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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

A4.6/5.0
Behavior5/5

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

The description adds significant behavioral details beyond annotations: explains monotonicity checks, partition-sum validation, Jaccard similarity filtering, placeholder handling, fill checking against CLOB depth, and what the response contains. Annotations already indicate read-only, open-world, idempotent, non-destructive, and the description aligns without contradiction.

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 thorough and front-loaded with the main purpose and modes, but it includes detailed algorithmic and output explanations that could be streamlined. Every sentence adds value, but the length reduces conciseness. Structure is logical, starting with usage then response details.

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?

Despite no output schema, the description fully outlines the response structure (opportunities[], partition_check, fill check) and operational details. It covers input modes, behavioral filters, and ties to another tool for custom sizing. The tool's complexity is well-addressed, leaving no significant gaps.

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

Parameters4/5

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

Schema coverage is 100% with descriptions for both parameters. The description enriches these by explaining the behavior for each parameter (e.g., event slug usage, topic as seed question), providing examples, and clarifying output implications, thus adding meaning beyond the schema.

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 differentiates itself from sibling tools like polymarket_edges and polymarket_fill_risk by specifying its unique algorithmic approach.

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?

Explicit guidance is provided for when to use each mode: no args for trending scan, event for a specific market (with examples), topic for cross-event scanning. It also mentions the related tool polymarket_fill_risk for custom sizing, but does not explicitly state when not to use this tool vs. siblings like polymarket_edges. Still, the context is clear and actionable.

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
Disambiguation4/5

Most tools have distinctly described purposes, but there is some overlap among query tools like ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim, which could cause confusion for an agent.

Naming Consistency3/5

Tool names are consistently snake_case but vary in pattern: some are verb_noun (ask_pipeworx, compare_entities), while others are noun_noun (entity_profile, polymarket_arbitrage) or longer phrases (scan_competitor_ai_presence), making the naming system inconsistent.

Tool Count2/5

With 33 tools, the server feels over-scoped. Many tools are niche (e.g., polymarket-specific ones) and the high number exceeds the typical range for a focused server, leading to potential overwhelm.

Completeness3/5

The server covers a broad range of query and monitoring tasks for its domains, but lacks write operations (except memory tools). There are notable gaps like no tool for creating or editing ScienceBase items, which seems incomplete given the server's name.

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