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Arcgis Branson

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

A5/5.0
Behavior5/5

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

Even though annotations already declare the tool read-only and non-destructive, the description adds substantial behavioral context: it discloses the >3pp deviation threshold, the ≥0.30 Jaccard similarity anchor, the partition placeholder filter with >20% cap, the response structure, and the fill-check logic warning that realizable_edge_pp ≤ 0 means the trade should not be executed. This is far beyond what annotations provide and gives the agent critical operational details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

While long, every sentence earns its place given the tool's complexity. The description is front-loaded with the overall purpose and mode selection, then logically structured with labeled sections like SEMANTIC ANCHOR, PARTITION FILTER, Response, and FILL CHECK. It is dense but not redundant; the technical details are necessary for correct tool invocation and interpretation.

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 explains the primary response fields (opportunities[] with gap_pp, suggested_trade, reasoning, monotonicity violation context, and partition_check details). It covers all three invocation modes, parameter semantics, filters, and the fill-check process. It also links to a related tool for sizing, making the description 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.

Parameters5/5

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

The schema has 100% coverage for the two parameters, but the description greatly enhances them. It provides concrete examples for `event` slugs like 'fed-decision-may-2026' and notes that full URLs are accepted. For `topic`, it gives seed question examples and explains the cross-event search behavior. The description also explains the mode-dependent behavior and response differences, adding meaning well beyond the schema's simple type and description fields.

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 clear verb+resource+method: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It defines the exact scope (monotonicity violations and partition-sum checks) and distinguishes itself from sibling Polymarket tools by specifying the arbitrage methodology. The three modes (trending_scan, event, topic) are clearly outlined, leaving no ambiguity about what the tool does.

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 explicitly states when to use each mode: call with no args for a trending scan of top markets, pass `event` for a specific market, and pass `topic` for cross-event scanning. It even recommends `event` for a specific market and explains that cross-event mode catches patterns single-event misses. It also directs the agent to a sibling tool for related needs: 'For custom sizing use polymarket_fill_risk.' This provides clear when-to-use and alternative guidance.

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
Disambiguation5/5

All 33 tools have clearly distinct purposes, even those that seem related like ask_pipeworx, ask_pipeworx_grounded, and deep_research are well-differentiated by use case and behavior. Memory tools (remember/recall/forget) and subscription tools (subscribe/unsubscribe/list_subscriptions/recent_alerts) are similarly distinct.

Naming Consistency3/5

All tools use snake_case, but naming patterns are mixed: some are single verbs (forget, recall), some verb_noun (query_layer, search_datasets), some noun_noun (entity_profile, layer_info), and some longer phrases (polymarket_kalshi_spread, scan_competitor_ai_presence). While readable, the inconsistency makes the set feel less coherent.

Tool Count2/5

With 33 tools, the surface is overly broad for a server named after a specific ArcGIS dataset. Many tools are unrelated to the core purpose (e.g., polymarket tools, npm scanning, AI visibility), making the count feel bloated and unfocused.

Completeness2/5

For the stated ArcGIS Branson focus, only 3 tools (search_datasets, query_layer, layer_info) are relevant, offering only read access. The rest are a miscellaneous collection from the Pipeworx ecosystem and other domains, leaving obvious gaps in GIS functionality and no write or analysis capabilities.

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