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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

A4.9/5.0
Behavior5/5

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

Annotations declare readOnlyHint and idempotentHint, which align with the read-only computation nature. The description adds rich behavioral details: monotonicity checks, partition sums, semantic anchoring, placeholder filtering, and CLOB depth fill checks. 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?

Description is thorough and well-structured, starting with the core purpose then detailing each mode and behavior. Every sentence provides useful information, though slightly verbose for some readers. Could be tightened without losing clarity.

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 complex tool with no output schema, the description fully explains return fields (opportunities array, partition_check object), covering all modes and edge cases (low similarity, placeholder fraction, fill check). Includes practical warnings and cross-reference to sibling tools, making it self-contained enough for correct invocation.

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 already documents both parameters. The description adds significant value by explaining the behavior of each mode with examples (event slug type, topic seed question), how the tool processes them (walks child markets, searches related events), and the algorithm applied. High schema coverage does not diminish the added semantic value.

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?

Clearly states it finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. Verb and resource are explicit, and it distinguishes 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?

Explicitly describes three modes (no args for trending_scan, event for specific market, topic for cross-event scan) and provides recommendations. Includes when-not-to-use warnings such as the Jaccard similarity threshold and placeholder filters, plus the fill check condition for not trading when realizable_edge_pp ≤ 0. References sibling tool 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
Disambiguation4/5

Most tools have clearly distinct purposes, but there is some overlap between research-oriented tools like ask_pipeworx and deep_research, and between entity_profile and compare_entities. Descriptions help differentiate them, so overall an agent can tell them apart.

Naming Consistency2/5

Tool names are inconsistent, mixing snake_case (ask_pipeworx, list_subscriptions) and camelCase (bag_research, compare_entities). There is no uniform naming pattern, which can be confusing.

Tool Count2/5

With 35 tools, the set is too large for a server named 'Mastodon'. Only a few tools are actually Mastodon-related (e.g., get_account, get_timeline), while the majority are PipeWorx tools unrelated to the core purpose.

Completeness1/5

As a Mastodon server, the toolset is severely incomplete: it lacks basic social media operations like posting statuses, following/unfollowing, and engaging with content. The name misrepresents the actual capabilities.

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