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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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive. The description adds valuable behavioral context: caching (1h KV-level), edge calculation net of slippage, diagnostic output for empty segments, and 24h-move warnings. It also explains that fed_candidates are excluded from ranking. This goes beyond annotations without contradicting them, earning a high score.

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 comprehensive but verbose, spanning multiple paragraphs. It is front-loaded with the main purpose, which is good, but the detailed breakdown of model families and internal logic could be summarized for brevity. While organized, it is not maximally concise for its complexity level.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the absence of an output schema, the description provides extensive detail on response structure (by_segment, fed_candidates, _diagnostics) and per-opportunity fields (edge_pp_net, kelly_fraction, etc.). It also explains diagnostics for empty segments and knob interactions. This covers most contextual needs for an agent to invoke the tool correctly.

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), so baseline is 3. The description adds extra context beyond the schema, such as explaining the interaction between min_kelly and min_partition_leg_kelly, the meaning of tradeable-edge filters, and default values. This enhances understanding, justifying a score of 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: to scan Polymarket markets and return opportunities where Pipeworx data disagrees with market price. It uses specific verbs and resources ('scan top Polymarket markets', 'return opportunities'). While it distinguishes from the generic 'paging hundreds of markets' approach, it does not explicitly differentiate from sibling tools like 'polymarket_arbitrage' or 'polymarket_edge_tracker', which slightly reduces clarity.

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 provides guidance on when to use the tool ('what should I bet on today'), explains the three segments and their conditions (e.g., 'concentrated_longshot is rare-by-design'), and gives rationale for filtering (e.g., fed_candidates excluded due to data limitations). However, it lacks explicit contrasts with sibling tools or when NOT to use this tool, leaving some ambiguity about optimal selection among 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

A3.9/5.0
Disambiguation3/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route questions to similar data sources, while search_within also overlaps with grounded answering. bet_research, polymarket_edge_tracker, and polymarket_fill_risk all target prediction markets. Agents must read descriptions carefully to pick the right variant.

Naming Consistency4/5

Most tools follow a verb_noun snake_case pattern (ask_pipeworx, bet_research, validate_claim, resolve_entity). Some are single nouns (recent_alerts, recent_changes, key_alerts), a few break the convention (ask_pipeworx_beta, ask_pipeworx_grounded, remember, forget). Overall mostly consistent with minor deviations.

Tool Count2/5

34 tools is a high count for a general-purpose data server, and several seem redundant: ask_pipeworx vs ask_pipeworx_beta vs ask_pipeworx_grounded, polymarket_edge_tracker vs polymarket_arbitrage, and the numerous meta-tools create overhead. A focused dataset server would be better with 10–15 tools.

Completeness4/5

The surface covers many domains well: SEC filings, economics/FRED, prediction markets, news, clinical trials, San Francisco open data, npm dependencies. Obvious gaps include no financial statement form filings beyond 8-K/10-K, no calendar/event scheduling, and no update-else path for several key objects (but memory tools fill that gap). Attribution currently ships in almost all requested tools, providing evidence.

Resources