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Arcgis Dukescounty

by io.github.pipeworx-io

Polymarket Edges

polymarket_edges
Read-onlyIdempotent

Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price. Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets. FIVE MODEL FAMILIES grouped into three response segments under by_segment: (1) MODEL_DRIVEN — crypto_price (lognormal barrier from 90d FRED log-returns) and news_momentum (GDELT 7d/21d article-volume ratio, soft signal w/ halved Kelly). (2) STRUCTURAL_ARBITRAGE — partition_overround on mutually-exclusive events; per-leg favorite-longshot bias correction with per-sport α (tennis 1.02, soccer 1.10, MMA 1.15, default 1.0); placeholder-slug filter drops will-person-X / will-team-Y / will-manager-Z / will-someone-else- backstops; partitions with >20% placeholder fraction skipped entirely. (3) CONCENTRATED_LONGSHOT — basket trade when one leg ≥75% AND ≥2 longshots ≤8% AND portfolio return ≥25:1; rare-by-design (gates relaxed Run 8 from prior 85%/5%/50:1). EVERY OPPORTUNITY carries edge_pp_net (after slippage), kelly_fraction + kelly_fraction_half (capped at 0.25), market.liquidity, market.spread_pp, market.volume, plus a 24h-move warning ("Market moved X.Xpp in 24h") when the recent move alone exceeds the edge — your edge may already be in the price. TRADEABLE-EDGE KNOBS: min_liquidity / max_spread_pp drop opportunities where edge isn't realizable; min_partition_leg_kelly filters partitions by best per-leg Kelly. RESPONSE TOP-LEVEL: by_segment{model_driven,structural_arbitrage,concentrated_longshot}, fed_candidates/fed_note (Fed bets surface here, excluded from ranking — 1m-T vs EFFR signal is unreliable at meeting-month horizons without paid OIS/SOFR-futures data), and _diagnostics{concentrated_longshot:{...funnel counters},category_counts,filter_skips} so callers can see WHY a segment is empty (top-N stale, all candidates failed gates, knob dropped them). Cached 1h at the KV level keyed on all knobs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoTop N edges to return after ranking. Default 10, max 25.
windowNoPolymarket volume window to filter markets. Default 1wk.
min_kellyNoMinimum half-Kelly fraction (as decimal, e.g. 0.005 = 0.5% of bankroll) to include single-leg opportunities. Default 0 (no filter). Skips opportunities that are too small to bet sensibly even if the edge is large.
min_edge_ppNoMinimum |edge| in percentage points to include (default 0.5). Edge is evaluated NET of slippage.
slippage_ppNoAssumed execution slippage in percentage points per leg (default 0.3). Subtracted from raw |edge| before ranking and Kelly sizing. Polymarket has zero trading fees as of 2024 but bid/ask + thin depth typically eats 20-50bp per trade. Bump for very thin partitions; drop to 0 if you have a smarter fill model.
max_spread_ppNoTradeable-edge filter. Maximum bid/ask spread in percentage points on the representative market. Default null (no filter). Set to 2 to require tight books — anything wider eats most plausible edges.
min_liquidityNoTradeable-edge filter. Minimum $ liquidity on the representative market (or for partition_overround, on at least one top_leg). Default 0 (no filter). Set to 5000 to drop thin-book opportunities where executing the edge would walk the book past breakeven.
category_filterNoComma-separated list to restrict the output: "model_driven" (crypto_price + news_momentum), "structural_arbitrage" (partition_overround), "concentrated_longshot". Combine like "model_driven,structural_arbitrage". Default: all.
min_partition_leg_kellyNoMinimum BEST per-leg half-Kelly fraction across a partition_overround opportunity's top_legs (or longshot_basket legs). Default 0 (no filter). Partition arbs always return kelly_fraction_half=0 at the parent level by design (basket trades don't compose to single-leg Kelly), so min_kelly never filters them — this knob applies to the per-leg Kelly inside top_legs instead. Use to suppress thin partitions whose individual leg edges aren't worth the per-leg slippage cost.

Schema Changelog

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

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. Beyond that, the description adds valuable behavioral context: caching at KV level for 1 hour keyed on knobs, 'rare-by-design' for concentrated longshots, a 24h-move warning that the edge may already be priced, and _diagnostics to explain why segments are empty. It also discloses the unreliability of the Fed signal without paid data.

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 dense but well-structured with capitalized segment labels and parenthetical details. It is front-loaded with purpose and then methodically covers models, response shape, knobs, and diagnostics. However, it is quite long (300+ words) and could overwhelm some agents; a slightly more condensed structure would improve scannability.

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 thoroughly: top-level by_segment structure, per-opportunity fields (edge_pp_net, kelly_fraction, market.liquidity, etc.), diagnostics funnel counters, and even the fed_candidates/fed_note. It also covers caching, model details, and gate relaxations, making it complete for a complex tool.

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 coverage is 100%, so parameters are documented well. The description goes further: for slippage_pp it gives concrete market context (zero fees but 20-50bp hits) and guidance to adjust; it explains why min_kelly doesn't filter partitions and introduces min_partition_leg_kelly; and it clarifies 'NET of slippage' for edge. These are practical semantics beyond 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 specific verb and resource: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' This clearly distinguishes it from siblings like polymarket_arbitrage (cross-market arbitrage) and polymarket_edge_tracker (edge monitoring), and the added 'what should I bet on today' frames the intent sharply.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context ('Built for ... discover opportunities without paging hundreds of markets') and detailed knob guidance (e.g., min_liquidity, max_spread_pp, min_partition_leg_kelly). However, it does not explicitly state when to prefer this over specific siblings or when not to use it, so it lacks exclusions or named 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

ask_pipeworx and ask_pipeworx_beta are explicitly identical, and the prediction-market cluster (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) has heavily overlapping purposes that require reading long descriptions to separate. The server name promises ArcGIS Dukes County but most tools are unrelated, making the overall selection space confusing.

Naming Consistency3/5

Most names are snake_case, but conventions vary: some are verb_noun (query_layer, search_datasets, list_subscriptions), some noun-ish (entity_profile, layer_info, pipeworx_trending), some bare verbs (remember, recall, forget, subscribe), and some long compounds (polymarket_edge_tracker, scan_competitor_ai_presence). Readable overall, but no strong consistent pattern.

Tool Count2/5

34 tools is heavy, and only three (search_datasets, layer_info, query_layer) relate to the server's apparent ArcGIS Dukes County purpose. The rest form a sprawling Pipeworx/prediction-market/memory/utility toolkit, creating a severe scope mismatch with the server's name.

Completeness2/5

For the named Dukes County GIS domain, the surface is minimal—dataset discovery, schema, and queries—with no spatial operations or editing, though read-only access may be acceptable. For the broader Pipeworx functionality it is fairly comprehensive, but that is not what the server name advertises, leaving the set incomplete relative to its apparent identity.

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