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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

A5/5.0
Behavior5/5

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

Beyond the readOnlyHint annotations, the description details critical behavioral traits: the 3pp deviation threshold, the placeholder slug filter with >20% cutoff returning null, the skipped_low_similarity counter, and the fill_check logic that warns against trading when realizable_edge_pp ≤ 0. These are non-obvious behaviors that substantially affect how the agent should interpret results.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

The description is long but exceptionally well-organized. The first sentence states the core purpose, and subsequent sections are bolded (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK) for easy scanning. Every sentence adds actionable information; there is no fluff. The structure mirrors the tool's execution flow, making it easy for an agent to follow.

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?

For a complex tool with no output schema, the description provides a thorough picture: it describes the response fields (opportunities[], partition_check{}, fill_check), explains edge cases (placeholder fractions, low similarity, thin legs), and even mentions a related tool for further sizing. This gives an AI agent enough context to invoke the tool correctly and interpret its results.

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?

Even though the schema already covers both parameters, the description enriches them significantly. It explains that event accepts slugs or full URLs, gives example slugs, defines topic as a seed question, and clarifies that no args triggers a trending scan. It also clarifies the semantic difference between event (single-event) and topic (cross-event) modes, which the schema only hints at.

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 opens with a specific verb and method: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It clearly distinguishes the three invocation modes (no args, event, topic) and gives concrete examples. This differentiates it from 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 is provided for each mode: 'Call with NO args for a trending_scan', 'pass event for the strongest per-event partition_check', and 'pass topic for a themed cross-event scan'. It also recommends when to use each mode ('event (recommended for a specific market)') and points to an alternative tool for custom sizing ('For custom sizing use polymarket_fill_risk').

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

A4.1/5.0
Disambiguation3/5

Several tool families overlap at the boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same 5,724 tools, and ask_pipeworx_beta is currently functionally identical to ask_pipeworx. The Polymarket family is large but each member has a fairly distinct role (research vs. edge scan vs. fill risk vs. tracking); the memory trio and book tools are clear.

Naming Consistency3/5

Most tools follow a snake_case verb_noun pattern (get_book, create, search_books, resolve_entity, list_subscriptions), but there are notable exceptions: recall/remember/forget are bare verbs without a domain prefix, ask_pipeworx begins with a verb but doesn't follow the noun-object structure, and ai_visibility_check/generate_llms_txt break the pattern. It's readable and mostly predictable, but not uniform.

Tool Count3/5

35 tools is heavy and exceeds the typical well-scoped range, but the server is a meta-platform exposing a universal data router plus prediction-market analysis, book lookup, memory, subscriptions, and several composite research tools. Each tool appears to earn its place, though the set feels sprawling and would benefit from consolidation of the ask_pipeworx variants.

Completeness4/5

Coverage is thorough within the apparent domains: data lookup has multiple tiers (casual, grounded, deep research, claim validation), the Polymarket workflow is complete from research to edge discovery to fill-risk verification, memory has save/retrieve/delete, and subscriptions have create/list/cancel/pull. Minor gaps exist (e.g., book author search by name only via Open Library key, no direct tool for invoking a specific raw data pack), but nothing that would strand an agent.

Resources