Entity Enricher

Run benchmark

run_benchmark

Start billed asynchronous execution and scoring of a benchmark. Requires owner, a benchmark-enabled plan and a judge; a verified reference is also required except for sample generation. Supply model_keys or providers; omitting both runs all active models with usable provider keys. Repetitions and judging increase cost. Returns job_id and total_models: poll get_job_status, then read get_benchmark_scenario_results. Re-running replaces each selected model's previous result. An organization's benchmark runs and scoring passes execute one at a time: a launch while another is in flight is queued (queue_position, status 'pending'), and a launch on a scenario whose run is still queued folds its models into that run (merged=true, job_id names the queued run). Reference setup and score interpretation: enricher://docs/model-benchmark.

Input Schema

TableJSON Schema

Name	Required	Description
`providers`	No	Provider names (e.g. ['anthropic', 'mistral']) — runs every active model of those providers that has a valid key.
`model_keys`	No	Explicit model composite keys. Overrides `providers`.
`scenario_id`	Yes	UUID of the scenario to run.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 25, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the annotations, the description discloses billing, asynchronous execution, queueing behavior, model folding into queued runs, and the side effect that re-running replaces previous results. It also explains the merged=true and queue_position semantics. This is rich behavioral context that annotations alone would not provide.

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

Conciseness5/5

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

The description is dense but every sentence earns its place: purpose, prerequisites, parameter behavior, return/next steps, side effects, queueing semantics, and a docs pointer. It is front-loaded with the core action and remains structured despite covering a complex tool.

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

Completeness5/5

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

Given the tool's complexity, the presence of an output schema, and the rich annotations, the description covers prerequisites, side effects, queueing, cost, follow-up tools, and documentation. Nothing essential for invoking the tool correctly is missing.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds value by explaining the relationship between model_keys and providers, the behavior when both are omitted, and the cost impact of repetitions and judging. This goes beyond the schema's per-parameter descriptions.

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

Purpose5/5

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

The description opens with a specific verb and resource: 'Start billed asynchronous execution and scoring of a benchmark.' It clearly distinguishes this from sibling tools like get_benchmark_scenario_results and get_job_status by framing it as the launch action, not a read or setup action.

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

Usage Guidelines4/5

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

The description gives clear operational context: prerequisites (owner, plan, judge, verified reference), model selection behavior, cost implications, and the expected follow-up polling/read workflow. It does not explicitly name alternatives or state when not to use this tool, but the guidance is strong enough for an agent to select it correctly.

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

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