brain_analytics

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

No annotations are provided, so the description must disclose behavior. It mentions the database is local/dev and flags feedback famine, offering some transparency about data source and a specific output feature. However, it does not disclose whether the tool is read-only, what the response format is, or error behavior, leaving gaps.

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?

Two sentences, with the core function in the first and context/caveat in the second. Every word adds value; no redundancy or unnecessary detail.

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 zero-parameter, no-output-schema tool, the description covers the key aspects: the data source (local/dev SQLite), the tables (prediction/feedback), the metrics (row counts, recency), and a unique flag (feedback famine). It is self-contained enough for an agent to decide if this is the right tool, though more detail on return format would help.

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 tool has zero parameters, and the schema is empty, so there is nothing to document. The description adds no parameter info, but the baseline for zero-parameter tools is 4, and the description is consistent with that.

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?

Description clearly states the tool provides row counts and recency for specific prediction/feedback tables in a local dev SQLite database, distinguishing it from siblings like brain_db_schema or brain_ml_models. The scope (local/dev) and the 'flags feedback famine' detail give clear purpose.

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 notes this is local/dev rather than production, providing a caution for non-production use, but it does not specify when to choose this tool over siblings or any exclusion criteria. No explicit alternative tools are named, so usage guidance is minimal.

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