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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.

TDQS

A4.8/5.0
Behavior5/5

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

Annotations indicate readOnlyHint, idempotentHint, and no destructive hint. The description reinforces this by explaining it is a computation tool that does not execute trades. It details behavioral traits like partition filter (placeholder fraction check), semantic anchor (Jaccard similarity), and fill check against live CLOB depth, providing full transparency.

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 well-structured with clear sections for each mode and key concepts. It is informative but somewhat verbose, with unnecessary details like the placeholder filter mechanism. Still, it is efficient and front-loaded with the most important information.

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 no output schema, the description thoroughly explains the response structure: opportunities[] with fields, partition_check with sum_yes_prices, and fill_check with theoretical vs realizable edge. It also covers edge cases and references a sibling tool for further customization, making it complete for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema covers both parameters with descriptions (100% schema coverage). The description adds significant context beyond the schema: examples of slugs for event, seed questions for topic, and how each mode operates. This elevates from baseline 3 to 4.

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 three modes: no-args trending scan, event mode for specific markets, and topic mode for cross-event scanning. This specificity differentiates it from sibling tools like polymarket_edges.

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 guidance on when to use each mode: no args for trending scan, event for a specific market slug, topic for cross-event scanning. It also includes when-not-to-use instructions, such as not trading when fill check shows realizable_edge_pp ≤ 0, and references sibling tool 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

B3.3/5.0
Disambiguation2/5

The tool set mixes USDA food data tools with a large number of unrelated tools (Polymarket betting, AI visibility, npm scanning, etc.), causing significant overlap in purpose. Many tools like ask_pipeworx, ask_pipeworx_grounded, deep_research, and bet_research all perform research/lookups with similar scopes, making it difficult for an agent to select the appropriate tool.

Naming Consistency3/5

Tool names generally follow a descriptive verb_noun pattern (e.g., list_foods, search_foods), but there is inconsistency in prefixes (ask_pipeworx vs. pipeworx_feedback vs. polymarket_arbitrage) and some names are long and varied. The naming is readable but not highly predictable.

Tool Count2/5

35 tools is excessive for a server ostensibly focused on USDA Food Data Central. Many tools are unrelated to food (e.g., Polymarket, Kalshi, npm scanning, subscription management), making the server feel bloated and unfocused. A typical food data server would have 5-10 tools.

Completeness4/5

For the USDA FDC domain, the tool surface is complete: it includes list, search, get, and nutrient retrieval. However, the presence of many unrelated tools dilutes the server's focus. The food-specific operations are well-covered, but the overall server lacks coherence.

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