Skip to main content
Glama

Weatherapi

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

A4.5/5.0
Behavior5/5

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

Description extensively documents behavior: caching at KV level for 1h keyed on knobs, three model families with specific logic, diagnostic output for empty segments, and tradeable-edge filters. Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint, and description adds rich context without contradiction.

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?

Description is quite lengthy, covering extensive details in dense paragraphs. It is structured with segments, but could be more concise by condensing some technical explanations. While informative, it burdens the agent with reading time.

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?

Given the complexity with 9 parameters and no output schema, the description is highly complete. It explains top-level response structure, edge metrics, diagnostics, caching, and knobs in detail, leaving little ambiguity for an agent to select and 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?

Schema coverage is 100% with detailed parameter descriptions. Description adds extra context for several parameters (e.g., explains why min_kelly doesn't filter partitions for min_partition_leg_kelly, mentions zero trading fees but practical slippage for slippage_pp). Not all parameters gain additional meaning, but the added value justifies above baseline 3.

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?

Description clearly states the tool scans Polymarket markets for opportunities where Pipeworx disagrees with market price, targeting 'what should I bet on today'. It distinguishes itself from siblings like polymarket_arbitrage by specifying the Pipeworx data source and unique model families.

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?

Provides explicit guidance on when to use ('what should I bet on today') and details on customizable knobs like min_liquidity and max_spread_pp. Lacks direct comparison to all siblings, but context signals list related tools, and description implies use for discovering opportunities rather than executing trades.

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

All Weatherapi 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
Disambiguation2/5

Several tools have near-identical purposes: ask_pipeworx and ask_pipeworx_beta are explicitly described as functionally identical, and six Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread, polymarket_edge_tracker) overlap heavily in discovery, edge, and arbitrage roles. Company-research tools (entity_profile, compare_entities, recent_changes) also blur boundaries, making misselection likely.

Naming Consistency3/5

All names use lowercase snake_case with underscores, which is a consistent base convention. However, the lexical pattern varies: bare single words (current, forecast, remember, forget) coexist with verb_noun compounds (resolve_entity, validate_claim) and noun compounds (entity_profile, polymarket_edges). The lack of a uniform verb_noun structure makes the set less predictable, though still readable.

Tool Count1/5

35 tools is well into the 'too many' range, and the server's name promises weather while only 4 of 35 tools (current, forecast, astronomy, marine) are weather-related — an extreme mismatch between the declared purpose and the actual surface. The remaining 31 tools form a general data/prediction-market platform that would be better served under a different server name.

Completeness4/5

Judged by its actual (non-weather) domain, the set is quite complete: generic routed lookup, grounded answer mode, deep research, entity resolution/profile/comparison, claim validation, subscriptions, memory, discovery, and feedback are all present. The weather subset covers current conditions, forecasts, marine, and astronomy, though it lacks historical weather and alert endpoints — a minor gap.

Resources