calling fuses LLM heuristics with API-grade accuracy, delivering three compounding wins:
- Enhanced precision — Verified data replaces probabilistic guesses
- Operational efficiency — External services shoulder compute-heavy tasks, trimming token usage
- Scalable delegation — One agent can orchestrate hundreds of calls without ballooning context windows or retry loops.
These gains force a rethink of API design: schemas must be lossless, endpoints deterministic, and error surfaces explicit.
Human-Centric vs. Machine-Native API Design
Human-centric APIs were built for developer eyeballs. They prioritise forgiving schemas, extensive documentation, and prose-style error messages that help humans debug. Flexibility is a feature: optional fields, nested objects, and varied response shapes are tolerated because a person will read, interpret, and adapt.
Machine-native APIs, by contrast, are built for autonomous agents that can’t “read between the lines.” Every byte must be deterministic, token-perfect, and immediately actionable:
| Dimension | Human-Centric Endpoint | Machine-Native Endpoint (Tool Calling) |
| Schema tolerance | Loose—extra or missing fields are often allowed | Strict—schema-validated, no surprises |
| Error handling | Verbose messages in plain language | Compact codes and machine-readable hints |
| Response shape | Nested objects, mixed types | Flat, minimal JSON; consistent field order |
| Authentication | Multi-step OAuth flows | Single token header; stateless |
| Version changes | Soft deprecation, long overlap periods | Version pinning: breaking changes require a new path |
Example
Fetch Tesla stock price
Human-oriented response
1{2 "status": "success",3 "data": {4 "company": "Tesla, Inc.",5 "price": 725.50,6 "currency": "USD",7 "timestamp": "2025-05-21T15:00:00Z"8 },9}
Machine-oriented response
1{2"price": 725.5,3"currency": "USD"4}
The second payload contains only what the agent needs, no prose, no nested objects, no superfluous metadata. This not only reduces token count (cheaper for the model to parse) but also eliminates ambiguity that could derail downstream logic.
Takeaway: If an endpoint must serve an autonomous agent, design it like a circuit: tight, predictable, and lossless. In the next section, we’ll distil these principles into concrete best practices and common pitfalls to avoid when crafting tool-callable endpoints.
Best Practices and Common Pitfalls
| Do This | Avoid This |
|---|---|
| One endpoint, one job (e.g., /search , /summarise ) | Overloaded endpoints with optional mode flags |
| Self-describing fields ( stock_ticker , date_range ) | Ambiguous names ( symbol , since ) |
| Flat, minimal JSON | Deeply nested objects |
| Deterministic ordering & types | Fields that appear/disappear or change type |
| Machine-readable errors {"error":"INVALID_TICKER","hint":"ticker must be uppercase"} {"error":"INVALID_TICKER","hint":"Ticker must be uppercase"} | HTML/markdown error pages or prose-only messages |
| Single bearer-token auth | Interactive OAuth flows that require human consent |
Valyu v2: is_tool_call
Valyu v2 exposes is_tool_call, an intelligent gateway flag that discerns whether a request was written by a human or an autonomous agent, and optimises the path accordingly.
is_tool_call behaves as follows:
- Human queries - Free-text requests are rewritten into deterministic function calls, so end-services see a precise schema every time.
- Machine queries - Structured calls emitted by agents skip rewriting and flow straight through, preserving latency and token budgets.
Why This Matters for Builders
- No branching in your client code - Send whatever you have; the gateway decides.
- Guaranteed schema - Whether rewritten or not, downstream services always receive the same field set.
- Latency < 150 ms - Agents bypass the rewrite hop, keeping multi-tool chains snappy.
Valyu v2 is therefore a concrete template for agent-ready APIs: two single-purpose endpoints, strict schemas, deterministic outputs, and a front door fluent in both human and machine, without asking either to compromise.
Agent-Ready APIs for the Machine-Native Future
APIs are becoming lossless conduits for autonomous agents. As tool-calling LLMs multiply, latency, schema determinism, and crystal-clear errors shift from best practice to hard contract—one stray field can stall a multi-tool chain. Valyu v2’s is_tool_call gateway shows what’s next: a single front door that speaks conversational English to humans and strict JSON to machines. Design your endpoints the same way—tight, predictable, pin-versioned—and they’ll slot straight into the machine-native future.