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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 indicate readOnlyHint and idempotentHint, and the description adds substantial behavioral context: scanning process, partition checks, similarity thresholds, placeholder filters, and fill check logic. No 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 comprehensive but somewhat verbose; it could be slightly more concise. However, it is well-organized and front-loaded with the core purpose.

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?

No output schema, but the description fully explains the response structure (opportunities, partition_check, fill_check), covers edge cases (low similarity, placeholder filters), and provides trading guidance. Complete for such a complex 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?

Both parameters are fully described in the schema, and the description adds extensive context: event slug examples, topic seed question examples, and detailed explanations of how each mode works.

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 it finds arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes from siblings by being specific to Polymarket arbitrage and detailing the mechanisms.

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?

It explicitly explains when to use each call mode (no args for trending, event for specific market, topic for cross-event), including when to use event over topic, and provides examples.

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

A3.8/5.0
Disambiguation2/5

Several tools are functionally near-identical: ask_pipeworx_beta is explicitly a duplicate of ask_pipeworx (descriptions say they 'currently match exactly'), and ask_pipeworx_grounded differs only in answer-extraction mode. The six Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) heavily overlap on edge-finding and fill-risk, and ai_visibility_check duplicates scan_competitor_ai_presence's per-entity probing. Agents will frequently misselect among these.

Naming Consistency4/5

Naming is consistently snake_case with a mostly verb_noun pattern (get_post, top_launches, subscribe, forget, validate_claim, resolve_entity). Minor deviations exist where nouns lead (entity_profile, recent_changes, recent_alerts, bet_research), and polymarket_* names use a domain-prefix style rather than pure verb_noun, but the overall pattern is predictable and readable.

Tool Count2/5

33 tools is at the heavy end, and the count is badly mismatched to the server's stated identity: it is named 'Producthunt' yet only 2 of 33 tools (get_post, top_launches) are Product Hunt related — the rest are Pipeworx data lookup, prediction-market, memory, and subscription tools. The redundancy (duplicate ask_pipeworx_beta, overlapping polymarket tools) inflates the count without adding surface.

Completeness2/5

Judged against the Product Hunt domain implied by the server name, the surface is severely incomplete: coverage is limited to list-top-launches and get-one-post, with no search, users, comments, votes, collections, or categories — and no way to act on Product Hunt data at all. As a general Pipeworx data platform the coverage is broader, but for the named purpose there are large gaps that will force agent failures.

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