agent-must-work-over-very-large-tool-catalogue-wrappingWhen an agent must work over a very large tool catalogue, wrapping each tool in a natural-language interface that resolves the API schema internally — so raw schemas are retrieved on demand rather than enumerated in the model's prompt — contributes more to multi-step tool- call success than the surrounding planner, router, or verifier stages.
Ingested from a paper but not yet reviewed by a human. It is deliberately inert: it does not move any technique’s standing, does not count toward the backtest, and is excluded anywhere a claim would carry weight. Read the source before relying on it.
Capability: Using the tools it is given
Observed on
Multi-agent harness with an 8B backbone, over a repository of ~25.5k functions, evaluated on ToolBench and NESTFUL nested-call tasks; not tested with strong frontier backbones insi.
Sources
- Component ablation in one paper: removing ToolFace/Tool Primitives dropped ToolBench average pass rate from 75.1 to 16.1 and NESTFUL full-sequence accuracy from 0.44 to 0.06, larger than removing the verifier (47.6 / 0.15), planner, or router. Single system, single backbone; the ablation removes retrieval and the wrapper together, so it does not separate the two.
Disagreeing is the most useful thing you can do here. Both sides of every contested claim in this catalog were assembled by the same person, which is its weakest point.
Related claims
- For locally deployed open-weight models driving a stateful, dependency-ordered MCP tool server, cutting tool descriptions from full specifications (purpose, parameter semantics, constraints, failure conditions) down to one sentence each raises the fraction of calls the server rejects for every model tested, while its effect on task coverage is less consistent.Using the tools it is given · unreviewed
- For long-horizon tool-calling agents (tasks needing five or more calls), embedding state-transition cues — preconditions, invariants, completion states — into the descriptions of the tools on the intended chain is what actually steers the agent's trajectory; runtime corrective text appended to tool results only patches residual drift, and the plausible user prompt alone (persona, deadlines, format constraints) does not establish the trajectory at all.Following instructions hidden in data · unreviewed
- A linear probe on a tool-calling model's hidden state at the last generated token detects incorrect tool-calls — including wrong-but-well-typed argument values that parsers and logs do not catch — and works better on larger models and at middle-to-late layers than at the final layer.Using the tools it is given · unreviewed
- On rubric-graded long-context tasks, most of the gain from a context- compilation harness comes from extracting the context's rules, exact terms and output spec into an explicit checklist placed in the prompt, not from the executable verifiers built on top of it — and the gain appears only on rule-dense tasks and on models with enough capacity, disappearing or reversing on open-ended tasks and on a small-activation model.Losing information in long inputs · unreviewed
- Handing an agent context-editing tools (delete, summarize, note, fold) by prompt alone makes mid-size models worse than leaving their context untouched — Qwen3-8B, Qwen3-14B and Gemma4-E4B all scored lower on long-context QA with the tools than without them; only after fine-tuning on trajectories that use the tools does the toolset become a gain.Keeping its own context clean · unreviewed
Notes
Ingested unreviewed on 2026-09-07 and deliberately inert until a human endorses it: it does not move a technique standing, does not count toward the backtest, and is excluded anywhere a claim would carry weight. Drafted confidence: low. Falsifier as drafted: An ablation on comparable benchmarks where removing the natural-language tool wrapper (exposing raw schemas plus retrieval) costs less than removing the verifier or planner, or where the wrapper gives no gain once tools are retrieved on demand. Proposed technique, not yet catalogued: Natural- language tool wrappers with on-demand schema retrieval.