get_belief_velocity

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

No annotations provided, so description carries full burden. It explicitly states 'No LLM call' and 'reads from data/belief_changes.jsonl (pure math output)', fully disclosing the data source and non-generative nature. This provides complete transparency for a read-only tool.

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?

Description is front-loaded with purpose and efficiently explains key behavioral trait (no LLM) in one line. The Args section is structured and clear. Could trim minor redundancy, but overall each sentence earns its place.

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?

With all parameters optional and an output schema (implied), the description covers all needed context: data source, filtering, ordering (top movers), performance note, and parameter details. Complete for a data retrieval 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?

Despite 0% schema description coverage, the description includes an 'Args' section that clearly explains each parameter's purpose and default values, adding meaning beyond the schema's type and default fields. This fully compensates for the lack of schema descriptions.

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 it gets pre-computed belief velocity data for prediction markets, specifies it returns top movers filtered by topic and time window, and distinguishes itself by noting it reads from a local file with no LLM call. This provides a specific verb+resource and context that separates it from sibling tools like get_market_details or get_trading_signals.

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?

Implied usage: fast, math-based data with no LLM call. But lacks explicit guidance on when to use this vs alternatives (e.g., get_trading_signals) or when not to use it. No exclusions or alternative suggestions, making it minimally adequate.

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