llm_council

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

It accurately discloses the core orchestration behavior: parallel broadcast to multiple members, automatic judge critique/cross-examination, and delivery of a single consensus solution. However, with no annotations at all, the description leaves important operational traits unstated, such as external API/cost implications, credential requirements, and behavior if a member fails.

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 two sentences with no filler. The core mechanism is front-loaded, and the examples ('Dahl, DeepSeek, Groq, Ollama') add useful specificity without bloating the text.

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?

For a complex 6-parameter nested tool with no annotations and no output schema, the description gives a solid conceptual frame and tells the agent what kind of result to expect ('single optimal consensus solution'). It does not cover return shape, failure behavior, or operational caveats, so it is only minimally complete for safe and correct invocation.

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?

The schema already provides 100% parameter description coverage, including defaults for judge and members. The description reuses the same conceptual labels like 'Chief Justice' and 'council members' without adding parameter-specific syntax, constraints, or relationships beyond the 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 opens with 'Multi-Model Consensus & Council Deliberation' and clearly states it broadcasts problems in parallel to multiple LLM members and synthesizes via a Chief Justice. This gives a specific verb, resource, and workflow that conceptually separates it from single-query siblings like llm_query. It does not explicitly contrast sibling tools, so it just misses the top score.

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?

It names concrete use cases — 'complex problem, code architecture, or security audit' — giving an agent clear when-to-use context. It does not name alternatives or state when not to use the council, so it provides clear context without explicit exclusions.

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