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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?

The description goes far beyond the annotations (readOnlyHint, idempotentHint, non-destructive). It discloses detailed internal behaviors: how event mode walks child markets and checks date/threshold ordering, how topic mode performs cross-event flattening, the ≥0.30 Jaccard similarity anchor, placeholder filtering, and the fill check with realizable vs theoretical edge. This level of detail ensures the agent understands what happens under the hood and what results to trust.

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 densely packs critical information with clear structural labels (SEMANTIC ANCHOR, PARTITION FILTER, RESPONSE, FILL CHECK). Every sentence serves a purpose: mode selection, examples, thresholds, response format, and usage caveats. There is no fluff or repetition, making it appropriately sized for the tool's complexity.

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?

Without an output schema, the description compensates by outlining the response structure (opportunities[], partition_check, suggested_trade, etc.) and covering key edge cases: fill-check conditions, similarity threshold, placeholder handling, and the 'do not trade if realizable_edge_pp ≤ 0' rule. This is complete enough for an agent to invoke the tool correctly and interpret 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?

Although the schema already provides 100% coverage of the two parameters, the description adds substantial meaning beyond the schema. It explains that 'event' switches to single-event mode with a slug or URL, gives concrete examples, and contrasts it with 'topic' mode. It also clarifies optional behavior when no args are provided (trending_scan), which the schema omits. This enriches parameter understanding significantly.

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 clear, specific verb and resource: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It distinguishes itself from sibling tools like polymarket_edges (edge detection) and polymarket_fill_risk (fill risk) by naming the unique arbitrage-detection approach and multiple scan modes.

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 explicitly states when to use each mode: 'Call with NO args for a trending_scan... pass event for the strongest per-event partition_check, or topic for a themed cross-event scan.' It also provides recommendations ('event (recommended for a specific market)') and points to an alternative tool for sizing ('For custom sizing use polymarket_fill_risk'). This gives clear context for choosing this tool over others.

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

B3.2/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical in function, while compare_entities and entity_profile both fan out across data sources. The PLOS-specific tools (article, search, recent) are distinct, but the broader set creates confusion about which entry point to use for general data questions.

Naming Consistency2/5

The naming is a mix of single-word nouns (article, recent), verb phrases (search_authored_by, validate_claim), and adjective_noun constructions (recent_changes, recent_alerts). While most multi-word names use snake_case, the lack of a consistent prefix or verb_pattern (e.g., some start with action verbs, others with data categories like polymarket_ or pipeworx_) makes the set feel inconsistent.

Tool Count2/5

At 35 tools, the server is heavily over-scoped for a PLOS journal interface—only 4 tools (article, search, search_authored_by, recent) actually relate to PLOS. The remaining 31 tools form a broad Pipeworx/Polymarket data platform, which suggests the server is trying to do far more than its name implies.

Completeness2/5

For the stated domain (PLOS), the surface is thin: basic search, fetch, recent, and author search lack advanced features like citation metrics, journal browsing, or full-text download links. Conversely, the Pipeworx tools are extensive but unrelated to PLOS, so the set is simultaneously over-complete in unrelated areas and incomplete for its apparent purpose.

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