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emojihub

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.

TDQS

A4.9/5.0
Behavior5/5

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

While annotations mark the tool as read-only and idempotent, the description discloses critical behavioral details beyond that: the 3pp threshold for partition signals, placeholder filtering logic, and the fill-check pricing semantics (realizable_edge_pp ≤ 0 means do not trade). This adds substantial value beyond structured 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 long and dense, but it's front-loaded with the main purpose and uses clear section labels (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK). There is some repetition (partition_check mentioned twice), but every section adds operational value.

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 lacking an output schema, the description spells out the response structure (opportunities[], partition_check, fill check), edge cases (thin_legs, skipped_low_similarity), and points to polymarket_fill_risk for sizing. For a complex multi-mode tool, this is remarkably complete.

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?

The schema descriptions for event and topic are already detailed, but the description enriches them with concrete examples (event slugs, seed questions), mode-specific behaviors, and the semantic-anchor rule (Jaccard ≥0.30). This goes well beyond a standard schema definition.

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+resource: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It clearly distinguishes the tool's purpose from siblings by detailing event and topic modes, and mentions related tools like 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?

Provides explicit usage guidance: call with no args for trending scan, pass event for specific markets, pass topic for cross-event scans. It even recommends which mode to use and directs to polymarket_fill_risk for custom sizing, offering 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

A3.9/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but there is some overlap among the numerous Pipeworx query and prediction market tools (e.g., polymarket_arbitrage vs. polymarket_edges vs. polymarket_fill_risk). Descriptions help differentiate them, so the ambiguity is minor.

Naming Consistency3/5

Tool names use a mix of verb_noun patterns (e.g., list_subscriptions, validate_claim), phrases (ask_pipeworx, bet_research), and standalone nouns (pipeworx_feedback). While readable, the lack of a single consistent convention makes the set feel less cohesive.

Tool Count3/5

33 tools is on the high side, with many highly specialized prediction market and Pipeworx management tools. The server's name 'emojihub' suggests a narrow focus, but the actual scope is much broader, making the count feel somewhat inflated for its apparent purpose.

Completeness4/5

The tool set covers a vast domain: factual data retrieval, company profiles, comparisons, claim validation, prediction market analysis, memory, subscriptions, and emoji lookup. Minor gaps exist (e.g., no direct tool for simple web search), but overall coverage is comprehensive.

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