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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.

Schema Changelog

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

  1. Changed1 schema field changed
    • removedInput schema / anyOf
      Removed value: -[
-  {
-    "required": [
-      "event"
-    ]
-  },
-  {
-    "required": [
-      "topic"
-    ]
-  }
-]
  2. Changed3 schema fields changed
    • addedInput schema / anyOf
      Added value: +[
+  {
+    "required": [
+      "event"
+    ]
+  },
+  {
+    "required": [
+      "topic"
+    ]
+  }
+]
    • changedInput schema / properties / event / description
      Previous value: -"Single-event mode: Polymarket event slug (e.g. \"when-will-bitcoin-hit-150k\") or full URL."New value: +"Single-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."
    • changedInput schema / properties / topic / description
      Previous value: -"Cross-event mode: a topic or seed question. Tool searches Polymarket for related markets across separate events and checks monotonicity across them. E.g. \"Strait of Hormuz traffic returns to normal\"."New value: +"Cross-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."
  3. Added

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds substantial behavioral context: thresholds (3pp deviation, 0.30 Jaccard, 20% placeholder fraction), the fill check logic (realizable_edge_pp <= 0 means do not trade), and the null signal behavior for partitions with high placeholder fraction. It also mentions skipped_low_similarity, disclosing rejected pair counts, going well beyond the 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?

The description is long but well-structured, using capitalized section headers (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK) to organize details. It is front-loaded with the core purpose and usage modes. While some technical internals (like fill check specifics) might be trimmed for an AI agent, each sentence carries meaningful information for a complex tool.

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?

With no output schema, the description fully specifies the response structure (opportunities[] with gap_pp, suggested_trade, reasoning, and partition_check{} fields). It covers edge cases such as placeholder slugs, low similarity filtering, and fill check outcomes. The reference to polymarket_fill_risk for custom sizing rounds out the guidance, making it complete for the tool's complexity.

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 description coverage is 100%, with both parameters already explained. The description adds significant value: it explains the no-args trending_scan, provides examples for both event and topic, details what happens in each mode (walking child markets, checking date-axis/threshold-axis ordering, partition check), and notes that full URLs are accepted for 'event'. This goes beyond schema-level descriptions.

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 a specific verb and resource: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It clearly differentiates from sibling tools by describing the monotonicity/partition approach and explicitly referencing polymarket_fill_risk for custom sizing. The three modes (trending_scan, event, topic) further define the tool's scope.

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 instructions: 'Call with NO args for a trending_scan', 'pass event for the strongest per-event partition_check', and 'topic for a themed cross-event scan.' It also gives concrete examples of event slugs and topic seeds, explains when cross-event mode is beneficial, and directs to 'polymarket_fill_risk' for custom sizing, effectively covering when and when not to use the tool.

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

A3.5/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx and ask_pipeworx_beta are currently identical, deep_research overlaps with ask_pipeworx, and discover_tools/suggest_questions both serve a discovery role. The extremely detailed descriptions help, but the boundaries between meta-tools and research tools are genuinely confusing, especially with 34 tools in one namespace.

Naming Consistency2/5

Naming is a mix of bare verbs (get, search, recall), nouns (affiliation, entity_profile, recent_changes), and verb_noun phrases (resolve_entity, compare_entities, validate_claim). Some families are consistent (polymarket_*), but overall there is no uniform convention or prefix scheme, making the set feel arbitrary.

Tool Count2/5

34 tools is well above the 25+ threshold for 'too many.' While the broad data-platform scope explains some of the count, many tools are meta-utilities (feedback, trending, memory, subscription management) and there are near-duplicate variants (three ask_pipeworx forms, six Polymarket tools) that inflate the surface.

Completeness4/5

For a data-research platform the surface is impressively complete: lookup, grounded verification, entity profiles, comparisons, claim checking, subscription lifecycle, memory, and tool discovery are all covered. Minor gaps exist (e.g., no direct general-purpose web fetch, and ROR lacks create/update, which is acceptable for a curated registry), but agents should rarely hit dead ends.

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