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Arcgis Palmbeach

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds extensive behavioral detail: semantic anchor (Jaccard similarity ≥0.30), partition filter (placeholder slugs), fill check with realizable_edge_pp ≤0 meaning 'do not trade it', and reporting of skipped_low_similarity and placeholders_filtered. This far exceeds the minimal baseline and enriches the agent's understanding of edge cases.

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?

The description is long but every sentence serves a purpose. It is front-loaded with the core purpose, then logically structured into labeled sections (SEMANTIC ANCHOR, PARTITION FILTER, RESPONSE, FILL CHECK) that group related details. No fluff or redundancy; the density is justified by the tool's complexity.

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 covers return values: opportunities[] fields, partition_check breakdown, and fill check outputs. It also addresses failure modes (realizable_edge_pp ≤0, placeholder fractions) and actionable guidance ('do not trade it'). The agent has enough context to use the tool correctly and interpret results.

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%, so baseline is 3, but the description adds substantial value: it gives concrete examples for `event` ('fed-decision-may-2026' or a full URL) and `topic` ('Strait of Hormuz traffic returns to normal'), explains the behavioral difference between modes, and clarifies what each mode does with the parameter. This goes well beyond restating 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 clear verb-object-method: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It distinguishes from siblings by naming the specific detection techniques and enumerating three modes (trending_scan, event, topic), which immediately clarifies scope and uniqueness.

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 a trending_scan... pass event for the strongest per-event partition_check, or topic for a themed cross-event scan.' It recommends which mode to choose for a specific market, explains when cross-event mode is advantageous, and even references polymarket_fill_risk as an alternative for custom sizing.

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

B3.4/5.0
Disambiguation2/5

Multiple tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer data questions, while discover_tools and suggest_questions both serve discovery. The three ArcGIS tools are distinct but are drowned out by the unrelated Pipeworx and prediction-market tools, making it hard to pick the right one.

Naming Consistency3/5

Tool names are mostly snake_case but mix verb_noun (query_layer, search_datasets, remember), noun_noun (layer_info, entity_profile), and less conventional forms (search_within, generate_llms_txt). The naming is readable and not chaotic, but there is no single consistent pattern across the set.

Tool Count1/5

34 tools is excessive for a server named 'Arcgis Palmbeach'. Only 3 tools are GIS-related (search_datasets, query_layer, layer_info); the other 31 are unrelated Pipeworx data, prediction-market, and memory utilities. The count grossly mismatches the server's apparent purpose.

Completeness1/5

For the stated ArcGIS/Palm Beach County GIS purpose, the tool surface is severely incomplete: only search, query, and layer-schema lookup exist, with no data editing, feature operations, or map-service management. While the Pipeworx domain is heavily covered, that is not what the server name promises, so the surface is a poor fit.

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