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by io.github.pipeworx-io

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

A4.6/5.0
Behavior5/5

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

The description goes far beyond the annotations (readOnlyHint, openWorldHint, idempotentHint) by detailing extensive behavioral traits: it explains the resolution process, classifier fan-out, safety mechanisms (low-confidence short-circuit), closed market handling, wide spread warnings, and cancellation rule parsing. It also describes the RESOLVER CONTRACT and PARENT_EVENT EXTRACTOR. 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.

Conciseness4/5

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

The description is verbose but front-loaded with the core purpose in the first sentence. It uses headers like RESOLVER CONTRACT and PARENT_EVENT EXTRACTOR to organize complex information. While every sentence adds value, the length could be trimmed without losing essential detail. A slightly more structured summary would improve conciseness.

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 absence of an output schema and the tool's complexity (3 parameters, multiple data sources, edge cases), the description is remarkably thorough. It covers the response shape (market, analysis, evidence fields), resolution confidence, parent event extraction, news fallback, safety overrides, illiquid spreads, and cancellation rules. The agent has all necessary context to use the tool correctly and interpret results.

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?

With 100% schema coverage, baseline is 3. The description adds value by explaining the 'market' parameter can be slug, URL, or text (already in schema), but more importantly details 'depth' with quick vs thorough behavior ('2-3 evidence sources' vs 'full fan-out') and 'include_raw' with size estimates (~20KB vs 50-500KB). These additions help the agent choose parameters effectively.

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: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It identifies the specific resource (Polymarket bet) and action (research via data pull), and distinguishes itself by mentioning the fan-out to multiple data sources in one call. Examples like 'should I bet on X' further clarify its use, setting it apart from sibling tools like polymarket_edge_tracker.

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 lists three use cases: 'should I bet on X', 'what does the data say about Y', and 'is there edge in Z'. This provides clear context for when to use the tool. However, it does not explicitly mention when not to use it or directly compare to alternatives like polymarket_arbitrage or deep_research, which would have improved the score.

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

A4/5.0
Disambiguation2/5

ask_pipeworx, ask_pipeworx_beta (currently identical in behavior), and ask_pipeworx_grounded overlap heavily, and the six-tool Polymarket cluster (edges, arbitrage, fill_risk, edge_tracker, kalshi_spread, bet_research) requires careful reading to distinguish. Descriptions are detailed, but several tools present real selection ambiguity.

Naming Consistency4/5

Nearly all tools use snake_case with a mostly verb-first or resource-first pattern (ask_, list_, fetch_, read_, subscribe, validate_claim). Minor deviations like entity_profile and recent_changes break the verb-noun pattern slightly, but the overall naming is predictable.

Tool Count2/5

34 tools is excessive for the 'Science Feeds' name, which implies a narrow feed-reading service; only 3 tools actually relate to feeds. The rest form a broad Pipeworx grab bag (memory, npm scanning, AI visibility, prediction markets, feedback), making the set feel unfocused and overweight.

Completeness4/5

For the broad query/research domain the descriptions actually establish, coverage is strong: discovery, single lookups, grounded/refusal-safe answers, deep research, entity resolution, comparison, change feeds, claim validation, subscriptions, memory, and feedback are all present. The literal science-feed surface is thin, but the toolkit as a whole has few dead ends.

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