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ukpolice

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. Changed2 schema fields changed
    • addedInput schema / properties / max_spread_pp
      Added value: +{
+  "description": "Tradeable-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.",
+  "type": "number"
+}
    • addedInput schema / properties / min_liquidity
      Added value: +{
+  "description": "Tradeable-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.",
+  "type": "number"
+}
  2. Changed1 schema field changed
    • addedInput schema / properties / min_partition_leg_kelly
      Added value: +{
+  "description": "Minimum 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.",
+  "type": "number"
+}
  3. Changed4 schema fields changed
    • addedInput schema / properties / category_filter
      Added value: +{
+  "description": "Comma-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.",
+  "type": "string"
+}
    • changedInput schema / properties / min_edge_pp / description
      Previous value: -"Minimum |edge| in percentage points to include (default 0.5)."New value: +"Minimum |edge| in percentage points to include (default 0.5). Edge is evaluated NET of slippage."
    • addedInput schema / properties / min_kelly
      Added value: +{
+  "description": "Minimum 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.",
+  "type": "number"
+}
    • addedInput schema / properties / slippage_pp
      Added value: +{
+  "description": "Assumed 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.",
+  "type": "number"
+}
  4. Added

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses a wealth of behavioral detail: three response segments with model definitions, edge_pp_net net of slippage, Kelly caps at 0.25, a 24h-move warning, exclusion of Fed bets due to unreliable signal, diagnostics funnel counters for empty segments, and 1-hour KV caching keyed on all knobs. This far exceeds what annotations provide and contains no contradictions.

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 information-dense, with purpose front-loaded and clear structural labels (MODEL_DRIVEN, STRUCTURAL_ARBITRAGE, CONCENTRATED_LONGSHOT, TRADEABLE-EDGE KNOBS, RESPONSE TOP-LEVEL). Every sentence contributes value, and the structured formatting aids scanning. It could be slightly tightened but is not wasteful.

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 carries the full burden of explaining return values, and it does so thoroughly: top-level by_segment structure, per-item fields (edge_pp_net, kelly_fraction, liquidity, spread_pp, volume), fed_candidates/fed_note, and _diagnostics with funnel counters and filter_skips to explain empty segments. It also covers caching semantics and constraints, 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?

The schema already covers all 9 parameters with 100% description coverage, so the baseline is 3. The tool description adds meaningful semantic nuance beyond the schema, particularly for min_partition_leg_kelly (explaining why partition_overround returns kelly_fraction_half=0 at parent level, so min_kelly doesn't apply) and for tradeable-edge filters (min_liquidity, max_spread_pp) that drop opportunities 'where edge isn't realizable.' This extra context raises the score above baseline.

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 action and scope: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It clearly distinguishes from sibling tools like polymarket_arbitrage by framing this as a broad opportunity discovery tool for 'what should I bet on today' rather than a narrow arbitrage finder.

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 explicitly states the intended use: 'Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets.' It also describes tradeable-edge knobs (min_liquidity, max_spread_pp) that indicate when to filter results. However, it never explicitly names alternative tools or states when not to use this one, so it falls short of a full 5.

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

A3.6/5.0
Disambiguation2/5

Several clusters of tools overlap heavily: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research all answer questions, and the five polymarket_* tools require careful reading to distinguish. The three UK police tools are clear, but they sit among many near-duplicate data-query and memory utilities.

Naming Consistency2/5

Mixed conventions: snake_case verb_noun (get_crimes) coexists with brand-style names (ask_pipeworx), noun phrases (polymarket_arbitrage), and bare verbs (forget, recall). The polymarket_* and ask_pipeworx_* families are internally consistent, but the overall set lacks a single pattern.

Tool Count2/5

34 tools is heavy, and only three relate to the server's stated ukpolice domain, while the rest form a general-purpose data platform. Many meta-tools (suggest_questions, discover_tools, pipeworx_feedback, pipeworx_trending, memory) add bulk relative to the core purpose.

Completeness3/5

For the actual Pipeworx scope, coverage is strong: query, research, comparisons, subscriptions, memory, and feedback are all present. But for the ukpolice name, it is missing many UK police endpoints (neighborhoods, stop-and-search, etc.) and has no write/update operations, so the surface feels mismatched.

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