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

With no annotations, the description must carry behavioral disclosure. It notes two traits: candles are 'quality-flagged' and 'priced per 1,000 candles,' which gives some sense of quality processing and cost. However, it omits any mention of authentication, rate limits, data range behavior, or return structure, leaving critical unknowns.

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 extremely concise, using two short fragments that front-load the core output ('Quality-flagged OHLCV candles') and then the cost note. Every phrase adds some information, though it remains under-specified.

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 tool with four parameters, no output schema, and zero annotations, this description is far from complete. It does not explain parameter interactions, return fields, pagination, or any typical usage context, leaving the agent to rely on assumptions.

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 has 0% description coverage for its four parameters, and the description adds no parameter-specific meaning. An agent cannot infer what 'symbol', 'tf', 'start', or 'end' require regarding format, defaults, or constraints from either source.

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 identifies the resource as 'OHLCV candles' with a 'quality-flagged' qualifier, which conveys the tool's primary output. However, it lacks a verb (e.g., 'retrieves' or 'returns') and does not explicitly distinguish get_bars from sibling data tools beyond the acronym.

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 provides no guidance on when to use this tool over alternatives. There is no mention of contexts, exclusions, or comparisons to sibling tools like get_fundamentals or get_orderflow.

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