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Arcgis Phoenix

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.

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint), the description adds substantial behavioral context: response segment structure, edge metrics (edge_pp_net, kelly_fraction), diagnostics funnel counters, the 24h-move warning, caching behavior ('Cached 1h at the KV level'), and the 'rare-by-design' nature of concentrated longshots. These details are not derivable from the schema or 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 and dense, but this is justified for a complex tool with 9 parameters and a rich response model. It is well-structured with clear sections for model families, tradeable-edge knobs, and response top-level, and it front-loads the primary purpose. Some repetition or over-detail (e.g., the exact model family formulas) could be trimmed, but overall every section earns its place.

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?

The tool has no output schema, so the description carries the full burden of explaining the response. It comprehensively describes the top-level by_segment structure, the _diagnostics block, and the key edge fields. It also covers caveats (e.g., Fed signal unreliability) and filtering rationale. Given the tool's complexity, this description is exceptionally complete.

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?

Although the input schema already has 100% description coverage, the tool description adds meaningful cross-parameter semantics. For example, it explains how min_liquidity and max_spread_pp are 'tradeable-edge filters' that drop opportunities, and it clarifies that min_kelly does not apply to partition_overround opportunities because 'basket trades don't compose to single-leg Kelly' — requiring the separate min_partition_leg_kelly knob. This goes far beyond the schema's individual parameter descriptions.

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 and resource: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It clarifies the intended use case ('what should I bet on today') and clearly distinguishes this from sibling tools like polymarket_arbitrage or polymarket_edge_tracker by focusing on cross-referencing Pipeworx data with market prices for discovery.

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 provides clear context for when to use this tool: 'agents discover opportunities without paging hundreds of markets.' It also includes guidance on knobs (e.g., 'Set to 2 to require tight books') and explicitly explains why certain segments are excluded (e.g., Fed bets 'excluded from ranking... unreliable'). However, it does not explicitly name alternative sibling tools or state when NOT to use this tool, which prevents a 5.

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

A3.7/5.0
Disambiguation3/5

Most tools have distinct purposes, but the ask/research family is crowded: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same underlying catalog and require careful description reading to select correctly. Prediction-market tools also overlap in scope, though each has a reasonably distinct angle.

Naming Consistency3/5

All names are snake_case and mostly readable, but conventions are mixed: some are verb_noun (query_layer, resolve_entity), some are noun_noun (entity_profile, pipeworx_feedback, polymarket_arbitrage), and some are adjective_noun or brand-prefixed phrases. No consistent verb/noun ordering pattern exists across the set.

Tool Count2/5

34 tools is too many for the apparent focus, especially since the server is named 'Arcgis Phoenix' but only 3 of the tools are actually GIS tools. The bulk is a sprawling Pipeworx data/prediction-market ecosystem plus unrelated utilities, making the set feel over-stuffed and unfocused.

Completeness3/5

The Pipeworx data side is fairly complete for lookups, entity profiles, comparisons, validation, and subscriptions, but the ArcGIS Phoenix portion is only search/schema/query and lacks any analysis, geocoding, or editing capability. The presence of generate_llms_txt and scan_dependency highlights that the overall domain is undefined and therefore hard to call complete.

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