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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?

Annotations already mark the tool as read-only, open-world, and idempotent, but the description goes far beyond them by detailing exact behavioral thresholds (3pp deviation, 0.30 Jaccard, 20% placeholder cutoff), the partition-check logic, placeholder filtering, and the fill-check procedure against live CLOB depth. This is rich, non-obvious behavioral context with no contradiction to annotations.

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: it front-loads the core purpose, then uses labeled sections (SEMANTIC ANCHOR, PARTITION FILTER, RESPONSE, FILL CHECK) to pack complex behavioral rules into scannable chunks. Every sentence adds operational value, and the examples make the modes immediately understandable.

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 tool with two optional parameters and no output schema, this description is remarkably complete. It covers all invocation modes, output field names, filtering behavior, failure conditions, fill-check semantics, and even points to a sibling tool for custom sizing. An agent can select and invoke this tool correctly without any additional documentation.

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 describes both parameters, the description adds substantial meaning: it explains that no args triggers a trending scan, gives concrete example slugs and seed questions for 'event' and 'topic', and describes what each mode does internally ('walks child markets', 'flattens markets'), which is far beyond the schema's basic field 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 opens with a specific verb and resource: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It clearly distinguishes the tool from siblings by describing single-event and cross-event modes, and it even references polymarket_fill_risk for custom sizing, helping separate it from related tools.

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 when-to-use guidance for all invocation patterns: no args ('trending_scan'), 'event' (recommended for a specific market), and 'topic' (for cross-event scanning). It explains when cross-event mode is preferable and even includes a concrete do-not-trade instruction for when realizable edge is non-positive.

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

A3.6/5.0
Disambiguation1/5

Multiple tools have nearly identical purposes: ask_pipeworx and ask_pipeworx_beta are explicitly described as identical, and ask_pipeworx_grounded differs only in grounding. generate_users and generate_by_gender overlap, as do ai_visibility_check/scan_competitor_ai_presence and the several polymarket_* tools that all target edge detection and arbitrage. An agent would struggle to select the correct tool.

Naming Consistency2/5

All names use snake_case, but the pattern is inconsistent: some start with verbs (generate_users, resolve_entity, validate_claim), some are nouns (entity_profile, deep_research, recent_changes), and some are compound noun phrases (ai_visibility_check, pipeworx_feedback, polymarket_arbitrage). There is no predictable verb_noun structure across the set.

Tool Count2/5

33 tools is well above the 'too many' threshold, and the set includes many meta-tools, memory helpers, and niche prediction-market tools. While the broad domain might justify some diversity, the count feels bloated and dilutes the server's focus, especially given the server name suggests only random user generation.

Completeness4/5

For the apparent core domain (data lookups, research, prediction market analysis, subscriptions), the tool surface is quite comprehensive: it covers direct queries, grounded answers, deep research, entity profiles, comparisons, claim verification, discovery, trends, memory, and subscription management. Minor gaps exist (e.g., no direct CRUD for user-generated profiles beyond creation), but overall the feature set feels well covered.

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