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Glama

crypto

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?

Despite strong annotations (readOnly, openWorld, idempotent, non-destructive), the description goes far beyond them by disclosing critical safety behaviors: low-confidence matches short-circuit with status:'low_confidence_match' and suppress analysis; closed/inactive markets return status:'market_closed_or_inactive'; wide-spread markets carry tradeability:'illiquid_wide_spread'; resolution-rule risk parses cancellation rules and quantifies ev_impact. It even explains GDELT 429 fallback behavior. No contradiction 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 long and dense, but its structure is well-organized with uppercase section headers (RESOLVER CONTRACT, PARENT_EVENT EXTRACTOR, NEWS FIELDS, SAFETY, RESOLUTION-RULE RISK). Each section delivers actionable information without fluff, and the length is justified by the tool's complexity. Minor redundancy exists between SAFETY and RESOLVER CONTRACT regarding low-confidence matches, preventing a perfect 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?

With no output schema, the description fully carries the burden of explaining return values. It does so thoroughly: result.market fields, result.analysis with model probabilities and edge, result.evidence keyed by source, resolver contract fields, parent_event structure, and status codes. It also covers classifiers, fan-out examples, safety mechanisms, and resolution-rule risk—covering every aspect a user agent needs to invoke the tool and interpret results correctly.

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?

Although schema coverage is 100% (baseline 3), the description adds meaningful value beyond the schema: market input formats are expanded with examples and question-text clarification, include_raw gains 'when you need to recompute deltas, cite specific observations, or post-process', and depth's quick vs thorough fan-out is illustrated via concrete examples (BTC, Fed, Hormuz). The description also enriches understanding of what the parameters ultimately affect in the output structure.

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 opens with a specific verb-resource pair: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It clearly states inputs (slug, URL, question text), the resolution/classification/fan-out workflow, and the output (evidence packet + market-vs-model comparison). The scope is unambiguous and easily distinguishes it from sibling tools like deep_research or get_crypto_price.

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 says to use it for 'should I bet on X', 'what does the data say about Y', or 'is there edge in Z', which is a clear usage context. It also explains when to set include_raw=true (needing to recompute deltas, cite observations, post-process). However, it does not explicitly state exclusions or when-not-to-use compared to alternatives like get_crypto_price or ask_pipeworx, so it falls short of a 5.

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
Disambiguation4/5

Most tools have distinct purposes with clear descriptions, but some overlap exists (e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research all retrieve structured data with subtle differences). Competitor analytics (ai_visibility_check, scan_competitor_ai_presence) also share similar goals.

Naming Consistency3/5

Names follow loose patterns within subgroups (get_crypto_*, polymarket_*, ask_pipeworx*), but overall there is no single consistent convention. Verbs and noun orders vary (e.g., get_crypto_price vs. validate_claim vs. remember).

Tool Count3/5

35 tools is high; many exceed the core 'crypto' domain (company profiles, npm dependencies, memory management, subscriptions). While each tool seems justified, the count feels heavy for a single server, risking cognitive load.

Completeness4/5

The tool set covers crypto basics (price, market, history), company data, prediction markets, and general data retrieval comprehensively. Minor gaps exist (e.g., no direct on-chain crypto data), but cross-domain coverage is strong.

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