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Arcgis Lakecountyil

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?

Even with annotations (readOnlyHint, openWorldHint, idempotentHint), the description adds substantial behavioral context: threshold details (>3pp), placeholder filters (>20%), Jaccard similarity (≥0.30), and the fill check explanation with realizable_edge_pp ≤ 0 meaning 'do not trade it.' It also describes exactly what the response contains. No contradiction 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?

The description is long but well-structured with labeled sections (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK) and front-loaded purpose. It contains many specific numbers and examples, all relevant. It could be slightly tightened, but no sentence feels wasted.

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 two modes and many behavioral details, the description is remarkably complete. It explains input modes, underlying checks, filters, response structure, and even the interpretation of the fill_check output. The absence of an output schema is compensated by the detailed response description.

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 covers both parameters (event and topic) with descriptions, but the tool description goes far beyond: it explains exactly when to use each, gives concrete examples ('fed-decision-may-2026'), and describes the behavioral differences (single-event vs cross-event). This adds deep meaning beyond the schema 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 specific verb+resource+method: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It clearly distinguishes itself from sibling tools by explaining its unique approach and modes. The distinction between event, topic, and no-arg trending_scan further clarifies its scope.

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 gives explicit when-to-use guidance: 'Call with NO args for a trending_scan... pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan.' It also names an alternative tool for related use: 'For custom sizing use polymarket_fill_risk.' This effectively tells the agent when to use this 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

A3.7/5.0
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical, while ask_pipeworx_grounded, deep_research, validate_claim, and entity_profile all perform data lookups with only subtle differences. discover_tools and suggest_questions also serve similar onboarding/exploration roles.

Naming Consistency4/5

Tool names are uniformly snake_case and generally follow a verb_noun or noun_phrase pattern (e.g., ask_pipeworx, query_layer, entity_profile, remember). There is a slight mix between verb-first and noun-first names but no chaotic conventions like camelCase or inconsistent verb tense.

Tool Count2/5

At 34 tools, the count is well above the typical 15-25 range for a coherent server. More critically, the server is named 'Arcgis Lakecountyil' but only 3 tools (search_datasets, layer_info, query_layer) actually relate to ArcGIS; the remaining 31 tools are a broad Pipeworx data platform, creating a severe scope mismatch that makes the count feel excessive and unfocused.

Completeness2/5

For the ArcGIS domain implied by the server name, the surface is barely complete: it offers search, schema inspection, and querying, but no create, update, delete, or management capabilities. Conversely, the Pipeworx side is relatively rich, but that doesn't match the server's stated purpose, leaving the overall set incomplete for its apparent intended use.

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