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Boi Il

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

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds caching behavior ('Cached 1h at the KV level keyed on all knobs') and details response structure including diagnostics. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is excessively long and dense, burying the core purpose under detailed explanations of model families and knob behavior. While well-structured in paragraphs, it violates conciseness by including excessive technical detail that could be streamlined or moved to a separate reference.

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 fully compensates by detailing response structure (by_segment, fed_candidates, _diagnostics), caching behavior, and tradeable-edge knobs. It provides everything an agent needs to understand how to interpret results and handle edge cases like empty segments.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the description's primary value is in clarifying parameter semantics beyond the schema. It adds context for slippage_pp (e.g., 'zero trading fees but 20-50bp per trade') and min_kelly (e.g., 'half-Kelly fraction'). However, most parameters are already well-described in the input schema, so the description adds marginal value.

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 tool scans Polymarket markets for discrepancies between Pipeworx data and market prices, explicitly targeting 'what should I bet on today' use case. It differentiates from siblings like polymarket_arbitrage by focusing on Pipeworx disagreement and segmenting opportunities into MODEL_DRIVEN, STRUCTURAL_ARBITRAGE, and CONCENTRATED_LONGSHOT.

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 explains the tool is designed for discovering betting opportunities without paging hundreds of markets, and provides tradeable-edge knobs and diagnostics for why segments are empty. However, it lacks explicit guidance on when not to use this tool versus alternatives like polymarket_arbitrage or polymarket_edge_tracker.

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

A4/5.0
Disambiguation3/5

Most tools have highly detailed descriptions that clarify their distinct roles, and the pipeworx/boi/polymarket families are individually distinguishable. However, ask_pipeworx_beta is explicitly described as currently identical to ask_pipeworx (a true duplicate), and the polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) share overlapping purpose and could cause misselection despite their lengthy docs.

Naming Consistency3/5

Many tools follow a clear verb_noun pattern (compare_entities, discover_tools, resolve_entity, validate_claim), but a large subset uses noun-first or prefixed compound names (ai_visibility_check, bet_research, boi_exchange_rate, polymarket_arbitrage). The naming is readable and group-consistent (boi_*, polymarket_*, ask_pipeworx*) but the overall convention is mixed rather than uniform.

Tool Count2/5

At 34 tools, the set exceeds the 25+ threshold for 'too many' and spans many unrelated domains (data lookup, prediction markets, memory, subscriptions, AI visibility, llms.txt generation, feedback). The broad scope explains the count, but many tools feel like add-on utilities rather than a tightly scoped server, making the surface feel bloated.

Completeness4/5

The core domain—authoritative structured data access—is extremely well covered: universal routing, grounded mode, deep research, entity profiles, comparisons, claim validation, resolution, discovery, and suggestions. Minor gaps exist (no explicit tool for fetching a pipeworx:// citation URI directly, no update operation for subscriptions), but agents can work around these via the router and existing subscription lifecycle tools.

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