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Data Paris

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

A4.9/5.0
Behavior5/5

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

Discloses internal logic: semantic anchor (Jaccard >= 0.30), partition filter (placeholder slugs), fill check with CLOB depth, and response structure. Annotations already declare readOnly, idempotent, openWorld; description adds valuable context 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?

Description is lengthy but well-structured, front-loading the core purpose then detailing each mode. Every sentence adds value, though some redundancy (e.g., repeated fill-check mention) could be trimmed.

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?

Covers all modes, edge cases (low similarity, placeholders), response format with fields, fill-check behavior, and references to companion tools. Without output schema, the description provides a comprehensive picture of tool behavior and outputs.

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 has 100% coverage with descriptions for both parameters. The description adds meaning: explains that 'event' accepts Polymarket URLs or slugs, how 'topic' triggers cross-event scanning with seed questions, and internal processes like flattening markets and checking partition sums.

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 states the tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. It distinguishes between three modes (no-args trending scan, event-specific, cross-event topic) and contrasts with 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?

Explicit guidance: call with NO args for trending scan, pass 'event' for specific market (recommended), or 'topic' for cross-event. It explains when to use each mode, recommends fill-check via polymarket_fill_risk for custom sizing, and warns against trading when realizable edge <= 0.

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

A4.2/5.0
Disambiguation5/5

Each tool targets a distinct function or data domain, from prediction markets (bet_research, polymarket_*) to company research (entity_profile, compare_entities) to data queries (query, dataset_info). No two tools have overlapping purposes; meta-tools like ask_pipeworx and deep_research are clearly differentiated.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with descriptive verbs (e.g., search_datasets, validate_claim, remember). No mix of conventions like camelCase or inconsistent verb choices.

Tool Count4/5

At 33 tools, the server covers a broad range of capabilities (data retrieval, AI visibility, prediction markets, company profiles, subscriptions, etc.). While slightly above the ideal range, the count is justified by the breadth of functionality and no tool feels redundant.

Completeness4/5

The tool set provides comprehensive coverage for data discovery, retrieval, and analysis across multiple domains (SEC, FDA, FRED, Paris Open Data, prediction markets, etc.). Minor gaps exist (e.g., no update/delete for Paris Open Data), but the primary focus on reading and analysis is well-served.

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