council

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

With no annotations, the description carries full burden. It describes the process (topic selection, backend consultation, synthesis) but does not disclose potential issues like latency, cost, or failure modes. Some transparency is provided, but gaps remain.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (3 sentences) and front-loads the purpose with a clear title and summary. It provides necessary details without verbosity, though it could be more structured with bullet points.

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

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

The description covers purpose, usage, process, and parameter behavior adequately. No output schema exists, but the description explains the synthesis step. It feels incomplete regarding error scenarios or limitations, but generally sufficient for a multi-backend tool.

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

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

Schema coverage is 100% with descriptions for all parameters, so baseline is 3. The description adds context for topic and confidence_needed enums (backend mappings, counts), but this largely overlaps with schema descriptions. No significant additional meaning beyond schema.

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

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

The description clearly states the tool's purpose: 'Multi-AI Council - Get consensus from multiple AI backends on complex questions.' It specifies the action (get consensus) and resource (multiple AI backends), and distinguishes it from sibling tools like batch_analyze or dual_iterate by emphasizing consensus from multiple backends.

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

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

The description explicitly recommends use cases: 'Use for architectural decisions, controversial topics, or when you need validation from multiple viewpoints.' It mentions the process but does not explicitly state when not to use it or list alternatives, though the context implies it's for complex questions.

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