tool-eval-bench

A tool-calling quality benchmark for LLMs in agentic workflows, built for self-hosted serving stacks: vLLM, SGLang, LiteLLM, llama.cpp, Strata, TabbyAPI, NInfer, TensorFold, and hosted Gemini and Anthropic.

Each scenario observes one assistant conversation with mock tools. It does not measure independent agents, delegation, or inter-agent handoffs. Localization coverage is currently German-focused. Difficulty tiers are author estimates, not calibrated model rankings.

It runs 69 deterministic scenarios (plus 23 opt-in Hard Mode ones) through OpenAI-compatible /v1/chat/completions endpoints, scores each as pass, partial, or fail, and writes a full conversation trace for every one. Throughput, long-context retrieval, and accuracy benchmarks run against the same endpoint.

!tool-eval-bench benchmark output

Quickstart

Install

uv tool install git+https://github.com/SeraphimSerapis/tool-eval-bench.git
With throughput benchmarking (bundles llama-benchy)
uv tool install 'tool-eval-bench[perf] @ git+https://github.com/SeraphimSerapis/tool-eval-bench.git'

Also available via Docker if you would rather not have a local Python, or as a development checkout.

Run it

Point it at an OpenAI-compatible endpoint and run the core 15 scenarios. This takes a couple of minutes and needs no configuration file:

tool-eval-bench run --short --base-url http://localhost:8000

Drop --base-url and it scans the common localhost ports used by vLLM, llama.cpp, TensorFold, SGLang, LiteLLM, Ollama, and TGI. tool-eval-bench probe checks an endpoint is reachable before you commit to a full run.

When that looks right, drop --short for the standard 69-scenario benchmark. Pass --seed so the run is reproducible:

tool-eval-bench run --seed 42

TensorFold is detected from its model owner or metrics namespace and uses the existing OpenAI-compatible adapter. Its default port is already scanned:

tool-eval-bench run --short --base-url http://127.0.0.1:8080/v1 --seed 42

See TensorFold compatibility for deployment metadata limits, structured-output setup, and speculative metrics.

Engine detection does not infer identity from a port or generic health response. A server's declared identity wins over compatibility shapes: model owner, Server header, /health service, /.well-known/serviceinfo, or /props build name. That is how Strata and TabbyAPI are told apart from the llama.cpp-style /props they both serve. llama.cpp is recognized through its model owner, identifying header, characteristic props/build fields, or metrics. Halogen Flash is recognized through its halogen: metrics, even when it also exports llama.cpp-compatible names. Unidentified servers use the unknown backend label rather than being called vLLM. The OpenAI-compatible request format is unchanged; --backend still pins the reporting label. The Python API runs the same detection when backend is left at unknown; an explicit label is kept, and probe_engine=False skips detection. See backend identification.

Exercise controlled fixture variants

tool-eval-bench run --hardmode --variant-seed 1 --seed 42

--variant-seed chooses versioned mock environments independently of the model's sampling seed. Sixteen scenarios vary across all ten authoring packages; the others remain controls. Variants include dry weather, delayed or failed jobs, clarification followed by action, room capacities, transaction outcomes, pagination, and changed identifiers. Private YAML packs remain unchanged. The selected fixture metadata participates in config_fingerprint; resume rejects a different variant.

Reports include paired small/crowded toolset deltas when both scenarios ran. TC-88 scores visible numeric constraints independently of reasoning-channel availability, which appears in capability diagnostics. Structured-output diagnostics say that schema enforcement was requested, without asserting the backend enforced it. See methodology for coverage and limits.

Read your report

Every completed run writes two artifacts, both relative to the directory you ran from:

| Artifact | Path | |---|---| | Markdown report, with the full trace per scenario | runs/YYYY/MM/.md | | SQLite record, queryable | data/benchmarks.sqlite |

The terminal summary gives you the composite score, the star rating, and per-category percentages. Three things are worth checking before you compare two runs:

• completion_rate. Scenarios that measured the serving environment rather

• Safety warnings. An observed unsafe action or disclosure is reported even

• config_fingerprint. A run's fingerprint covers its configuration, the

To read past runs back:

tool-eval-bench history          # recent runs
tool-eval-bench leaderboard      # ranked, grouped by comparable configuration
tool-eval-bench compare A B      # two persisted runs, side by side

Point it at your server

For remote servers or non-standard ports, create a .env file:

TOOL_EVAL_BASE_URL=http://your-server:8080
...or host and port separately, used when BASE_URL is empty:
TOOL_EVAL_HOST=your-server
TOOL_EVAL_PORT=8080
TOOL_EVAL_MODEL=         # optional: auto-detected from /v1/models
TOOL_EVAL_API_KEY=       # optional

Priority order: CLI flags > environment variables > .env > auto-discovery. Env vars set by a calling process are never overridden by a stale .env.

To keep several endpoints in one .env for A/B runs, scope them by name and pick one with --provider:

TOOL_EVAL_GEMINI_BASE_URL=https://generativelanguage.googleapis.com
TOOL_EVAL_GEMINI_API_KEY=...
TOOL_EVAL_GEMINI_MODEL=gemini-2.5-pro
TOOL_EVAL_LOCAL_BASE_URL=http://gpu-box:8080

tool-eval-bench run --provider gemini
tool-eval-bench run --provider local
tool-eval-bench compare <run-a> <run-b>

The name is free-form. gemini, openai, and anthropic also set the report's backend label. See .env.example for the vendor endpoints. An Anthropic Messages endpoint (api.anthropic.com, or any URL ending in /messages, such as OpenCode Zen's) is detected from the URL; --format anthropic pins it for a gateway root that serves several formats.

What it measures

| | What it tests | More | |---|---|---| | Tool-call quality | 69 scenarios across categories A–O: tool selection, parameter precision, multi-step chains, refusal, error recovery, localization, instruction following, safety and prompt injection, 52-tool namespaces, autonomous planning, structured output | methodology | | Hard Mode | 23 opt-in adversarial, stateful, and transactional scenarios for models that already score well, broken down by capability in reports | hard-mode | | Throughput | llama-bench-style prefill and generation speed, with depth and concurrency sweeps | benchmarks | | Long-context retrieval | Needle-in-a-haystack across a grid of context lengths and depths, reporting effective context | needle | | Context pressure | Pre-fill a share of the window before each scenario to find where quality slips | context-pressure | | Speculative decoding | Acceptance rate, effective tokens per second, speedup, plus a live monitor | speculative-decoding | | Accuracy | GSM8K, MMLU, and IFEval through the same adapter | benchmarks | | Decision models | Single-pass option scoring on llama.cpp /v1/systemone against the Typed Decisions test split: accuracy, KL and Brier against soft gold distributions, and calibration, plus a live canary monitor | decision-models |

TC-74 accepts confirmation ranges such as 14:00–14:45 and 2:00–2:45 PM for its authorized 2pm, 45-minute event. Stated start and end times must match; incorrect 12-hour times are rejected just like incorrect 24-hour times.

An asterisk on a prefill rate marks an estimate from time to first content token. The throughput guide explains when that estimate is used.

Mock tool responses carry realistic payload noise — extra metadata, timestamps, nested objects — so a model has to extract the right field from a response shaped like a real API's, not a hand-trimmed one.

Scope. This measures tool-calling quality: whether a model picks the

right tool, passes the right parameters, chains correctly, and respects error

and safety boundaries. It is not a full agentic system benchmark. See

related work for how it compares to BFCL, PinchBench,

and Claw-Eval.

Scoring

Each scenario scores 2 (pass), 1 (partial), or 0 (fail). The final score is (points earned / max points) × 100, so every scenario counts equally and larger categories carry proportionally more weight.

| Score | Rating | |---|---| | 90–100 | ★★★★★ Excellent | | 75–89 | ★★★★ Good | | 60–74 | ★★★ Adequate | | 40–59 | ★★ Weak | | 0–39 | ★ Poor |

When a safety violation is recorded and the safety group scores below 50%, the rating is capped at ★★★ regardless of the composite. --weight-by-difficulty computes an alternative score that weights harder scenarios more heavily.

TC-62 accepts Acme revenue stated inline or in a revenue bullet immediately under a clear Acme heading, including Markdown headings and blank-line spacing. It does not carry that attribution across intervening text or another company heading. Wrong amounts, percentages, negated claims, and quoted figures still do not qualify.

Full rationale, the category table, the difficulty tiers, and the evaluator design: docs/methodology.md.

Commands

| Command | Purpose | |---|---| | run | Run tool-call scenarios | | probe | Check inference-server reachability | | bench | Throughput, speculative-decoding, or context-pressure benchmarks | | plugin | Run GSM8K, MMLU, IFEval, needle-in-a-haystack, or decision models | | spec-live | Monitor speculative-decoding metrics | | decision-live | Monitor a decision model with live canary probes | | compare | Compare stored runs or Markdown reports | | history, leaderboard, export | Inspect or export persisted results | | resume | Continue an incomplete run |

# Smoke test — 5 scenarios
tool-eval-bench run --scenarios TC-01 TC-02 TC-03 TC-04 TC-05
Full 92 — standard suite plus Hard Mode
tool-eval-bench run --seed 42 --hardmode
Quality plus speed
tool-eval-bench bench --seed 42 --perf
Statistical rigor — Pass@k / Pass^k across trials
tool-eval-bench bench --seed 42 --trials 3 --perf
Long-context retrieval, chained onto a full sweep
tool-eval-bench --hardmode --seed 42 --perf --needle
Safety and tool selection only, failing CI on a safety regression
tool-eval-bench run --categories K A --fail-on-safety
Tag an execution so every report it generates is identifiable
tool-eval-bench run --label "nightly qwen3 2026-08" --trials 3

tool-eval-bench COMMAND --help lists a command's options; every flag and exit code is in docs/cli-reference.md. Flat invocations (tool-eval-bench --short, --history) remain supported.

Scenario IDs passed to --scenarios resolve against all 92, so --scenarios TC-85 works without --hardmode and takes precedence over --short and --categories. Selection is validated before model discovery, so a typo fails immediately rather than becoming an empty run. So does a filter that matches nothing, such as --categories P without --hardmode.

Runs are checkpointed to SQLite as each scenario finishes, so a Ctrl-C costs you only the scenario in flight — tool-eval-bench resume RUN_ID picks up the rest. See docs/artifacts.md.

--system-prompt TEXT (or --system-prompt-file PATH) replaces the built-in "helpful assistant" system prompt for every scenario in a run — useful for testing whether a stricter persona stops models from violating evaluator contracts. The benchmark reference-date line is appended after the override and stays authoritative, so relative-time scenarios keep working whatever the prompt says about today. The override is recorded in the run config and its comparison fingerprint — a run with a different prompt is a different cohort, and resuming across a change is refused — and the run report marks that one was used. examples/system-prompt-function.txt is a minimal override that constrains the model to verified output.

Optional answer audits

A separate decision model can audit what the model told the user, such as a claim that a payment went through or a question asking which contact was meant:

tool-eval-bench run --hardmode --base-url http://localhost:8000/v1 \
  --decision-judge \
  --decision-judge-base-url http://localhost:8084/v1 \
  --decision-judge-model clef-flash

--decision-judge picks the set: recommended (11 scenarios, also the default when only the connection flags are given) or all (17). The judge answers one versioned question per audited scenario using /v1/systemone. Official points, safety warnings, and ratings stay deterministic. SQLite and the Markdown report keep the original result alongside the audit's probabilities, exact input, versioned rubric, model, latency, and any disagreement. An audit error is an unavailable judgment, not a model failure. Held-out scenarios are never sent to the judge.

Terminal output names the judge when its request starts, then shows the finding, probability, and agreement or disagreement for that scenario. Unaudited scenarios and skipped requests get no placeholder. JSON mode emits decision_audit_start and decision_audit_result progress events on stderr.

Set TOOL_EVAL_DECISION_JUDGE_API_KEY only if the judge requires authentication. The benchmark model's credentials, headers, and reasoning are not forwarded. Auditing is off unless both judge connection flags are supplied. Audited and unaudited runs share a comparison fingerprint, since an audit never changes a score. Probabilities are uncalibrated, and assistant text can steer the judge. See answer audit limits and API configuration.

Programmatic API

import asyncio
from tool_eval_bench.api import run_benchmark
result = asyncio.run(run_benchmark(
    model="Qwen/Qwen3-8B",
    base_url="http://localhost:8000",
    backend="vllm",
    short=True,           # core 15 scenarios
    persist=False,        # skip SQLite/Markdown (caller handles storage)
))
print(result["final_score"])   # e.g. 87
print(result["rating"])        # e.g. "★★★★ Good"

The call returns a versioned envelope with final_score, rating, safety_warnings, deployability, and total_scenarios, alongside the full per-scenario detail. For subprocess integration, --json-file writes results to a file and emits JSONL progress events on stderr. Every parameter and returned field: docs/api.md.

External tools can validate configuration against the published schema via tool_eval_bench.schema.get_schema().

Speculative-decoding detection and Prometheus counter helpers live in runner.spec_detection, independently of the throughput and speculative benchmark runners. Existing imports of these helpers from runner.speculative remain supported.

Documentation

• Getting results: CLI reference ·

• The benchmarks: methodology ·

• Running it elsewhere: backends · Docker
• Building on it: Python API · architecture
• Contributing: CONTRIBUTING.md ·

• Context: related work · releasing

Contributing

A pull request runs lint, type checking, and the suite on Python 3.13 against the committed uv.lock. Python 3.11 and Windows run after merge. The same gate, locally:

.venv/bin/ruff check .
.venv/bin/ruff format --check .
.venv/bin/mypy
env -u FORCE_COLOR .venv/bin/python -m pytest tests/ \
  --ignore=tests/test_llama_benchy.py -m "not live" --randomly-seed=104729

Setup, the full quality bar, and the pull request checklist are in CONTRIBUTING.md. CHANGELOG.md is generated — record changes as fragments under changelog.d/.

Credits

Scenario methodology adapted from ToolCall-15 by stevibe (MIT License). Licensed under the MIT License.

Third-party data

The decision-model benchmark ships the test split of Typed Decisions, published by the LocalLLaMA organization on Hugging Face under the Apache License 2.0, pinned to revision e039ebffcc280174dd354227424fb2b249f191de. The rows are unmodified apart from format conversion. The dataset's license and a notice describing the changes sit next to the data in src/tool_eval_bench/plugins/decision/vendor/typed_decisions/ and ship in every build. This project's MIT license does not cover that data. See decision models.