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Arcgis Johnsoncity

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 already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description goes far beyond this by disclosing internal logic such as monotonicity/partition-sum checks, Jaccard similarity threshold (≥0.30), placeholder filtering (>20% returns null), and fill-check behavior (realizable_edge_pp ≤0 means 'do not trade it'). It also describes response fields and edge cases, adding rich context without contradicting the 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?

The description is lengthy but each sentence contributes necessary detail for a complex tool. It is front-loaded with purpose and invocation modes, but could be more scannable with structured lists or bullet points. Still, no sentence feels wasted, so it earns a high score.

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?

With no output schema, the description must explain return values, and it does: opportunities[] with fields, partition_check structure, and fill_check details. It also covers filtering logic, skipped_low_similarity, and references a sibling tool for custom sizing. All invocation modes and edge cases are addressed, making it highly complete.

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 coverage is 100% for both parameters, but the description adds substantial meaning: it defines event slugs with concrete examples ('fed-decision-may-2026'), topic seed questions ('Strait of Hormuz traffic returns to normal'), and behavior of each mode. This goes far beyond the schema's basic descriptions, helping the agent choose and format parameters correctly.

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 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks', using a specific verb and resource. It clearly distinguishes this from sibling tools by detailing event and topic modes and referencing polymarket_fill_risk for custom sizing, making its unique purpose obvious.

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 explains three invocation modes: no args for trending_scan, event for single-event checks, and topic for cross-event scans. It contrasts single-event vs cross-event capabilities and points to polymarket_fill_risk for custom sizing, providing clear when-to-use and alternatives 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

A3.7/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route questions to similar data sources, and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The many polymarket tools also blur together despite detailed descriptions.

Naming Consistency3/5

Most names are snake_case, but there is no consistent verb_noun pattern: ask_pipeworx, bet_research, entity_profile, layer_info, pipeworx_trending, recent_changes, and validate_claim follow different stylistic conventions. The pattern is readable but feels like several naming vocabularies were merged.

Tool Count2/5

34 tools is well over the comfortable range and most of them are unrelated to the apparent ArcGIS Johnson City purpose. Only search_datasets, layer_info, and query_layer actually serve GIS needs; the remaining 31 tools form a sprawling Pipeworx meta-platform bolted onto the same server.

Completeness3/5

The ArcGIS portion covers discover-schema-query reasonably well for read-only open data, and the Pipeworx side has broad coverage with subscriptions, memory, feedback, and research workflows. However, the surface is defined by two unrelated domains, making it hard to judge true completeness for any one stated purpose.

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