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Pride

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

A4.6/5.0
Behavior5/5

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

Even with annotations declaring read-only, idempotent, and non-destructive behavior, the description goes far beyond by disclosing caching ('Cached 1h at the KV level keyed on all knobs'), the 24h-move warning, the deliberate kelly_fraction_half=0 for partition_overround parents, placeholder-slug filtering, and diagnostic output for empty segments. These details give callers critical insight into runtime behavior.

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: it front-loads the purpose, then uses clear section headers (model families, knobs, response top-level). Every sentence contributes useful information, and the density is justified by the tool's complexity. It could be tightened slightly, but it avoids redundancy and is organized logically.

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 return values: by_segment structure, fed_candidates/fed_note, _diagnostics counters, per-opportunity fields (edge_pp_net, kelly_fraction, liquidity, spread, volume, 24h warning). It also explains why segments may be empty and how knobs affect filtering. For a tool with 9 parameters and three model families, this is 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, but the description adds meaning: it groups min_liquidity and max_spread_pp as 'TRADEABLE-EDGE KNOBS', explains how min_partition_leg_kelly interacts with partition arbs (where parent kelly is always 0), and clarifies that slippage_pp affects edge_pp_net and Kelly sizing. This enhances parameter understanding beyond the raw schema.

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 ('Scan') and resource ('top Polymarket markets'), and clearly states the unique value: returning opportunities where Pipeworx data disagrees with market price. It distinguishes itself by explaining the three model-family segmentation (MODEL_DRIVEN, STRUCTURAL_ARBITRAGE, CONCENTRATED_LONGSHOT) and the 'what should I bet on today' use case, which differentiates it from sibling tools.

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?

It explicitly states the intended use case ('agents discover opportunities without paging hundreds of markets') and includes operational guidance such as tradeable-edge knobs, the Fed candidates exclusion, and the rarity of concentrated longshots. However, it does not name alternative sibling tools or provide explicit when-not-to-use directions, so it stops short of full discrimination.

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
Disambiguation2/5

Several tools are near-identical in purpose: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route the same style of question, and the company-research tools (entity_profile, compare_entities, recent_changes) and Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research) have overlapping triggers. The long descriptions help, but an agent could easily select the wrong one.

Naming Consistency3/5

All names are snake_case and mostly readable, but conventions are mixed: some are verb-led (ask_pipeworx, resolve_entity, scan_dependency), some are noun-led (entity_profile, pipeworx_feedback, polymarket_arbitrage), and some are adjective-noun phrases (recent_changes, recent_alerts). The polymarket_* and ask_pipeworx_* families are internally consistent, but the overall set has no unifying pattern.

Tool Count2/5

33 tools is high and the server bundles several unrelated domains: Pipeworx data research, prediction markets, PRIDE proteomics, memory, subscriptions, and AI-visibility checks. Each cluster is individually useful, but the aggregate surface feels over-stuffed rather than well-scoped for a single MCP server.

Completeness4/5

The main clusters are well covered: query/grounded/deep research, entity resolution/profile/compare/validate, memory CRUD, subscription lifecycle, and a rich Polymarket analytics toolkit. Minor gaps exist—PRIDE is limited to metadata search/get and there is no trade execution for prediction markets—but most workflows can be completed without dead ends.

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