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Arcgis Peoria

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?

Even though annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, the description adds rich behavioral context: the fill check (realizable_edge_pp ≤ 0 means don't trade), the Jaccard similarity anchor (≥0.30), the placeholder filter (>20% returns null), and the response structure (opportunities[], partition_check). This goes well beyond what annotations convey, with no contradictions.

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 but well-structured and front-loaded. The first sentence states the core function. Subsequent sections clearly separate event mode, topic mode, semantic anchor, partition filter, response, and fill check. While every sentence adds value, the length is near the upper bound for a tool description, so it doesn't reach a 5.

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?

Considering the tool's complexity (multiple modes, subtle fill-check logic, response fields) and the lack of an output schema, the description is exceptionally complete. It explains the full decision pipeline: how modes are chosen, what filters apply, what the response contains, and how to interpret the fill check. No important aspect appears missing.

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?

Schema description coverage is 100% (both event and topic have detailed schema descriptions), so baseline is 3. The description adds extra meaning by providing concrete examples (e.g., 'fed-decision-may-2026', 'Strait of Hormuz traffic returns to normal'), explaining behavior per parameter (walks child markets, flattens markets), and clarifying that no args triggers trending_scan. This exceeds the schema's basic descriptions.

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's purpose: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It specifies the resource (Polymarket) and the method (monotonicity + partition-sum). It also distinguishes itself from siblings like polymarket_fill_risk by mentioning custom sizing as a separate tool, and from polymarket_edges/edge_tracker by focusing on arbitrage rather than edge tracking.

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?

Explicit usage guidance is provided: 'Call with NO args' for trending_scan, 'event (recommended for a specific market)' with example slugs, and 'topic' for cross-event scanning with example seed questions. It also names an alternative tool: 'For custom sizing use polymarket_fill_risk.' This fully addresses when and how to use the tool versus 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

A3.5/5.0
Disambiguation2/5

Many tools are very similar (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and serve the same purpose with slight variations, making it hard for an agent to choose correctly. Additionally, the tool set mixes completely unrelated domains (ArcGIS geospatial vs. Pipeworx/Polymarket data), further confusing the purpose of each tool.

Naming Consistency2/5

Tool names follow multiple conventions: ask_pipeworx uses snake_case, while layer_info and query_layer use snake_case as well but with a different pattern. There is no consistent verb_noun pattern across the set; some are descriptive (validate_claim) while others are vague (process, run). The mix of conventions and lack of a unified naming scheme hurts predictability.

Tool Count1/5

At 34 tools, the count is excessive for a server supposedly focused on ArcGIS Peoria. Only 3 tools (layer_info, query_layer, search_datasets) are actually related to geospatial data, while the other 31 are from external services (Pipeworx, Polymarket). This mismatch suggests the server is extremely poorly scoped.

Completeness1/5

For a geospatial server, the tool set is severely incomplete. It lacks basic GIS operations like spatial filtering, editing, or analysis. The three geospatial tools only provide schema discovery and simple attribute queries. Meanwhile, the bulk of the tools cover a completely different domain (data lookup, prediction markets), leaving the core domain almost entirely unaddressed.

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