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architect.validate_consensus

Runs multiple independent reviews of agentic code in parallel and returns a consensus score with median, variance, and stability metrics for an unbiased assessment.

Instructions

Pro/Teams — N-shot CONSENSUS doctrine review of agentic code. ON CLIENT TIMEOUT — DO NOT RETRY THIS TOOL. Long-running (~80-120s for N=3 parallel LLM calls); MCP clients often close the call before the server returns. Retrying re-runs N × 60-180s LLM calls from scratch and burns N× compute. RECOVERY: same heartbeat pattern as architect.validate — the run_id is emitted in the FIRST progress event at t=0s (before LLM children fire); on timeout, call me.validation_history(run_id='<that-id>') to fetch the persisted consensus envelope. Runs N parallel architect.validate calls with private_session=True, then aggregates them to a per-principle MODE verdict + median severity + per-principle stability + score range/stdev. Returns one ConsensusValidationResponse with the headline median score, the honest variance band, and a representative full ValidationResponse (the child whose score is closest to the median). WHEN TO CALL: the user wants an HONEST first-pass score on agentic code, with the architect's variance surfaced. The single-shot architect.validate re-asserts the prior persisted run's verdict via baseline-anchor injection — same code can score 60/C anchored vs 98/A unanchored. Consensus mode is the unanchored honest read. WHEN NOT TO CALL: when you NEED the iteration delta against a prior run (regressions/improvements panel) — for that, call architect.validate which keeps baseline injection on. CHAIN RESUME: each child runs with private_session=True (no anchor) on purpose, but the CONSOLIDATED outer row IS persisted with lifecycle_status='completed' — the next single-shot architect.validate on the same repository auto-resolves it as prior_run_baseline. Consensus checkpoint becomes the new anchor. See the architect-validation-orchestration skill in the agent-asset pack for the full validate → consensus → certify sequence. BEHAVIOR: N (default 3, max 5) parallel LLM calls run concurrently; wallclock ~80-120s for N=3 (max child latency, not sum). Cost = N × LLM bill. Each child runs with private_session=True so the doctrine prompt's prior-run baseline injection is suppressed (no anchor bias). One CONSOLIDATED UserValidationRun row is written carrying the consensus envelope; the N children themselves do NOT persist (private_session contract). AUTH: Bearer , Pro/Teams plan. Same paid-plan gate as architect.validate. INPUTS: same shape as architect.validate. n is the only extra arg (range 2..5). private_session is implicit (always true for children); the OUTER consolidated row IS persisted unless the tool itself is called inside another private context — but no such wrapper exists today. OUTPUT: response carries score_consensus_median (headline), score_stdev (honest uncertainty), score_range (min, max), mode_stability_min_pct (the cert-eligibility gate's input — ≥ 80% means the consensus is stable), per_principle (mode + distribution + severity median per principle), and representative_response (the closest-to-median child's full ValidationResponse so existing UI components render unchanged). TYPED FAILURES: same as architect.validate (timed_out, rate_limited, dependency_unavailable). Plus consensus-specific: consensus_quorum_failed when fewer than 2 child runs succeeded (≥ 2 required to compute a meaningful median).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nNoNumber of parallel child runs. Default 3 (the variance signal is visible at N=3; cost = 3× LLM bill). Capped server-side by Settings.consensus_n_max (default 5).
taskNoWhat the agent or workflow is trying to accomplish.
filesNoList of file paths relevant to the implementation.
goalsNoSpecific safety or quality goals to evaluate against.
languageNoProgramming language of the code (e.g. 'python').
focus_areaNoOptional: narrow the review to a principle cluster or slug.
repositoryNoIteration key. Consensus children all run unanchored (`private_session=True`), but the consolidated row IS persisted under this key — discoverable as prior baseline for the next single-shot `architect.validate`. Same value across calls keeps the iteration arc inspectable.
example_limitNoMax curated examples per child run.
implementation_contextYesThe artifact under review. SEND FULL FILE CONTENTS VERBATIM — same constraint as architect.validate. Truncation produces hallucinated findings on code that isn't there.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Annotations indicate readOnlyHint=false, openWorldHint=true, etc. The description adds extensive behavioral context: timeout duration (80-120s), retry prohibition, recovery pattern via validation_history, parallelism and cost (N× LLM), auth requirements (Pro/Teams), private_session behavior, and typed failures including consensus-specific. This far exceeds what annotations alone 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?

Despite length, the description is well-structured with clear sections: timeout warning, when-to-call, behavior, auth, inputs, outputs, failures. Every sentence is informative and earns its place. Front-loaded with critical retry instruction.

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 tool complexity (9 params, sibling set, annotations, output schema exists), the description covers all necessary aspects: purpose, behavior, constraints, usage guidance, interaction with siblings, error types, and output fields. No gaps noted.

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 description coverage is 100%, so baseline is 3. The description adds value beyond the schema by explaining the role of parameters in the consensus context (e.g., 'n' capped server-side, 'repository' as iteration key, 'implementation_context' requiring full content). This extra context justifies above baseline.

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 it performs an 'N-shot CONSENSUS doctrine review of agentic code', distinguishing itself from the sibling architect.validate by explaining that the single-shot version re-asserts prior baselines while consensus provides an unanchored honest read. The verb 'validate' is combined with 'consensus' to specify the unique approach.

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?

Explicit 'WHEN TO CALL' (honest first-pass score with variance surfaced) and 'WHEN NOT TO CALL' (when iteration delta needed, use architect.validate) sections provide clear guidance. Also includes chain resume sequence and references the full orchestration skill.

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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