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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

A4.3/5.0
Behavior5/5

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

The description is exceptionally transparent, detailing resolution confidence levels (high/medium/low/none), short-circuit behavior for low confidence and closed markets, spread handling with illiquidity warnings, cancellation rule parsing with specific examples and risk quantification, and news fallback fields. All of this adds value beyond the annotations (readOnlyHint, idempotentHint, etc.) and reveals no contradiction.

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 comprehensive but verbose. Critical information is front-loaded, but the text then meanders through many detailed sections (e.g., resolver contract, parent event extractor, news fields, safety, resolution-rule risk). Some repetition exists (resolution details appear in multiple paragraphs). With better structuring (e.g., bullet points or clear sections), it could be more concise while retaining all information.

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?

The description provides exhaustive context about the tool's behavior, response structure, edge cases, and safety mechanisms. Despite lacking an output schema, it meticulously documents the return shape (result.market with specific fields like best_bid, model_probability; result.analysis conditions; result.evidence keyed by source; resolver contract details; parent event extractor; news fields with fallback info). This level of detail ensures an AI agent can correctly interpret and use the tool's output.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear parameter descriptions. The tool description does not add significant new meaning to the three parameters; it reiterates the input format for 'market' and mentions 'depth' implicitly, but provides no additional constraints or context. Baseline score of 3 is appropriate given high schema coverage.

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 relevant Pipeworx data in one call. It specifies the input formats (slug, URL, question text) and the high-level workflow (resolve, classify, fan out, return evidence packet plus comparison). This distinguishes it from sibling tools like polymarket_edges which focus on edge detection, making the purpose unambiguous.

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 provides usage examples: 'should I bet on X', 'what does the data say about Y', 'is there edge in Z'. It also gives extensive examples of classifiers and fan-out scenarios. However, it does not offer explicit guidance on when to avoid this tool or when to prefer alternatives like polymarket_edges or polymarket_arbitrage. The usage context is clear but lacks exclusionary guidance.

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
Disambiguation3/5

While many tools target distinct resources, there is notable overlap between ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and between discovery tools (discover_tools vs suggest_questions). The Polymarket prediction tools also have blurred boundaries (polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage). This overlap can confuse an agent trying to select the right tool.

Naming Consistency4/5

Tool names consistently use snake_case and are mostly descriptive. However, naming patterns vary: some start with verbs (list_subscriptions, validate_claim), others with nouns (ask_pipeworx, bet_research). The memory tools use single-word imperatives (remember, recall, forget), which differ from the multi-word pattern. Overall, it's readable but not rigidly consistent.

Tool Count3/5

34 tools is on the high side for a single server, but the breadth of domains (data access, prediction markets, biotech, subscriptions) justifies the count. Some tools feel peripheral (gene_annotations, generate_llms_txt) and could be split off, making the set slightly bloated for the core purpose of business/financial data and prediction markets.

Completeness4/5

The tool surface covers a wide range of data sources (SEC, FDA, FRED, patents, news) and prediction market analysis comprehensively. However, there are minor gaps: no direct web search tool, no tool for fetching a specific SEC filing by accession (though ask_pipeworx may cover it). The set supports most core workflows without dead ends.

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