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bench

Measure a Python script's runtime across repeated timed runs with warmup, aggregating mean, standard deviation, and minimum plus raw samples for performance comparisons.

Instructions

Run a Python script n times in the user's interpreter and aggregate its timing. Contract: the script prints one JSON line to stdout containing at least {"ms": } and optionally {"ok": } for a correctness check. Returns mean/std/min over the n timed runs plus every raw sample, so a LEADERBOARD row can cite n and seed.

Input Schema

NameRequiredDescriptionDefault
nNo
cwdNo
argsNo
scriptYes
warmupNo
timeoutNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

B3.2/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It does disclose the important trait that the script runs inside the user's own interpreter and states the stdout JSON contract ({"ms": float}, optional {"ok": bool}), plus the aggregate return shape. It does not say what happens on timeout, non-zero exit, missing ms line, or whether process state is reused across runs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with the core action, then the script contract, then the return shape. Dense and mostly waste-free, with only the unexplained 'seed' reference costing clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema and no annotations, so the description must do more; it covers the return aggregates (mean/std/min plus raw samples) and the script's output contract well, but leaves half the input surface and all failure behavior undocumented. Adequate to attempt a call, not complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% across 6 parameters. The description explains only the script contract and, indirectly, n; warmup, timeout, cwd, and args are never mentioned, so an agent must guess their semantics from bare titles. It also mentions a 'seed' that is not actually a parameter, which invites confusion.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: run a Python script n times in the user's interpreter and aggregate timing. That is concrete and distinct from siblings like profile or record, though it never explicitly contrasts itself with them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied by the trailing clause about a LEADERBOARD row citing n and a seed, which hints at micro-benchmark use. No explicit when-to-use / when-not, and no mention of profile, torch_trace, or roofline as alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.