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by io.github.pipeworx-io

Polymarket Arbitrage

polymarket_arbitrage
Read-onlyIdempotent

Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a trending_scan of the top ~200 markets by weekly volume; pass event for the strongest per-event partition_check, or topic for a themed cross-event scan. event (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). topic (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
eventNoSingle-event mode (use this if you know the specific Polymarket event): event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k". Full Polymarket URLs also accepted.
topicNoCross-event mode (use this if you want to scan related events across the platform): a topic or seed question like "Fed rate decision" or "Strait of Hormuz traffic returns to normal". Tool searches Polymarket for related events and checks monotonicity across them.

TDQS

A4.9/5.0
Behavior5/5

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

Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) are consistent. The description adds extensive behavioral details: monotonicity checks, partition_sum, filler thresholds (3% gap), semantic anchor with Jaccard similarity, partition filter with placeholder fraction, and fill check comparing theoretical vs realizable edge with a warning not to trade when realizable edge <= 0. This far exceeds what annotations alone provide.

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

Conciseness4/5

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

The description is slightly verbose (multiple paragraphs) but well-structured with section-like breaks and CAPS for key terms. It front-loads the purpose and uses clear separators for modes. While some details could be trimmed, the structure aids readability for an AI agent.

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 the tool's complexity and lack of output schema, the description thoroughly covers the response structure (opportunities[] array with fields, partition_check object in event mode, fill check behavior). It also references sibling tools for custom sizing. All necessary information for understanding inputs, outputs, and edge cases is present.

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

Parameters5/5

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

Schema coverage is 100% with both parameters documented. The description adds significant context beyond the schema: it explains the recommended use for each parameter (event for specific markets, topic for cross-event scanning) and provides examples of acceptable values (e.g., 'fed-decision-may-2026' for event, 'Fed rate decision' for topic). This helps the agent understand parameter semantics fully.

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: finding arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes three modes (no args, event, topic) and provides examples of each, making it easy to understand what the tool does and differentiating it from sibling tools like polymarket_edges and polymarket_fill_risk.

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

Usage Guidelines5/5

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

The description explicitly guides when to use each mode: 'Call with NO args for a trending_scan', 'pass event for the strongest per-event partition_check', or 'topic for a themed cross-event scan.' It explains why cross-event mode is useful for catching patterns single-event misses, and directs users to polymarket_fill_risk for custom sizing. This provides clear decision criteria.

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

A3.7/5.0
Disambiguation4/5

Most tools have distinct purposes despite overlapping domains like prediction markets, but detailed descriptions help agents differentiate. A few tools like `ask_pipeworx` and `deep_research` could be confused without careful reading.

Naming Consistency2/5

Naming patterns are inconsistent, mixing `ask_`, `polymarket_`, `scan_`, `recent_`, `entity_`, etc., with no unifying convention. The server name 'Hash' mismatches the tool set entirely.

Tool Count3/5

32 tools is on the high side for a focused server, but the set covers many areas. The count is slightly above the typical 3-15 range, yet each tool has a clear purpose.

Completeness2/5

The tool set feels like a collection of unrelated utilities rather than a coherent domain. Core hashing functionality is minimal, while other areas like prediction markets are over-represented with gaps elsewhere.

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