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Bet Research

bet_research
Read-onlyIdempotent

Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-bitcoin-hit-150k-by-june-30-2026"), a polymarket.com URL, or a question text. The tool resolves the market, classifies the bet, fans out to category-specific data packs in parallel, and returns an evidence packet + simple market-vs-model comparison. Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z". CLASSIFIERS: crypto_price, fed_rate, geopolitical, sports, sports_championship, drug_approval, election_candidate, tech_launch, space_launch, corporate, corporate_earnings, corporate_event, public_figure_speech, weather, other. FAN-OUT EXAMPLES: BTC bet → coingecko + fred + gdelt+gnews; Fed bet → fred (DFEDTARU + EFFR + CPIAUCSL) + kalshi_macro (KXFED implied probs) + recent_fed_actions (federal-register rules, last 365d); Hormuz bet → imf_portwatch + airspace + gdelt; Yankees WS → mlb_stats_standings + parent_event partition + news; hottest-year bet → climate_projection_nyc + gistemp_latest (NASA global anomaly, rank since 1880) + news; NVDA-vs-AAPL → finnhub get_quote + edgar shares-outstanding (derived market cap) + edgar filings + news. RESPONSE SHAPES: result.market carries best_bid/best_ask/spread_pp/liquidity/price_change_1h/1d/1w; result.analysis carries model_probability/edge_pp/kelly_fraction_half when a closed-form model fires PLUS a 24h-move warning ("Market moved X.Xpp in 24h, comparable to model edge — your edge may already be priced in") when relevant; result.evidence is keyed by source. RESOLVER CONTRACT: result.market_match_confidence ∈ {high, medium, low, none}, market_match_score (0-1 token-overlap), market_match_alternatives[] (other candidate markets the resolver considered), and suggestions[] (explicit re-query hints when the match is fuzzy) — ALWAYS inspect these before trusting the analysis block, because medium/low matches can still surface other fields. PARENT_EVENT EXTRACTOR: when the bet is one leg of a partition (Yankees WS, Romania election), result.parent_event{matched_candidate, top_legs_by_price[], partition_size, placeholders_filtered} gives you the peer prices in one place — that's the headline for elections/championships. NEWS FIELDS: news entries carry _fallback_attempted / _fallback_failed_reason / retry_after_sec when GDELT 429s and GNews backfill ran or failed. SAFETY: low-confidence resolutions short-circuit with status:"low_confidence_match" and suppress analysis fields so agents can't accidentally size on phantom matches. Closed/dead markets that ARE still indexed by Polymarket (yes_price≈0, no volume, no liquidity) return status:"market_closed_or_inactive" and skip fan-out. In practice resolved markets are usually de-indexed and instead surface via the low_confidence_match path above — both routes are BLOCKING, just different mechanisms. Wide-spread markets (>10pp) carry tradeability:"illiquid_wide_spread" + an explanatory note. RESOLUTION-RULE RISK: market.cancellation_rule parses the void/postponement settlement out of the resolution text — refund_50_50 (shares settle flat 50¢ on void; EV-material for any entry away from 50¢, with ev_impact quantified), resolves_no_on_cancel, resolves_yes_on_cancel, carries_to_reschedule, or mentioned_unclear. null means the description never mentions cancellation. Check this before sizing sports/esports/event-occurrence bets — audited arb-bot ledgers show flat-50¢ void settlements are a recurring pure-rules loss.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNoquick = 2-3 evidence sources, thorough = full fan-out. Default thorough.
marketYesPolymarket slug ("will-bitcoin-hit-150k-by-june-30-2026"), full URL ("https://polymarket.com/event/..."), or question text ("Will Bitcoin hit $150k by June 30?")
include_rawNoDefault false. When false (recommended), FRED/FDA/GDELT/Federal-Register evidence is summarized to the few fields agents actually use — keeps responses under ~20KB. Pass true to get full upstream payloads (50KB-500KB) when you need to recompute deltas, cite specific observations, or post-process.

TDQS

A4.5/5.0
Behavior5/5

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

The description extensively discloses behavioral traits beyond annotations: resolver contract (market_match_confidence, alternatives), fan-out logic, safety mechanisms (low-confidence short-circuit, closed market handling, wide-spread warnings), resolution-rule risk (cancellation_rule parsing). All this adds significant value beyond the readOnlyHint, openWorldHint, idempotentHint annotations.

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?

The description is very long and detailed, with multiple sections (CLASSIFIERS, FAN-OUT EXAMPLES, etc.). While well-structured, it contains more detail than strictly necessary for agent selection and invocation, potentially overwhelming. Concise front-loading of purpose and usage is good, but overall length reduces conciseness score.

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 no output schema, the description fully compensates by detailing return shapes (result.market, analysis, evidence), resolver contract, parent event extraction, news fields, and failure modes. All edge cases (low confidence, closed markets, wide spreads, cancellation rules) are covered, making the tool complete for agent use.

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%, so baseline is 3. The description adds context beyond schema: explains depth='quick' vs 'thorough' (2-3 vs full fan-out) and include_raw default false and its effect on response size. This slightly enhances understanding.

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 researches Polymarket bets by pulling Pipeworx data, specifying input methods (slug, URL, question text). It distinguishes from sibling tools like 'ask_pipeworx' by focusing on bet-specific research and evidence packet generation.

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 explicitly states use cases: 'should I bet on X', 'what does the data say about Y', 'is there edge in Z'. It provides context on when to use but does not explicitly exclude alternatives or state when not to use, leaving some ambiguity.

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

Multiple tool families overlap heavily: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded all route through the same 5,721 tools, polymarket_edges/polymarket_arbitrage/bet_research/polymarket_fill_risk all target prediction-market opportunities, and ai_visibility_check vs scan_competitor_ai_presence cover the same probe. An agent would struggle to pick the right one without reading every description carefully.

Naming Consistency3/5

There are coherent subfamilies (ask_pipeworx_*, polymarket_*, remember/recall/forget, list/read/fetch_feed), but the overall set mixes verb-first names (list_feeds, validate_claim, fetch_feed) with noun-first names (entity_profile, bet_research, deep_research) and adjective-led names (recent_alerts, recent_changes). The inconsistency is noticeable but not chaotic.

Tool Count2/5

34 tools is heavy for a server branded 'Law Feeds,' and many tools are off-domain (Polymarket betting, npm dependency scanning, AI visibility marketing, generic memory). The breadth could justify a larger catalog, but the overlapping research/Polymarket tools inflate the count beyond what the surface needs.

Completeness3/5

As a general data-gateway, the set is fairly complete: routing, grounded answers, deep research, entity resolution, comparison, subscriptions, memory, and feedback are all covered. Relative to the 'Law Feeds' identity, though, the surface is shallow — only list_feeds, read_feed, and fetch_feed serve that purpose, with no feed search, management, or update capabilities.