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Semanticscholar

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds extensive behavioral context: details on semantic anchor (Jaccard similarity), partition filter (placeholder fraction), fill check against live CLOB depth, and response structure. No contradictions with annotations.

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

Conciseness3/5

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

The description is thorough but somewhat verbose, containing multiple sections and detailed explanations. While well-structured and front-loaded with key purpose, it could be more concise; every sentence earns its place but the length may deter quick comprehension.

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, the description comprehensively explains the response structure (opportunities[], partition_check details, fill check results) and references related tool polymarket_fill_risk. It addresses edge cases (placeholders, low similarity, thin legs) and provides sufficient context for a complex arbitrage tool.

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 describes both parameters with 100% coverage, but the description adds significant meaning beyond the schema: explains accepted formats (slugs, URLs, seed questions), behavior differences between event and topic modes, and internal processing details (walks child markets, searches related events, flattens markets).

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 from siblings by detailing specific algorithms and modes (trending scan, event mode, cross-event mode), providing a specific verb+resource with unique methodology.

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 provides explicit guidance on when to use each mode: no args for trending scan, event for a specific market, topic for cross-event scanning. It also explains when not to trade (fill check with realizable_edge_pp ≤ 0) and references polymarket_fill_risk for custom sizing, offering clear alternatives and exclusions.

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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, especially the core ones like get_paper, search_papers, and entity_profile. However, some pairs like ask_pipeworx and ask_pipeworx_grounded, or the polymarket tools, could cause momentary confusion, though descriptions help differentiate.

Naming Consistency2/5

Naming patterns are inconsistent: tools use verb_noun (e.g., get_paper), noun_phrase (e.g., polymarket_arbitrage), and bare verbs (e.g., forget, recall). There is no unifying pattern, and styles like 'pipeworx_feedback' vs 'search_papers' further add to the inconsistency.

Tool Count3/5

With 34 tools, the set covers a broad range of domains (academic papers, company data, prediction markets, memory, subscriptions). While the scope justifies the number, it feels slightly heavy and could benefit from consolidation or clearer grouping.

Completeness4/5

The tool set covers major functionalities for research, data retrieval, and monitoring, with only minor gaps (e.g., limited to US public companies, npm-only dependency scanning). Overall, the surface is comprehensive for the stated capabilities.

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