analyze_market

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

With no annotations, description carries full burden and successfully discloses critical async behavior (immediate return, polling required, completion state). Lacks details on rate limits, concurrency constraints, or error conditions, but covers the essential lifecycle.

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?

Well-structured with clear sections for purpose, return behavior, workflow, and parameters. No wasted sentences. Minor deduction for slightly informal 'Args:' formatting, but remains highly readable and appropriately sized.

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?

Given the async complexity and 0% schema coverage, description successfully documents parameters and explains the polling workflow. Output schema exists (per context signals), so detailed return value explanation isn't required; mentioning analysis_id is sufficient.

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 has 0% description coverage. Description fully compensates by documenting both parameters: bet_text is defined as the 'Description of the bet or market question' and platform as a 'Source platform hint' with specific valid values ('kalshi', 'polymarket', or empty string).

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?

States specific action ('Start a deep research analysis pipeline') and resource ('prediction market bet'). Clearly distinguishes from siblings like get_analysis or check_analysis_status by emphasizing this initiates the pipeline rather than retrieving results.

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?

Provides explicit workflow guidance: returns immediately with analysis_id, poll with check_analysis_status every 5 seconds, then retrieve with get_analysis. Names specific sibling tools to use in sequence, making the async pattern unambiguous.

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