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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

A4.4/5.0
Behavior5/5

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

The description far exceeds the annotations by detailing internal model logic (e.g., 'lognormal barrier from 90d FRED log-returns', 'partition_overround with favorite-longshot bias correction'), edge calculation, caching strategy (1h KV-level), and output structure including diagnostics for empty segments. No contradictions with annotations (readOnly, openWorld, idempotent, non-destructive).

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 well-structured, starting with the purpose, then dividing into model families, response fields, and knob explanations. Every sentence adds meaningful detail. However, it could be more concise by condensing some technical explanations. The front-loading of purpose is effective.

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 complexity (9 parameters, no output schema), the description thoroughly explains the response shape: by_segment with three segments, fed info, and diagnostics. It covers what each segment contains, edge metrics, and why certain edges are excluded. This compensates for the lack of output schema and ensures agents understand the full context.

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). The tool description adds value by grouping parameters into 'TRADEABLE-EDGE KNOBS' and explaining their practical effect (e.g., 'drop opportunities where edge isn't realizable'). It also clarifies parameter behavior (e.g., min_kelly applies to single-leg, min_partition_leg_kelly to per-leg). This contextual guidance 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 starts with a clear verb+resource combo: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It explicitly states the tool's purpose and its intended use case ('what should I bet on today'). The detailed breakdown of three model families and response segments further clarifies the specific outputs, distinguishing it from broader market tools.

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

Usage Guidelines3/5

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

The description implies usage context ('agents discover opportunities without paging hundreds of markets') and provides adjustment knobs, but it does not explicitly state when to use this tool versus alternatives like polymarket_arbitrage or polymarket_edge_tracker. No 'when-not-to-use' or explicit exclusions are provided, though it does note Fed bets are excluded and why.

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

A3.5/5.0
Disambiguation2/5

Multiple tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all handle query routing, and the five polymarket_* tools plus bet_research blur the line between market scanning, edge detection, and fill-risk analysis. Several pairs (entity_profile/compare_entities/recent_changes, ai_visibility_check/scan_competitor_ai_presence) also overlap substantially.

Naming Consistency2/5

The tool names mix multiple conventions: verb_noun (validate_gtin, list_subscriptions, generate_llms_txt), brand-prefixed groups (pipeworx_*, polymarket_*, ask_pipeworx*), and bare nouns (entity_profile, recent_alerts). The server is named after GTIN/barcodes, yet most tools are branded Pipeworx or Polymarket, making the set feel incoherently named.

Tool Count2/5

33 tools is well over the threshold where a typical agent can comfortably navigate the surface, especially since they span unrelated domains: barcode validation, data lookups, prediction-market arbitrage, memory storage, subscriptions, npm scanning, and llms.txt generation. The count reflects an overgrown grab bag rather than a well-scoped toolset.

Completeness2/5

There is no coherent domain to assess completeness against: for the server's apparent GTIN/barcode purpose, only gtin_check_digit and validate_gtin exist (and not even a lookup for product data by GTIN). For the broader Pipeworx platform hinted at by most tools, the surface is scattered, with deep coverage of prediction-market edges but arbitrary one-off utilities elsewhere.

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