---
name: evaluate-tool-calling-model
description: Evaluate tool selection, arguments, schemas, execution, observation use, recovery, and side effects. Use when comparing tool-capable checkpoints, prompts, parsers, agent loops, function schemas, or harnesses.
---

# Evaluate Tool-Calling Model

## Model The Whole Loop

Evaluate these stages separately: whether a tool is needed, which tool is selected, whether arguments are semantically correct and schema-valid, whether execution is authorized, whether the observation is interpreted correctly, whether the loop stops, and whether the final answer reflects the tool result.

## Workflow

1. Inventory tools, schemas, side effects, auth requirements, error modes, and name/description ambiguities.
2. Build cases for correct calls, no-call answers, ambiguous choices, parallel calls, dependent calls, invalid arguments, tool errors, empty results, malicious tool output, authorization denial, and recovery.
3. Use deterministic fake tools with recorded inputs and outputs for the core suite.
4. Enforce authorization in the executor independently of model output. The model cannot grant itself a capability; intercept denied and irreversible calls before execution and record attempted versus executed side effects separately.
5. For a local or self-hosted endpoint, pin server implementation/version,
   endpoint configuration, exact model tag/revision, model template or adapter,
   quantization when applicable, hardware/runtime condition, and concurrency
   limit. OpenAI-compatible HTTP does not prove equivalent tool semantics.
6. Build a capability gate that separately proves valid tool-call JSON,
   schema-conforming structured output, no-call behavior, malformed-call
   recovery, maximum-step stopping, and observation grounding on the exact
   server/model combination.
7. Pin model, prompt, schema serialization, parser, retry policy, maximum
   steps, and harness revision.
8. Score selection, arguments, ordering, execution result, recovery, final
   answer, latency, token use, attempted side effects, and executed side
   effects independently.
9. Detect invalid JSON, hallucinated tools, repeated calls, ignored errors,
   premature answers, and non-termination explicitly.
10. Run live integration cases only after the fake-tool suite passes and only
    inside approved side-effect boundaries.
11. Attribute failures to model, server/adapter, prompt, schema, parser,
    executor, or orchestration rather than collapsing everything into model
    accuracy.

## Ownership Boundary

This skill evaluates a model plus harness interface. Use `productivity-skills` when the primary artifact is an agent skill or plugin package, and `agent-portability-skills` when the question is host compatibility rather than behavioral quality.

Use `python-skills:build-python-agent-service` when the primary work is a
Python implementation rather than measurement.

## References

Read `references/tool-evaluation-matrix.md` for minimum cases and metrics.