PolyDesk

format_watchlist_entry

Generate a Notion API properties object for adding a Polymarket market to a Watchlist database, including category, AI signal, and fair value fields.

Instructions

Generate the properties object for adding a market to the Notion Watchlist database. Returns the exact Notion API properties structure — pass it to the Notion MCP's API-post-page tool.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "market_id": "<market_id>"
    },
    "name": "format_watchlist_entry"
  }
}

Input Schema

Name	Required	Description	Default
`category`	No	Market category	Other
`market_id`	Yes	Polymarket market ID
`conviction`	No	AI signal	Hold
`fair_value`	No	AI estimated fair value

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 30, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations, the description carries the behavioral burden. It is transparent that the tool only generates and returns a structure rather than performing side effects, and it explicitly states that the result is meant for another tool to consume. It does not detail edge cases, but the non-mutating formatting behavior is sufficiently clear.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no filler. The first sentence states the core purpose, and the second adds the crucial integration context about how the result should be used. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a formatter with complete input schema coverage, the description explains both what it returns and how the return value should be used. There is no output schema, but the description clarifies that the output is the exact Notion API properties structure, which is sufficient given the tool's narrow scope.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all four parameters with descriptions and constraints. The tool description adds no additional parameter-level meaning, so the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Generate'), a precise resource (the properties object for adding a market to the Notion Watchlist database), and the exact deliverable (Notion API properties structure). It clearly differentiates the tool from siblings like watch, which likely performs the actual write, and format_research_for_notion, which targets a different format.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear usage context: use this tool to produce a properties object that is then passed to the Notion MCP's API-post-page tool. It does not explicitly name alternatives or exclusion conditions, but the downstream instruction leaves little ambiguity about when this tool is needed.

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