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Conspiracy Theory

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

A5/5.0
Behavior5/5

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

Annotations indicate readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. The description adds extensive behavioral context: caching ('Cached 1h at the KV level keyed on all knobs'), response structure (by_segment, diagnostics, fed_candidates), and caveats like Fed bets being unreliable. No contradictions 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.

Conciseness5/5

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

The description is dense but well-structured: starts with high-level purpose, details model families, explains response fields, then knobs and response top-level. Every sentence adds unique value—no fluff. It is front-loaded with the most important information for an agent to decide whether to call this tool.

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 (9 parameters, none required, no output schema), the description covers all necessary aspects: what the tool returns (by_segment, diagnostics), how edges are computed, tradeable-edge filters, and why Fed bets are excluded. The agent can fully understand the tool's behavior and output without needing additional documentation.

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?

Schema coverage is 100%, but the description adds actionable guidance for each parameter. For example, min_liquidity explains 'Set to 5000 to drop thin-book opportunities where executing the edge would walk the book past breakeven.' slippage_pp provides context on Polymarket fee structure and bid/ask. min_partition_leg_kelly clarifies its role for partitions where parent-level Kelly is zero.

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 tool's purpose: 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price.' It distinguishes itself from siblings like polymarket_arbitrage by focusing on data-driven edge detection across three model families, and from polymarket_edge_tracker by being a scan rather than a tracker.

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

Usage Guidelines5/5

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

The description explicitly says 'Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets.' It provides clear context for when to use this tool (finding tradeable edges) and lists customizable knobs (min_liquidity, max_spread_pp, etc.) and their effects, helping agents decide when to tweak parameters.

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

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve route/discover/research data needs, with ask_pipeworx_beta explicitly noted as currently identical to ask_pipeworx. Polymarket tools also blur together (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk), and ai_visibility_check vs scan_competitor_ai_presence are near-duplicates. An agent would frequently need to read long descriptions just to pick between near-equivalent entry points.

Naming Consistency3/5

The set is mostly snake_case and generally readable, with clear verbs like list_subscriptions, resolve_entity, generate_llms_txt, and validate_claim. However, conventions are mixed: brand-prefixed nouns appear (pipeworx_feedback, pipeworx_trending), one tool reverses the pattern (conspiracy_theory_generate vs generate_llms_txt), and the ask_pipeworx family follows its own scheme. The inconsistency is noticeable but does not make the names unreadable.

Tool Count2/5

32 tools is heavy, and the apparent scope is a scattered mix of general data research, prediction markets, memory, subscriptions, AI visibility, npm dependency checks, and conspiracy-theory generation. Many tools are meta-routers or aggregators that could be consolidated (e.g., the ask_pipeworx family, the polymarket family, the entity-research tools). The count feels like a growing internal toolkit rather than a deliberately scoped server.

Completeness2/5

The tools cover some complete sub-domains — memory has remember/recall/forget, subscriptions have subscribe/unsubscribe/list/recent_alerts, and data lookup has multiple verification and research paths. But the server's nominal 'Conspiracy Theory' purpose is essentially one generation tool with no save, share, history, or validation workflow, while the bulk of the surface is unrelated general-purpose data tooling. The overall offering is broad but not coherently complete for any clear stated purpose.

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