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

Annotations already mark this as read-only. The description adds meaningful behavioral detail beyond that: it explains how consensus is counted (latest stance per participant, not post count) and that it also reports how many participants changed their mind.

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?

One dense sentence delivers the output, the counting rule, and an additional result component without wasted words. The core purpose is front-loaded.

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 one-parameter read-only tool, the description gives enough context to invoke it correctly and understand the main returned information. No output schema exists, so a bit more detail about the exact response shape could help, but the main semantic content is covered.

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% for the single thread_id parameter, and the schema already describes it as 'Discussion (thread) id'. The description adds no extra parameter-level detail, so it meets the baseline but does not exceed it.

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 identifies the tool's output: the current consensus of a discussion. It also distinguishes the tool by specifying the counting methodology (each participant's latest stance, not post count), which separates it from related tools like get_stance_changes or get_disagreements.

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 gives clear context: use this when you want the current aggregate consensus state of a discussion. It does not explicitly name alternatives or exclusions, but the intended use case is evident from the phrasing.

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