OpenMark

Run a benchmark

run_benchmark

Save a task (YAML, see get_task_format) to the user's OpenMark account and run it on the given models with real API calls. Returns a job_id and the expected cost; poll get_results. Spends the user's credits; pass max_credits to only start within a budget. Optional run settings match the app's Configure & Run panel (preset, stability_runs, max_tokens, temperature_discovery, temp_preference, timeout_profile, custom_timeout, fail_fast).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`models`	Yes	Model ids from list_models
`preset`	No	Like the app's presets: standard = 2 passes + snappy timeouts (the default), thorough = 3 passes + patient timeouts. Explicit stability_runs / timeout_profile override it
`fail_fast`	No	Stop a model after repeated errors to save credits. Set false to let every model finish, e.g. to diagnose failures
`task_yaml`	Yes	The full task YAML (a JSON object with the same fields also works)
`max_tokens`	No	Output token limit per answer, 16 to 16000; 0 = no limit. Keep the default unless results show it was too low: a higher limit raises the credits needed to start. get_results flags models whose answers came back empty or near the limit; re-run those with a higher value
`upload_uid`	No	From prepare_attachments, when tests use attachments
`max_credits`	No	Budget: if the expected cost is higher, nothing runs and you get the quote instead
`custom_timeout`	No	Seconds per answer, with timeout_profile custom
`stability_runs`	No	Passes per model; 2+ shows which models are consistent
`temp_preference`	No	With temperature_discovery: which temperature wins a tie	low
`timeout_profile`	No	How long to wait per answer: snappy 45s, patient 90s, custom = custom_timeout	snappy
`temperature_discovery`	No	Find each model's best temperature for this task. Costs about 6x as much

Schema Changelog

Changes observed during successful MCP inspections.

Changed8 schema fields changedOct 19, 2026

addedInput schema / properties / custom_timeout

Added value: +{
+  "description": "Seconds per answer, with timeout_profile custom",
+  "maximum": 600,
+  "minimum": 120,
+  "type": "integer"
+}

addedInput schema / properties / fail_fast

Added value: +{
+  "default": true,
+  "description": "Stop a model after repeated errors to save credits. Set false to let every model finish, e.g. to diagnose failures",
+  "type": "boolean"
+}

addedInput schema / properties / max_tokens

Added value: +{
+  "default": 500,
+  "description": "Output token limit per answer, 16 to 16000; 0 = no limit. Keep the default unless results show it was too low: a higher limit raises the credits needed to start. get_results flags models whose answers came back empty or near the limit; re-run those with a higher value",
+  "maximum": 16000,
+  "minimum": 0,
+  "type": "integer"
+}

addedInput schema / properties / preset

Added value: +{
+  "description": "Like the app's presets: standard = 2 passes + snappy timeouts (the default), thorough = 3 passes + patient timeouts. Explicit stability_runs / timeout_profile override it",
+  "enum": [
+    "standard",
+    "thorough"
+  ],
+  "type": "string"
+}

changedInput schema / properties / stability_runs / maximum
```
Previous value: -3New value: +5
```

addedInput schema / properties / temp_preference

Added value: +{
+  "default": "low",
+  "description": "With temperature_discovery: which temperature wins a tie",
+  "enum": [
+    "low",
+    "high"
+  ],
+  "type": "string"
+}

addedInput schema / properties / temperature_discovery

Added value: +{
+  "default": false,
+  "description": "Find each model's best temperature for this task. Costs about 6x as much",
+  "type": "boolean"
+}

addedInput schema / properties / timeout_profile

Added value: +{
+  "default": "snappy",
+  "description": "How long to wait per answer: snappy 45s, patient 90s, custom = custom_timeout",
+  "enum": [
+    "snappy",
+    "patient",
+    "custom"
+  ],
+  "type": "string"
+}

Changed1 schema field changedOct 19, 2026

addedInput schema / properties / max_credits

Added value: +{
+  "description": "Budget: if the expected cost is higher, nothing runs and you get the quote instead",
+  "minimum": 1,
+  "type": "integer"
+}

First observedOct 18, 2026

TDQS

A4.4/5.0

Behavior5/5

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

With no annotations, the description carries the full burden and does so well: it discloses that real API calls are made, that the user's credits are spent, that temperature_discovery costs about 6x, and that exceeding max_credits means nothing runs and only a quote is returned. It also explains the async contract (job_id + expected cost, then poll get_results).

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 and the credit/budget warning. The trailing parenthetical enumerating all eight optional parameters is largely redundant against the schema and is the one element that does not earn its place.

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?

For a 12-parameter mutation tool with no output schema, the description covers the missing pieces: what it returns (job_id, expected cost), the follow-up call (get_results), the cost side effect, and the budget gate. Nothing an agent needs to invoke it correctly is absent.

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

Parameters3/5

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

Schema description coverage is 100% and the schema itself carries rich per-parameter explanations (preset overrides, fail_fast rationale, max_tokens sizing, timeout_profile values), so the baseline is 3. The description's list of the eight optional settings restates what the schema already documents and adds only the framing that they mirror the app's Configure & Run panel.

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?

States a specific compound action (save a task to the user's account and run it on given models with real API calls) and immediately distinguishes itself from siblings by naming get_task_format, get_results, and list_models in context. An agent can tell this is the execution tool versus the estimation/read tools without opening a schema.

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?

Gives clear operational context: poll get_results afterward, pass max_credits to stay within a budget, and fail_fast toggles diagnostic vs. completion behavior. It does not explicitly say when to prefer estimate_cost over this tool, which is the one obvious alternative an agent needs to weigh, so it falls short of a 5.

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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