Skip to main content
Glama

Arcgis Peoria

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.

TDQS

A4.7/5.0
Behavior5/5

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

The annotations already mark it read-only/idempotent, but the description adds significant behavioral detail: 1h KV caching keyed on all knobs, response diagnostics with funnel counters, model-specific mechanics (e.g., partition overround with per-sport α, placeholder-slug filters), and a 24h-move warning that the edge may already be priced in. This goes well beyond what annotations provide.

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 front-loaded with purpose and organized into capital-letter sections (MODEL_DRIVEN, STRUCTURAL_ARBITRAGE, CONCENTRATED_LONGSHOT, RESPONSE TOP-LEVEL). Every sentence carries technical specificity with no filler or redundancy. Given the tool's complexity (9 params, multi-segment response, diagnostics), the length is appropriate and well-structured.

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 fully explains the response structure: top-level fields (by_segment, fed_candidates/fed_note, _diagnostics) and per-opportunity fields (edge_pp_net, kelly_fraction, liquidity, spread_pp, volume). It also documents caching, filtering logic, and caveats (e.g., rare-by-design for longshot, Fed signal unreliability), making it fully actionable for an agent.

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?

All 9 parameters have schema descriptions (100% coverage), so the baseline is 3. The description adds practical rationale for the tradeable-edge knobs (min_liquidity, max_spread_pp, min_partition_leg_kelly), explains slippage assumptions, and clarifies why min_kelly does not apply to partition arbs. This exceeds the schema, though not every parameter (limit, window) is addressed outside 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 clearly states the specific verb+resource+scope: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It enumerates the three model families and distinguishes itself as an opportunity-discovery tool, separating it from siblings like polymarket_arbitrage or polymarket_edge_tracker.

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 phrase 'Built for "what should I bet on today"' provides an explicit use case, and 'without paging hundreds of markets' indicates it's for aggregate discovery. It also cautions against Fed bets at meeting-month horizons, which is an exclusion. However, it does not explicitly name alternative tools or conditions for switching, stopping short of full guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Arcgis Peoria toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources