auto_research_market

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

With no annotations, the description carries the full burden of behavioral disclosure. It does mention a notable side effect — writing findings to Notion via the Notion MCP server — and the iterative ratchet behavior. However, it ambiguously attributes that write to 'the AI agent' rather than to the tool call itself, leaving uncertainty about what actually happens when this tool is invoked. It also omits prerequisites or potential non-deterministic behavior.

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 short, front-loaded with the primary purpose, and avoids redundant phrasing. The middle sentence is somewhat convoluted and could be split or clarified, but overall the length is appropriate for the tool's complexity.

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 no output schema and no annotations, the description gives useful workflow context: it generates a prompt, involves an AI agent, writes findings to Notion, and supports iterative refinement. However, it leaves ambiguity about the tool's return value, whether the write happens immediately as a side effect, and what the generated prompt should contain. These gaps make the definition minimally viable but not complete.

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 description coverage is 100%, so the schema already explains both market_id and iteration. The description adds little beyond restating the iteration ratchet concept already present in the schema's iteration description. It does not clarify formats, defaults, or how iteration affects output, so it provides no significant additional semantic value.

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 opening sentence names a clear verb and resource: it generates a structured research prompt for a Polymarket market. However, the following sentence blurs the tool's actual behavior by stating 'the AI agent should analyze the prompt, form a thesis, then write findings to Notion' — it is unclear whether the tool merely produces a prompt or also performs the research/write. It also does not distinguish itself from siblings like research_with_ollama or batch_research.

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 gives a clear usage hint for the iteration parameter: 're-run with higher iteration to refine.' It does not explain when to choose this tool over sibling research tools, nor does it mention exclusions or prerequisites such as requiring the Notion MCP server to be configured. The usage context is implied rather than explicit.

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