Generate research prompts for multiple trending Polymarket markets at once, apply a minimum volume filter, and write analyzed reports to Notion for review.
Generate research prompts for multiple trending markets at once. The agent should process each, analyze, and write reports to Notion.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of markets to research | |
| min_volume_usd | No | Minimum volume filter in USD |
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It does disclose the main side effect—writing reports to Notion—and indicates an agent-driven multi-step flow. It does not mention potential long-running behavior, external service dependencies, or what happens if a market fails, leaving moderate transparency gaps.
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 a single sentence with no filler and front-loads the primary purpose. The second clause adds needed behavioral context about processing and Notion output, though the run-on structure slightly reduces crispness.
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?
The description covers the main action, batch scope, and Notion side effect, but omits return behavior, execution expectations, and any explicit differentiation from sibling tools. Given the absence of annotations and an output schema, it is adequate but not comprehensive for a multi-step orchestration 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?
Schema coverage is 100%, so the baseline is 3. The description adds no parameter-specific meaning beyond the schema, but the parameters themselves are well documented in the schema with defaults and bounds.
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 clearly states a batch operation over multiple trending markets and names the downstream effect of writing reports to Notion, distinguishing it from singular research tools like auto_research_market. The phrase 'Generate research prompts' is slightly ambiguous about whether the tool itself performs research or merely produces prompts, but the overall intent is discernible.
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?
Usage context is implied: use when multiple trending markets need research and Notion reporting. However, there is no explicit guidance about when not to use this tool or which sibling to prefer for single-market research, direct analysis, or formatting-only tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.