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predgeAI

whale-data-mcp

by predgeAI

Outcome attestation — verified settled truth (flagship)

predge_attest

Attest the settled outcome of a Polymarket market: get resolved status, resolution, and timestamp. Optionally verify whether a past yes/no signal matched the actual result.

Instructions

PAID (~$0.02). Resolved-outcome ATTESTATION for one Polymarket market: the SETTLED truth straight off the market row — resolved (bool), resolution (yes|no|null), resolved_at, and outcome_verified:{resolution:true}. Pure outcome-verified data — no modeled estimate anywhere. Optional side (yes|no): adds queried_side and correct (true iff side matches the resolved outcome) — use it to VERIFY whether a past signal, call, or advertised win-rate was actually right. Unknown/unresolvable market returns null and is NOT charged. Params: condition_id, side.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sideNo
condition_idYes
Behavior5/5

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

With no annotations, the description fully carries the behavioral burden: it discloses cost ('PAID (~$0.02)'), null/unresolvable behavior ('returns null and is NOT charged'), return shape ('resolved(bool), resolution(yes|no|null), resolved_at, outcome_verified'), and optional side effects. Nothing is hidden.

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 front-loaded with cost and purpose, then flows into behaviors, return fields, and optional parameter effect. Every sentence carries unique information; no filler.

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

Completeness5/5

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

Given no output schema and only two params, the description covers all essential context: cost, input, output fields, optional enhancement, null/uncharged path, and use case. It is self-contained.

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?

The description names both parameters ('Params: condition_id, side') and fully explains side's effect (adds queried_side and correct), but only implicitly defines condition_id as the market identifier via 'for one Polymarket market.' With 0% schema coverage, more explicit semantics for condition_id would help.

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 specifies a concrete verb+resource: 'Resolved-outcome ATTESTATION for one Polymarket market' and details the exact fields returned, distinguishing it from sibling attest tools (sports, kalshi, wallet) by the 'Polymarket market' scope and 'no modeled estimate anywhere' purity claim.

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?

It gives an explicit use case: 'use it to VERIFY whether a past signal, call, or advertised win-rate was actually right.' It also clarifies unsupported cases ('Unknown/unresolvable market returns null'), but doesn't name alternative tools (e.g., predge_sports_attest) for exclusion.

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

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