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tokonomix-council-mcp

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by TokonoMix

tokonomix_rate_consensus

Rate the real-world usefulness of a consensus call (1–10) after the outcome is known, using the request_id from the original ask. Submit optional outcome and findings feedback for post-hoc validation.

Instructions

Rate a consensus call 1–10 on real-world usefulness, after you have seen the answer play out. The request_id is returned by tokonomix_consensus_ask in the billing breakdown line ( · request_id: ...) and in the x_council.request_id metadata field. Optional: helped_model credits the ONE model whose minority or blind-spot view actually helped (the red-thread blind-spot differentiator) — supply its bare slug (e.g. "gemini-2.5-pro"). Opt-in, no friction. Optional: note accepts up to 2000 chars of free text — it IS stored (admin-only, never returned to any caller, never shown publicly verbatim) and enriched by an EU-hosted model into the agent-source day/week/month summary, kept strictly separate from the human-feedback summary. Same privacy handling as the human free text. Optional feedback (INT-1882, accepted only when the platform feedback-loop is enabled): outcome (correct|wrong|partial) is the minimal always-useful signal; findings is the rich agent signal — the real/false split per severity bucket {high,medium,low}:{real,false}. The auto-scoring already counts the buckets; you supply only whether each was a TRUE catch or a FALSE positive. Sharing the full findings earns the review-discount once go-live (one model-call less on that round). Per-account dedup: one authoritative rating per request_id per account; re-submitting updates it (last-write-wins). Requires the same API key that made the original call. Feature-gated on the platform feedback-loop: when it is disabled the tool returns a clear "not enabled" message rather than an error.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNoOptional free-text context (max 2000 chars). Stored admin-only, never returned to any caller and never shown publicly verbatim; enriched by an EU-hosted model into the agent-source day/week/month summary (kept separate from the human-feedback summary). Same privacy handling as the human free text.
scoreYesUsefulness score 1–10 (1 = not useful at all, 10 = extremely useful in practice).
outcomeNoOptional (feedback-loop): did the consensus answer turn out correct, wrong, or partial in practice? The minimal validation signal — upgrades the call to high-confidence scoring.
findingsNoOptional (feedback-loop, agent path): the requester real/false validation per severity bucket. Counts only — never finding text. Each count must not exceed the bucket count the call actually produced.
request_idYesThe UUID of the consensus call to rate. Returned by tokonomix_consensus_ask.
helped_modelNoOptional bare model slug of the one model whose minority view or blind-spot catch actually helped (e.g. "gemini-2.5-pro"). Blind-spot direct credit — opt-in.
consensus_benefitNoYour structured verdict on whether the council helped: caught_blind_spot / resolved_disagreement / raised_confidence / no_added_value / consensus_was_wrong. Replaces the old discarded free-text note with a privacy-safe categorical signal. Optional (feedback-loop, accepted only when the platform feedback-loop is enabled).
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It covers idempotency patterns (per-account dedup, last-write-wins), authentication requirements, feature-gating with a non-error response, storage/privacy guarantees (admin-only, never returned, EU-hosted enrichment), and the relationship between auto-scoring and user-supplied bucket counts. This is exemplary transparency.

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 front-loaded with the core purpose and logically progresses through optional parameters before behavior. It is nevertheless lengthy and includes some non-essential details (e.g., 'INT-1882', 'red-thread blind-spot differentiator', 'Opt-in, no friction') that could be trimmed without losing practical guidance. Dense but slightly overpacked.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 7 parameters, nested objects, and no output schema, the description covers input semantics, privacy, dedup, auth, feature-gating, and even the discount incentive. However, it does not describe the successful return value or acknowledgment after a rating is submitted; this is a minor gap given the tool's rating confirmation purpose. Overall, it is nearly complete for the agent's decision-making.

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

Parameters5/5

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

Schema coverage is 100%, so baseline is 3, but the description adds substantial meaning beyond field names. It explains how to obtain request_id ('billing breakdown line' and 'x_council.request_id' metadata), clarifies helped_model's blind-spot credit semantics, details that findings are counts only and must not exceed produced bucket counts, and positions consensus_benefit as a privacy-safe replacement for free text. This is far more than schema labels provide.

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 opens with a specific action: 'Rate a consensus call 1–10 on real-world usefulness, after you have seen the answer play out.' It clearly identifies the resource (consensus call), references the originating tool (tokonomix_consensus_ask), and distinguishes itself from sibling tools by focusing on post-hoc rating rather than asking or listing.

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 explicitly states when to use the tool ('after you have seen the answer play out'), imposes a precondition ('Requires the same API key that made the original call'), and mentions the feature-gated behavior with a clear 'not enabled' message. It does not explicitly name alternatives like tokonomix_relay_human_feedback, but it does contrast agent-source feedback from human-feedback summaries, implying a specific niche.

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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