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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 declare readOnlyHint=true. Description adds extensive behavioral context: monotonicity violation detection, partition-sum checks, semantic anchor for Jaccard similarity, partition filter for placeholders, and fill check against live book depth. 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.

Conciseness4/5

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

Information-dense but somewhat long. Well front-loaded with core purpose. Each sentence adds value, though some detail could be streamlined without losing clarity.

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 description fully explains response structure (opportunities array, partition_check details, fill check results). Covers edge cases like placeholder slugs and low similarity. Very complete for a read-only scanning 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 coverage is 100% with good descriptions. Description adds substantial meaning: event accepts full URLs, topic is a seed question, and explains how each mode works in depth, including internal processing details.

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?

Clearly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks. Distinguishes three modes: no args for trending scan, event for specific event, topic for cross-event scanning. Specific and unambiguous.

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?

Provides explicit guidance on when to use each mode: event recommended for specific markets, topic for cross-event scanning. Includes fill check warning and references sibling tool polymarket_fill_risk for custom sizing. Also explains when not to trade.

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

Many tools occupy clearly different niches, but ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded and deep_research heavily overlap the same router concept, and bet_research/polymarket_edges/polymarket_arbitrage all target similar 'find an edge' territory. An agent would need to read very long descriptions carefully to avoid selecting the wrong tool.

Naming Consistency4/5

Names are consistently lowercase snake_case and usefully grouped by prefixes like bnm_, polymarket_, and pipeworx_, which makes the set fairly scannable. However, conventions mix verb-first names (ask_, discover_, resolve_, validate_) with noun-phrase names (entity_profile, recent_alerts, recent_changes), so it is not a uniform verb_noun pattern.

Tool Count2/5

36 tools is well beyond the typical well-scoped 3-15 tool range, and the server bundles several distinct domains: BNM data, the Pipeworx research platform, prediction-market analysis, and memory/subscription management. This breadth would be better split into separate focused servers.

Completeness4/5

The BNM-specific surface is well covered, with dedicated tools for the main series plus a generic bnm_endpoint passthrough for anything else. The broader data side is also unusually complete, with routing, grounded answers, deep research, entity resolution, profiles, comparisons, and claim verification; only minor gaps remain, such as no dedicated historical endpoint for some BNM series.

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