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pg_uma_status

Global UMA Optimistic Oracle health on Polygon. Scans event logs from OOv2 (and optionally OOv1) over a configurable window, tallies events by type (RequestPrice / ProposePrice / DisputePrice / Settle), computes per-hour rate, and derives a dispute/propose ratio — a leading indicator of oracle-level contention.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
include_v1NoAlso scan UMA OOv1 (legacy, default false)
blocks_backNoBlocks to scan back (default 5000 ≈ 3h, max 20000)

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations provided, the description carries the transparency burden. It explains the internal computation logic (event tallying, rate, ratio) but does not explicitly state whether this is a read-only operation, potential performance implications of 'blocks_back', or any authentication/rate-limit requirements. It adds useful behavioral detail but omits some safety and performance aspects.

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 concise, with two sentences. The first sentence states the purpose, the second details the operation. Every clause adds value; no filler or redundancy.

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?

The description explains the tool's inputs and computational outputs well, and since there is no output schema, it adequately conveys what the tool returns (tallies, rate, ratio). However, it does not mention error conditions, units for 'blocks_back' beyond the schema default, or how the output is structured beyond these metrics.

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 baseline is 3. The description reinforces the parameters by mentioning 'configurable window' (blocks_back) and 'optionally OOv1' (include_v1), but adds no new meaning beyond what the schema already provides.

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 clearly states the tool's purpose: it scans UMA Optimistic Oracle event logs on Polygon to compute health metrics. It specifies the resource (OOv2/OOv1 on Polygon) and the specific computations (event tallies, hourly rate, dispute/propose ratio), distinguishing it from sibling tools like pg_uma_market_resolution which focuses on market resolution.

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

Usage Guidelines3/5

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

The description implies the tool is for monitoring oracle health but does not explicitly state when to use it versus alternatives, nor provide exclusions or prerequisites. It gives context that it is a health indicator but lacks direct guidance on selection among siblings.

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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TDQS

A3.9/5.0
Disambiguation5/5

Each tool has a clear, distinct purpose covering different aspects of prediction market integrity (market analysis, wallet analysis, AML/KYC, alerting, reporting). There is minimal overlap risk, as even related tools (e.g., pg_insider_signal_scan vs. pg_information_advantage_score) are differentiated by input (market vs. wallet) and output type.

Naming Consistency4/5

All tools share the 'pg_' prefix and use descriptive snake_case names, making the set predictable. However, the verb/noun order is inconsistent (e.g., pg_whale_add vs. pg_market_details). The pattern is still clear and functional, so minor deviation from a strict verb_noun pattern.

Tool Count4/5

With 33 tools, the set is large but well-scoped for a comprehensive platform covering market analysis, wallet intelligence, compliance, and reporting. Each tool serves a distinct function, and the count is justified by the breadth of the domain, though it pushes the upper bound of 'reasonable'.

Completeness5/5

The toolset covers the full lifecycle of prediction market integrity work: from market discovery and integrity scanning to wallet analysis, entity resolution, AML/KYC, watchlist management, alerting, and SAR reporting. There are no obvious gaps for the stated purpose.

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