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

No annotations exist, so the description carries the full burden and does meaningful work: it discloses that raw engine fields are deliberately withheld and that execution_hint.preferred_direction is derived from key_direction alone and may not correspond to a real setup. It omits read-only/auth/rate-limit traits, but the derivation caveat is a genuinely useful behavioral disclosure.

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?

Purpose and output inventory are front-loaded in the first sentence, followed by two dense but high-value caveats (raw-field exclusion, preferred_direction trap). No filler; every clause carries information.

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 no output schema and no annotations, the description compensates by enumerating returned fields and flagging the main pitfall in acting on the execution hint. It is largely complete, though it does not state the read-only nature or default-timeframe behavior.

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 67%, with tf's enum and default documented in the schema itself. The description implies timeframe selection and multi-TF output but adds no syntax or semantics for tf or include_tfs beyond what the schema already provides.

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?

Names a specific verb+resource ('AI-synthesized market reading for a symbol/timeframe') and enumerates the payload it returns (state description, bias scores, break targets, MTF verdict, execution hint). It also explicitly differentiates itself from siblings by pointing to decker.get_market_state and the REST raw contract for engine-native fields.

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?

Gives clear when-not guidance: for engine-native raw fields use GET /public/reading or decker.get_market_state instead. It also warns to verify a non-null setup before acting on preferred_direction. Lacks an explicit 'use this tool when you want the customer-facing view' framing, but the context is inferable.

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