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pg_kalshi_orderbook_analysis

Forensic orderbook analysis for one Kalshi market. Detects manipulation-vulnerable patterns: (1) wide spread (>$0.10), (2) shallow depth (<10 contracts), (3) few price levels, (4) single-order dominance (>80% in top level), (5) penny-wall pattern (large bids at ≤$0.005, commonly used to fake depth). Returns 0-100 score, severity, and full level-by-level data. Kalshi returns bids only — implied asks computed via yes_bid + no_bid = $1 reciprocity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesKalshi market ticker

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 full burden. It discloses the bid-only nature of Kalshi data and the reciprocity calculation, plus the return structure. It does not mention permissions, rate limits, or side effects, but given the read-only analysis context, this is adequate.

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 well-structured with a clear purpose statement, numbered pattern list, and return summary. Every sentence provides valuable information without unnecessary verbosity.

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?

Despite lacking an output schema, the description explains the return values (0-100 score, severity, level-by-level data) and a key data behavior (bid-only). It does not cover error handling or invalid tickers, but for a single-parameter tool this is nearly complete.

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 coverage is 100% with one parameter (ticker) described as 'Kalshi market ticker'. The description adds no additional parameter details beyond the schema, so the baseline of 3 applies.

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 'Forensic orderbook analysis for one Kalshi market' and enumerates specific manipulation-vulnerable patterns, distinguishing it from sibling tools like thin market alerts or market details. It defines exactly what the tool does and what it returns.

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 implies usage for deep-dive orderbook analysis on a single market, but does not explicitly address when to prefer this over alternatives (e.g., pg_kalshi_thin_market_alert). It provides clear context but no explicit exclusions or comparisons.

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.

Resources