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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

A4.5/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses detailed behavioral traits: 1-hour KV caching keyed on knobs, diagnostics output for why segments may be empty, the 24h-move warning, and the explicit caveat that Fed signal is 'unreliable at meeting-month horizons without paid OIS/SOFR-futures data.' It also explains model-specific logic like placeholder-slug filtering and half-Kelly capping. 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.

Conciseness3/5

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

The description is front-loaded with a clear purpose, but the body is extremely dense—nested parentheses, all-caps sections, and exhaustive detail about model families, filters, and response diagnostics. While structured into sections, it reads more like a specification than a concise tool description. Every sentence has content, but overall length and complexity reduce scannability.

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 compensates thoroughly: it outlines the top-level response structure (by_segment, fed_candidates/fed_note, _diagnostics), details what each opportunity includes (edge_pp_net, kelly_fraction, liquidity, spread, volume), and explains diagnostics for empty segments. The high parameter count and complex logic are well covered, making the description sufficient for an agent to use the tool effectively.

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% with detailed descriptions for all 9 parameters, so baseline is 3. The tool description adds value by grouping parameters into 'TRADEABLE-EDGE KNOBS' and clarifying relationships (e.g., why min_kelly does not filter partition arbs, and how min_partition_leg_kelly applies instead). This conceptual framing goes beyond the schema while not duplicating every parameter's semantics.

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 verb+resource+objective: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It clearly positions the tool for discovery ('what should I bet on today') and differentiates from siblings by emphasizing Pipeworx data and aggregated segments. Minor internal count mismatch ('FIVE MODEL FAMILIES' vs four enumerated) does not obscure the core purpose.

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 states a clear use case: 'Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets.' It also provides filter guidance (e.g., 'Set to 2 to require tight books', 'Set to 5000 to drop thin-book opportunities') and explains when Fed bets are excluded. It does not explicitly name alternative tools, but the context is sufficient for an agent to judge appropriateness.

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

A3.8/5.0
Disambiguation3/5

Many tools have distinct purposes, but there is overlap in data querying tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile, etc.) and prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.). Detailed descriptions help differentiate them, but the number of similar-sounding tools increases the chance of misselection.

Naming Consistency3/5

Tool names show mixed conventions: some use verb_noun (e.g., find_user, list_subscriptions), others are noun_verb (e.g., entity_profile, bet_research), and there are prefixes like polymarket_ and pipeworx_. While subgroups are internally consistent, the overall set lacks a unified pattern.

Tool Count2/5

With 34 tools spanning speedrun.com queries, Pipeworx data access, memory management, subscriptions, and prediction markets, the server bundles multiple domains. The scope is too broad for a coherent single server; splitting into separate servers would improve usability.

Completeness4/5

Within each domain (speedrun.com, Pipeworx, Polymarket), the tool set covers key operations comprehensively, including research, arbitrage, fill risk, and monitoring. Minor gaps exist (e.g., no tool to place bets), but the overall surface is well-covered for the advertised functionalities.

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