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Convene a council

convene_council

Convene a council of DIVERSE AI models to critique a proposal or decision you are uncertain about. Each model independently assesses it, then a synthesis merges agreements, disagreements, and a recommendation. Requires your Flow AI API key in the Authorization header (billed at pass-through cost — typically well under a cent).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelsNo2-6 model ids for the council (default: a diverse cost-band trio)
contextNooptional background the council should know
proposalYesthe solution/decision/plan to review
synthesizeNomerge opinions into one recommendation (default true)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations only signal read-only false and non-destructive, so the description adds useful behavioral context: it requires an API key, costs money, and involves independent assessment plus synthesis. It does not contradict the annotations and discloses relevant operational side effects.

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?

Two sentences, front-loaded with the core purpose, followed by the key authentication/cost fact. Every sentence earns its place; no wasted words.

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 four well-described parameters, no output schema, and non-destructive but billed behavior, this description covers the necessary context: what it does, when to use it, and the cost/authentication implication. Minor details about exact return format are not supplied, but the overall behavior is clear enough.

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 coverage is 100%, so the schema already explains all parameters with descriptions. The tool description reinforces the idea of diversity and synthesis but adds little meaning beyond what the schema already states, fitting the baseline 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific action (convene a council), the resource (diverse AI models), and the outcome (critique and synthesis). It is easy to distinguish from most siblings, though it doesn't explicitly name an alternative to contrast with.

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 gives clear context on when to use the tool: when uncertain about a proposal or decision. It does not, however, name alternatives or provide when-not-to-use guidance, so it stops short of a fully explicit routing.

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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