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Glama

Arcgis Palmbeach

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?

With annotations already declaring read-only/idempotent/non-destructive, the description adds rich behavioral context: the three response segments, model families (crypto_price lognormal barrier, news_momentum GDELT ratio), edge calculation net of slippage, Kelly caps at 0.25, 24h-move warning, tradeable-edge knobs, caching behavior (1h KV level), and diagnostics for empty segments. This far exceeds annotation coverage without contradiction.

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 front-loaded with a clear purpose and organized into identifiable sections (TRADEABLE-EDGE KNOBS, RESPONSE TOP-LEVEL). However, it is extremely dense and includes long parenthetical asides (e.g., per-sport alpha values, gate relaxation history from prior Run 8) that could be trimmed for readability. Every sentence adds value, but the length and complexity reduce conciseness.

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?

This tool has no output schema, so the description carries full responsibility for explaining the return shape. It thoroughly describes top-level fields (by_segment, fed_candidates/fed_note, _diagnostics), why segments might be empty, caching, and the meaning of tradeable-edge knobs. For a complex tool with 9 optional parameters, the description is highly complete.

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 baseline is 3. The description adds meaningful parameter semantics by explaining the 'TRADEABLE-EDGE KNOBS' (min_liquidity, max_spread_pp) and the interaction between min_kelly and min_partition_leg_kelly (partition arbs return parent kelly=0, so min_kelly never filters them). It also clarifies category_filter through the explanation of the three response segments. While not every parameter is individually discussed, this additional context goes 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 clear, specific action: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' This distinguishes the tool from siblings like polymarket_arbitrage or polymarket_edge_tracker by focusing on discovery ('what should I bet on today') rather than arbitrage or tracking.

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 clearly states the intended use case ('what should I bet on today') and that it avoids paging hundreds of markets, which tells agents when to reach for this tool. However, it does not explicitly name alternative tools or provide when-not-to-use conditions, aside from noting Fed bets are excluded from ranking. This is clear context but lacks direct exclusions or 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

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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