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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. Changed7 schema fields changed
    • addedInput schema / properties / category_filter
      Added value: +{
+  "description": "Comma-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.",
+  "type": "string"
+}
    • addedInput schema / properties / max_spread_pp
      Added value: +{
+  "description": "Tradeable-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.",
+  "type": "number"
+}
    • changedInput schema / properties / min_edge_pp / description
      Previous value: -"Minimum |edge| in percentage points to include (default 0.5)."New value: +"Minimum |edge| in percentage points to include (default 0.5). Edge is evaluated NET of slippage."
    • addedInput schema / properties / min_kelly
      Added value: +{
+  "description": "Minimum 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.",
+  "type": "number"
+}
    • addedInput schema / properties / min_liquidity
      Added value: +{
+  "description": "Tradeable-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.",
+  "type": "number"
+}
    • addedInput schema / properties / min_partition_leg_kelly
      Added value: +{
+  "description": "Minimum 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.",
+  "type": "number"
+}
    • addedInput schema / properties / slippage_pp
      Added value: +{
+  "description": "Assumed 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.",
+  "type": "number"
+}
  2. Added

TDQS

A4.6/5.0
Behavior5/5

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

The description adds extensive context beyond the annotations: it details the three model families (MODEL_DRIVEN, STRUCTURAL_ARBITRAGE, CONCENTRATED_LONGSHOT), the response structure (by_segment, fed_candidates, _diagnostics), the meaning of edge_pp_net, kelly_fraction, and even the 24h-move warning. It also explains why segments may be empty and caching behavior. This is far beyond what readOnlyHint conveys.

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 and dense, but each sentence carries specific information. It is front-loaded with the purpose and organized by categories (MODEL_DRIVEN, STRUCTURAL_ARBITRAGE, CONCENTRATED_LONGSHOT, knobs, response). While it uses all-caps and no line breaks, the structure is mostly logical. Some redundancy exists (e.g., 'TRADEABLE-EDGE KNOBS') but overall it earns its length for a complex tool.

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 fully specifies the response top-level structure, including by_segment, fed_candidates/fed_note, and _diagnostics. It also explains the fields on each opportunity (edge_pp_net, kelly_fraction, market.liquidity, etc.) and the conditions for empty segments. Given the complexity (9 parameters, multiple model families, filters), this description is remarkably complete.

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%, so the baseline is 3. The description adds semantic value by explaining the tradeable-edge knobs (min_liquidity, max_spread_pp, min_partition_leg_kelly) and how they drop opportunities. It also explains the logic behind slippage_pp and the interaction between min_kelly and min_partition_leg_kelly. It doesn't describe all parameters in the description, but the schema already covers them.

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: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' This clearly differentiates it from siblings like polymarket_arbitrage (focused on arbitrage) or polymarket_fill_risk (focused on execution risk). It also states the intended use case: 'what should I bet on today'.

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 clearly implies when to use: 'Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets.' It also includes exclusion notes (e.g., Fed bets excluded due to unreliability). However, it does not explicitly name alternative tools for different scenarios, so it lacks explicit when-not-to-use guidance.

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

A3.6/5.0
Disambiguation2/5

There is heavy overlap in the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, bet_research, validate_claim) — several are near-identical 'route a natural-language question to a source' tools differing only by small qualifiers. ai_visibility_check vs scan_competitor_ai_presence and entity_profile vs compare_entities vs recent_changes also blur together. An agent could easily misselect among these.

Naming Consistency4/5

The dominant convention is snake_case verb_noun/noun_verb (list_subscriptions, scan_dependency, validate_claim, resolve_entity) which is fairly consistent, but there are several bare single-word verbs (lookup, sequence, variation, vep, xrefs, recall, remember, forget) that break the pattern. No camelCase is present, so the inconsistency is minor rather than chaotic.

Tool Count2/5

38 tools is heavy, and the overwhelming majority (~31) are Pipeworx meta-tools (subscriptions, memory, feedback, trend, discovery, llms.txt generation) that have nothing to do with the server's declared Ensembl identity. Only about 7 tools are actually genomics-related, so the count is inflated by off-domain additions that dilute the surface.

Completeness2/5

For the Ensembl domain, the surface covers gene lookup, symbol resolution, sequence retrieval, orthologs, SNPs, variant effect prediction, and xrefs — but misses major Ensembl capabilities like gene trees/families, regulatory features, comparative/multi-species alignments, expression data, phenotypes, GO/ontology annotations, and region/overlap queries. Conversely the Pipeworx tools are complete for their own domain but irrelevant here, leaving the declared domain notably incomplete.

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