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Themeparks

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. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint), the description adds critical behavioral context: fill check against live CLOB depth, partition filter for placeholder slugs, semantic anchor for cross-event pairs, and when realizable edge is non-positive do not trade.

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 thorough but slightly verbose; it could be trimmed without losing critical information. However, it is well-structured with clear sections for each mode and additional notes.

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?

Given no output schema, the description comprehensively explains the response structure, including opportunities array fields, partition_check object, and fill check details. Covers edge cases and references related tools.

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 coverage is 100%, but description adds significant meaning: examples of slugs, accepted URLs, modes triggered, and details like 'Full Polymarket URLs also accepted'. It clearly defines what each parameter does beyond the schema.

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 clearly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes from siblings like polymarket_edges by specifying the exact methodology and modes (trending, event, topic).

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 on when to use each parameter: no args for trending scan, event for per-event partition check, topic for cross-event scan. Also explains when not to trade (fill check) and directs to polymarket_fill_risk for custom sizing.

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
Disambiguation3/5

Most tools have distinct, well-documented purposes, but several overlap or are explicitly redundant: ask_pipeworx_beta currently behaves identically to ask_pipeworx, discover_tools and suggest_questions both serve as discovery entry points, and scan_competitor_ai_presence wraps ai_visibility_check. The thematic split between theme-park, data-lookup, prediction-market, and memory tools also forces agents to navigate unrelated clusters.

Naming Consistency3/5

Naming is a mix of verb_noun (list_destinations, get_wait_times, remember, resolve_entity), noun_phrase (entity_profile, recent_changes, bet_research), and brand-prefixed nouns (polymarket_edges, pipeworx_trending). Within families the patterns are consistent, but across the set the conventions are inconsistent and sometimes reverse the verb/noun order, making the surface harder to predict.

Tool Count2/5

35 tools is heavy, and the server is named Themeparks yet only 4 tools actually relate to theme parks. The remaining 31 tools span Pipeworx data retrieval, prediction markets, memory, subscriptions, and feedback, creating a bloated and misaligned scope. A tightly scoped theme-park server would need far fewer tools, and a general data research server would not be named Themeparks.

Completeness2/5

For the implied theme-park domain, the surface is thin: list destinations, get entity metadata, get schedule, and get wait times cover basic lookups but omit search, attraction details beyond waits, historical data, pricing, dining/show info, and park updates. The non-theme-park tools are extensive, but they do not complete the server's apparent stated purpose.

Resources