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xkcd

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?

Despite annotations already declaring readOnly=true, openWorld=true, idempotent=true, destructive=false, the description adds rich behavioral context: the semantic anchor (Jaccard similarity threshold), partition filter (placeholder fraction >20% returns null), and the fill check (realizable_edge_pp ≤ 0 means do not trade). This goes well beyond the annotation coverage.

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 lengthier than typical, but every sentence conveys essential information. It is front-loaded with a clear summary, then details modes, filters, response format, and fill check. While dense and structured, it could be slightly more concise by trimming some redundancy (e.g., repeating the recommendation to use event mode).

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 the tool's complexity and absence of an output schema, the description thoroughly documents the response structure (opportunities[], partition_check) and edge cases (skipped_low_similarity, null arb scenario, fill check caveat). It is complete enough for an agent to understand what to expect and when to act.

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 description coverage is 100%, but the description significantly augments meaning: it provides concrete slug examples, clarifies what each mode does with the parameter, and explains the internal processing steps. For instance, it describes how 'event' walks child markets and computes partition_check, which is not in 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 opens with a precise statement: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It clearly distinguishes the three invocation modes (default trending_scan, event, topic) and differentiates itself from sibling tools by naming polymarket_fill_risk for sizing.

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 specifies when to use each mode: 'Call with NO args for a trending_scan... pass event... pass topic...'. It also recommends event mode for a specific market, explains when cross-event mode is preferable, and references an alternative tool: 'For custom sizing use polymarket_fill_risk.'

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
Disambiguation2/5

Many tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, entity_profile, compare_entities, and validate_claim all serve data-lookup purposes with unclear boundaries. The three xkcd comic tools are distinct but are buried under 31 unrelated tools, making selection confusing.

Naming Consistency2/5

Tool names follow no consistent pattern: some use verb_first (get_comic, list_subscriptions), some are nouns (entity_profile, deep_research), some have prefixes (ask_pipeworx_*, polymarket_*), and others are vague (scan_dependency, generate_llms_txt). The mixing of styles across the set makes it hard to predict naming.

Tool Count2/5

With 34 tools and a server name of 'xkcd', the count is wildly disproportionate; only 3 tools relate to comics. Even as a general data-access server, 34 tools is heavy and many are meta-tools (discover_tools, suggest_questions) that add bulk.

Completeness3/5

For the actual Pipeworx data domain, the surface is fairly comprehensive: querying, grounded answers, research, entity profiles, comparisons, validation, subscriptions, and prediction-market analysis are covered. However, there are notable gaps like no fetch-by-URI tool and no xkcd search/list capability, making the set incomplete for its name and slightly incomplete for its inferred domain.

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