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list grpo runs

list_grpo_runs
Read-onlyIdempotent

Monitor online-RL (GRPO) training runs with ledger-true spend, outcomes, and queued candidates to decide whether to launch another run.

Instructions

List the workspace's online-RL (GRPO) training runs with ledger-true spend and outcomes, plus how many self-improvement candidates are waiting in the queue — use it to monitor training and decide whether to start another run. GET /v1/grpo/runs (API-key scope: read). Returns: JSON object: { candidates_waiting: integer, auto_provision_available: boolean, runs: [ { id, status (ACTIVE|STOPPED|COMPLETED|FAILED|OVERBUDGET), model, budget_usd, spent_usd (reward/judge spend), gpu_spent_usd, env_spent_usd, gpu_hour_budget: number|null, gpu_rate_usd_per_hour: number|null, created_at (ISO), outcome: null | { steps?, first_half_mean_reward?, second_half_mean_reward?, stopped_by_tripwire?, bakeoff?: { verdict, delta, delta_ci95: [lo, hi], prompts, k, mean_sim_fraction, mean_tool_steps } } } ] }. Newest first, at most 50 runs; outcome is only fetched for the 10 newest non-ACTIVE runs (older ones return outcome null). Notes: Feature-flag gated: the entire training API (fineTuning flag) returns 404 'Fine-tuning is not enabled' when the flag is off. The key's minting user must be workspace OWNER/ADMIN or the call is 403. Spend figures come from the billing ledger, never self-reported. No pagination parameters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Install Server

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already mark this as read-only/idempotent/non-destructive, but the description adds substantial behavioral context: ledger-true spend figures, newest-first ordering, 50-run cap, outcome only fetched for the 10 newest non-ACTIVE runs, feature-flag 404 behavior, and OWNER/ADMIN permission requirement. This goes well beyond annotation coverage.

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?

The description is long but dense, and every section earns its place: purpose, endpoint, response shape, ordering, limits, permissions, and error behavior. It is front-loaded with the primary use case. Slightly verbose, but justified by the absence of an output schema to document the response.

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?

With no output schema, the description fully documents the JSON response shape, field semantics, ordering, limits, and caveats. It also covers auth, feature-flag failure, and ledger accuracy. An agent has everything needed to invoke the endpoint and interpret results correctly.

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?

The tool has zero parameters, so the baseline is 4. The description correctly avoids inventing parameter guidance and instead documents the fixed request scope and response behavior, which is appropriate for a parameterless endpoint.

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?

Description opens with a specific verb and resource: it lists the workspace's online-RL (GRPO) training runs. It also clearly distinguishes from sibling tools like get_grpo_run, start_grpo_run, and stop_grpo_run by framing this as the monitoring/list operation.

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 states when to use the tool: to monitor training and decide whether to start another run. It doesn't explicitly name alternatives or exclusions, but the list-vs-single-run distinction is clear enough given the sibling names and the stated purpose.

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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