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ouroboros_evaluate

Evaluate an execution session's output against acceptance criteria using a three-stage pipeline: mechanical checks, semantic compliance, and optional multi-model consensus.

Instructions

Evaluate an Ouroboros execution session using the three-stage evaluation pipeline. Stage 1 performs mechanical verification (lint, build, test). Stage 2 performs semantic evaluation of AC compliance and goal alignment. Stage 3 runs multi-model consensus if triggered by uncertainty or manual request.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
artifactYesThe execution output/artifact to evaluate
session_idYesThe execution session ID to evaluate
working_dirNoProject root used to resolve Stage 1 mechanical verification commands and Stage 2 source-file visibility. Commands are read from .ouroboros/mechanical.toml; when the file is missing, the evaluator makes one AI detect call that inspects manifests (package.json, pyproject.toml, Cargo.toml, Makefile, ...) and authors the toml. Stage 1 skips every check when no toml is produced — it never guesses.
seed_contentNoOriginal seed YAML for goal/constraints extraction
artifact_typeNoType of artifact: code, docs, config. Default: codecode
trigger_consensusNoForce Stage 3 consensus evaluation. Default: False
acceptance_criteriaNoMultiple acceptance criteria for checklist evaluation. When two or more items are provided, each AC is evaluated independently and the results are aggregated into a pass/fail checklist (#366). Overrides acceptance_criterion.
acceptance_criterionNoSpecific acceptance criterion to evaluate against
Behavior4/5

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

With no annotations provided, the description carries the behavioral burden. It clearly discloses the three-stage process and the consensus trigger, which is meaningful about how the tool behaves. However, it stops short of stating whether the operation is read-only or what side effects or return format to expect, preventing a 5.

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?

Three sentences, front-loaded with the main verb and resource, and each sentence earns its place by explaining one stage of the pipeline. No fluff or redundancy.

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?

The description is thorough about the evaluation process but omits what the tool returns or how to interpret the output. With no output schema present, this is a notable gap. The rich parameter descriptions compensate for some context, but the main description alone is not fully complete.

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%, so the baseline is 3. The main description adds no parameter-specific meaning beyond the schema, but it does not need to because every parameter is already well documented. The pipeline overview subtly relates to trigger_consensus but adds no new semantics.

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 uses a specific verb ('Evaluate') with a clear resource ('an Ouroboros execution session') and adds a concrete three-stage pipeline. It distinguishes this as the evaluation tool among many ouroboros sibling tools by describing the exact stages, making the purpose unambiguous.

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?

The description implies the tool is for evaluating execution sessions but does not explicitly state when to use it over siblings like ouroboros_start_evaluate or ouroboros_qa. The stage-level detail provides context but no explicit exclusions or alternatives.

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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