Skip to main content
Glama

Datos Ar

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

A4.7/5.0
Behavior5/5

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

Annotations already indicate readonly and idempotent, but the description adds extensive behavioral details: caching policy (cached 1h at KV level), segment definitions, filtering knobs (min_liquidity, max_spread_pp), diagnostics for empty segments, and per-opportunity fields. No contradiction 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.

Conciseness4/5

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

The description is long but well-structured with clear segmentation (MODEL_DRIVEN, STRUCTURAL_ARBITRAGE, etc.), bullet-like formatting, and acronym explanations (e.g., GDELT, OIS/SOFR). Some redundancy (e.g., repeated mention of Fed bets) but overall effective for an agent.

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?

No output schema, so description fully covers return structure: top-level fields (by_segment, fed_candidates, _diagnostics), per-opportunity fields (edge_pp_net, kelly_fraction, market.liquidity, etc.), and knob effects. Also explains diagnostics for empty segments, ensuring users understand why results are empty.

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

Parameters5/5

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

Schema coverage is 100% (baseline 3), but the description adds rich context well beyond schema descriptions, e.g., slippage_pp explains Polymarket fee structure and typical bid/ask, min_partition_leg_kelly clarifies why min_kelly doesn't apply to partitions, and knobs like min_liquidity are explained in terms of execution impact. This significantly aids correct parameter selection.

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?

Clearly states the tool scans Polymarket markets for opportunities where Pipeworx data disagrees with market price. It uses specific verbs like 'scan' and 'return opportunities', and distinguishes from siblings like polymarket_arbitrage and polymarket_edge_tracker by focusing on edge detection across model-driven, structural arbitrage, and concentrated longshot segments.

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?

Frames the tool as 'what should I bet on today' and notes agents discover opportunities without paging hundreds of markets. Mentions Fed bets exclusion and knob usage but lacks explicit when-not-to-use comparisons with siblings. The context is clear enough for an agent to decide when to invoke it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Datos Ar toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation3/5

Several tools cluster around the same underlying data router (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and the prediction-market family has five overlapping members, so misselection is possible. The descriptions are detailed enough to separate most intents, but ask_pipeworx_beta is explicitly identical to ask_pipeworx right now and ai_visibility_check/scan_competitor_ai_presence are close cousins.

Naming Consistency4/5

All tool names consistently use lowercase snake_case, and most follow a clear verb_noun shape like ask_pipeworx, get_series, subscribe, or validate_claim. A few noun-style names (entity_profile, pipeworx_trending, polymarket_edges) and the bare memory verbs (remember, recall, forget) break the pattern slightly, but the overall convention is predictable.

Tool Count2/5

33 tools is a heavy surface that exceeds the 25-tool threshold where selection cost becomes a real problem for agents. The count is inflated by auxiliary concerns like memory, subscriptions, feedback, trending, and AI-presence scans that sit alongside the core data-access mission.

Completeness4/5

The core data-research workflows are well covered: discovery (discover_tools, suggest_questions), retrieval (ask_pipeworx, deep_research, get_series), entity resolution (resolve_entity), profiles, comparisons, claim validation, and prediction-market analysis all have end-to-end support. Memory and subscription lifecycles are also complete. Minor gaps exist — some sources soft-fail and there is little Argentina-specific tooling beyond the time-series pair — but agents can generally work around them.

Resources