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Legislation Uk

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?

The description discloses caching behavior ('Cached 1h at the KV level keyed on all knobs'), response structure (by_segment, diagnostics, fed_candidates), and important nuances (Fed bets excluded due to unreliable signal; every opportunity carries specific fields). Annotations already declare readOnly=true, and description adds rich behavioral 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but well-structured with clear sections (MODEL_DRIVEN, STRUCTURAL_ARBITRAGE, CONCENTRATED_LONGSHOT) and front-loaded with purpose. While verbose, every part adds unique value for the complex tool, making it acceptable.

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 documents the response structure and field meanings. It explains all parameters, caching, and edge cases (Fed bets, diagnostics). The tool is complex, but the description covers all necessary context for correct invocation.

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?

With 100% schema coverage, baseline is 3, but the description adds substantial meaning beyond the schema. It explains defaults, usage, and rationale for each parameter (e.g., slippage_pp discusses Polymarket fees; min_partition_leg_kelly explains why it exists). This elevates the score.

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 clearly states the action ('Scan top Polymarket markets'), the resource ('Polymarket markets'), and the outcome ('return opportunities where Pipeworx data disagrees with market price'). It explicitly states the intended use case ('what should I bet on today') and distinguishes from siblings by mentioning specific segments and knobs.

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 the tool ('discover opportunities without paging hundreds of markets') and explains tradeable-edge knobs. However, it does not explicitly mention when not to use it or compare with sibling tools like 'polymarket_arbitrage' or 'polymarket_edge_tracker'.

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

A3.8/5.0
Disambiguation2/5

The legislation-specific tools are distinct, but the server bundles dozens of unrelated Pipeworx/prediction-market/AI-visibility tools, making the set's purpose unclear. Several near-identical pairs exist: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and ai_visibility_check overlaps heavily with scan_competitor_ai_presence. An agent would struggle to know which tool is the right entry point.

Naming Consistency2/5

Naming is mixed: snake_case dominates, but camelCase appears in ask_pipeworx, ask_pipeworx_grounded, generate_llms_txt, and pipeworx_feedback. There is also inconsistency in verb style — get_/search_/list_ coexist with bare verbs like remember, recall, forget, and subscribe. The legislation tools themselves follow a clean get_legislation* pattern, but the wider set does not.

Tool Count2/5

35 tools is too many for a server named 'Legislation Uk' where only 4 tools actually relate to UK legislation. The remaining 31 tools appear to belong to a broader data/prediction-market platform, which suggests severe scope creep or a mislabeled assembly. This bloats the surface area and makes the server harder for an agent to navigate.

Completeness4/5

For the stated UK-legislation purpose, the core read-and-search workflow is covered: search_legislation, get_legislation, get_legislation_section, and get_legislation_text together support discovery, metadata, targeted section lookup, and full-text retrieval with version selection. Obvious gaps remain, such as full-text content search and amendment/change history, but agents can complete the primary task of finding and reading legislation. The unrelated tools neither help nor complete this domain.

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