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Hurricanes

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. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false), the description adds critical behavioral context: partition-check logic (sum of YES prices ≈1), semantic anchor (Jaccard similarity ≥0.30), partition filter (placeholder slugs), fill check against CLOB depth, and conditions where trades should not be executed. No contradictions with 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 lengthy but well-organized with clear section headers (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK). Every sentence adds value, though the density of information could be slightly reduced for skimmability without losing critical details.

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?

Despite having no output schema, the description fully specifies the response structure (opportunities[] with gap_pp, suggested_trade, etc., partition_check fields). It covers all modes, fill check, and error conditions (e.g., placeholder fraction >20% returns null). No gaps remain for agent understanding.

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?

With 100% schema description coverage, both parameters already have descriptions. The tool description significantly enriches them by explaining the internal behavior of each mode (e.g., event mode walks child markets, topic mode searches related events) and providing specific examples of accepted values.

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 explicitly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks. It clearly distinguishes three modes: no-args trending scan, event mode for specific markets, and topic mode for cross-event scanning. This provides precise purpose and differentiation from sibling 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 offers detailed guidance on when to use each mode: no args for trending scan, event for a specific market (recommended), topic for cross-event scanning. It explains the limitations of single-event mode and when cross-event mode is needed. It also references polymarket_fill_risk for custom sizing, providing clear alternatives.

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
Disambiguation4/5

Most tools have distinct purposes with clear descriptions, but ask_pipeworx_beta is nearly identical to ask_pipeworx, and the presence of several meta-tools may cause slight confusion. Overall, an agent can differentiate most tools.

Naming Consistency4/5

Tool names consistently use lowercase_with_underscores and follow an action_domain pattern (e.g., validate_claim, scan_competitor_ai_presence). There is a mix of verb-noun and noun-verb, but the pattern is predictable and readable.

Tool Count3/5

33 tools is on the higher end, but given the broad scope (finance, drugs, prediction markets, data retrieval, memory), the count is reasonable. However, the server name 'Hurricanes' suggests a narrower focus, making the count feel excessive for that domain.

Completeness4/5

The tool set covers a wide range of data domains and includes meta-tools for discovery, grounded answers, and subscriptions. Minor gaps exist (e.g., no non-US company data), but overall the surface is comprehensive for a general-purpose data server.

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