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Uk Parliament

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

A4.1/5.0
Behavior5/5

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

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds rich context: caching for 1 hour keyed on knobs, detailed model family logic, slippage considerations, edge computation after slippage, 24h-move warnings, Fed bet exclusion, and diagnostic counters. 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 lengthy but well-structured with sections, lists, and formatting. It front-loads the overall purpose and then details each model family and response structure. Every sentence adds necessary information given the tool's complexity; there is no redundancy, making it concise for the amount of detail required.

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?

Despite no output schema, the description fully explains the response structure: by_segment, fed_candidates, _diagnostics, and what each segment contains. It covers edge metrics, tradeable-edge knobs, caching behavior, and diagnostic counters. All parameter descriptions in the schema are complete. The combination fully prepares an agent to use 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?

The input schema has 100% description coverage, providing thorough explanations for each parameter. The description adds extra context for several parameters (e.g., min_liquidity, max_spread_pp, min_partition_leg_kelly) by explaining their role in filtering tradeable edges. This goes beyond schema descriptions, justifying a score above baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool scans Polymarket markets for edge opportunities using Pipeworx data, specifying the verb 'scan' and resource 'Polymarket markets'. It details the three model families and output segmentation. However, it does not explicitly differentiate from sibling tools like polymarket_arbitrage or polymarket_edge_tracker, which reduces clarity for selection.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use for 'what should I bet on today' and mentions knobs for filtering, providing implicit usage context. However, it does not explicitly state when to use this tool versus alternatives, nor does it provide conditions when not to use it or when to prefer a sibling tool. Guidance is present but not explicit.

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

B3.4/5.0
Disambiguation2/5

Several tools are near-duplicates (ask_pipeworx and ask_pipeworx_beta are currently identical; ai_visibility_check vs scan_competitor_ai_presence overlap), and the massive mix of unrelated domains (Polymarket betting, general data lookup, AI visibility) alongside UK Parliament tools makes selection confusing. An agent would struggle to know whether to use ask_pipeworx, ask_pipeworx_beta, or ask_pipeworx_grounded, or which of the five polymarket tools fits.

Naming Consistency3/5

Most tools use snake_case with readable names, but the action placement varies (verb_noun like get_bill vs noun_verb like bet_research, entity_profile), and there are compound names like generate_llms_txt and scan_competitor_ai_presence. The style is mostly consistent but the verb_noun pattern is not uniform across the set.

Tool Count2/5

38 tools is heavy, and the overwhelming majority are unrelated to the server's stated UK Parliament purpose. Only 7 tools (get_bill, search_bills, bill_stages, get_member, search_members, search_hansard, recent_divisions) have anything to do with Parliament, making the count wildly inappropriate for the apparent scope.

Completeness2/5

The Parliament-specific surface is thin: basic bill/member/Hansard lookups exist, but there are no tools for specific divisions/votes, committees, publications, or detailed procedural information. The vast non-Parliament tooling is irrelevant, creating a dead end for any real Parliament research beyond the basics.

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