Skip to main content
Glama
Mipiti
by Mipiti

recompute_verdicts

Re-run coverage and group-sufficiency verdict evaluations for a threat model, or get a pre-flight cost estimate with dry-run mode. Bypass quiet-period batching for fresh verdicts.

Instructions

Re-run coverage and group-sufficiency verdict evaluation for a model, or return the pre-flight cost estimate without enqueueing anything.

dry_run selects between enqueueing the recompute and a cost-only quote:

  • dry_run=False (default) — ENQUEUE: force a fresh evaluation of every control's coverage verdict and every live control objective's group-sufficiency verdict, bypassing the normal quiet-period batching. Evaluation runs in the background; re-read the model's divergence report (or coverage surfaces) shortly after to see updated verdicts. The response carries estimated_credits — an informational estimate; nothing is charged from it, actual usage is metered as the evaluation runs, per the account's plan. Returns {model_id, model_version, enqueued_coverage, enqueued_group_sufficiency, total_enqueued, estimated_credits, quote, governor}. When governor.exhausted is true the work is queued and resumes automatically at governor.resets_at — it is never dropped.

  • dry_run=True — QUOTE ONLY: return the informational pre-flight cost estimate and enqueue NOTHING. Nothing is charged from the estimate. It carries computed_at and the pricing rate_version in force so a stale quote is detectable. Returns {estimated_credits, computed_at, rate_version, informational, total_enqueueable, already_evaluated, governor}, where total_enqueueable is the number of jobs a recompute would enqueue and already_evaluated counts subjects that already carry a verdict (a portion short-circuit without cost, so the estimate is an upper bound). When governor.exhausted is true, new evaluation would be queued until governor.resets_at.

Both modes return a 503-mapped error when verdict observability is unavailable on the deployment. To un-park verdicts stuck by a transient outage instead of force-enqueueing the whole model, use retry_verdicts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dry_runNoWhen True, return only the pre-flight estimate and enqueue nothing. When False (default), enqueue the recompute.
model_idYesID of the threat model to re-evaluate (or estimate for).
server_versionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations are provided, so the description carries full burden. It discloses key behaviors: forces fresh evaluation bypassing quiet-period batching, runs in background, returns specific fields, governor behavior (queued and never dropped), and error mapping (503). It also explains that dry_run does not enqueue and is informational. This is comprehensive and beyond what structured fields could convey.

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 long but well-structured with distinct sections for the two modes. It front-loads the main purpose and then details each mode with returns and edge cases. Every sentence adds value, and the format with bullet points enhances readability without redundancy.

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 (two modes, background execution, governor, error handling, alternative tool), the description is extremely complete. It covers return values, side effects, edge cases, and references an alternative tool. It also provides enough context for an agent to understand the full lifecycle, making it sufficient even with an output schema present.

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 schema covers dry_run and model_id but lacks description for server_version (67% coverage). The description extensively elaborates on dry_run (modes, returns, informational nature) and adds context for model_id, but does not clarify server_version. Given the coverage is below 80%, the description compensates partially but leaves a gap for one required parameter. Overall, it adds significant meaning beyond the schema for two of three parameters.

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 clearly states the tool's purpose: re-run coverage and group-sufficiency verdict evaluation, with an explicit scope (for a model) and an alternative mode (dry-run cost estimate). It distinguishes itself from sibling tools by mentioning retry_verdicts for a specific scenario. Specific verb+resource+scope is present, and the two modes are well-defined.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use the tool versus alternatives: it says to use retry_verdicts for unparking stuck verdicts instead of force-enqueueing the whole model. It also explains the two modes (enqueue vs dry-run) and their use cases, making selection clear for the agent.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Mipiti/mipiti-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server