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Gdacs

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 simple readOnly/non-destructive annotations, disclosing detailed behavioral nuances: the resolver contract with match confidence levels and suggestions, short-circuiting on low-confidence matches, handling of closed/de-indexed markets, wide-spread illiquidity notes, cancellation-rule risk with EV impact, and news fallback fields. It fully prepares the agent for edge cases and safe invocation, with no contradiction to the 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 but well-organized with labeled sections (RESOLVER CONTRACT, PARENT_EVENT EXTRACTOR, NEWS FIELDS, SAFETY, RESOLUTION-RULE RISK) and front-loaded with the core purpose and use cases. Every section contributes critical operational detail, so the length is justified for the tool's complexity, though a more concise summary might improve scannability.

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 thoroughly covers response shapes (result.market, result.analysis, result.evidence), status codes, safety paths, and resolution-rule risk. It explains both blocking routes for dead markets, parent-event extraction for partitions, and news fallback behavior, making the tool's behavior fully specified for an agent without needing external documentation.

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?

The schema already describes all three parameters, but the description adds meaning through concrete examples of market inputs, an explanation of the depth parameter's behavior, and a rationale for include_raw (response size tradeoff, when to override the default). This enriches the schema's baseline, making the parameters more actionable.

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: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It clearly defines the tool's scope with input formats (slug, URL, question text) and explicit use cases ('should I bet on X', 'what does the data say about Y', 'is there edge in Z'). This distinguishes it from siblings like validate_claim or ask_pipeworx by focusing on market research with cross-source data.

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?

Explicitly states 'Use for ...' with three common query types and provides a classifier list plus fan-out examples, which strongly imply when this tool is appropriate. It does not explicitly name sibling tools as alternatives or give 'when not to use' exclusions, but the context is clear enough for an agent to select this tool for bet-specific research queries.

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

A3.5/5.0
Disambiguation4/5

Most tools have clear, distinct purposes, but the three ask_pipeworx variants (especially ask_pipeworx_beta, identical to ask_pipeworx) and the six Polymarket tools overlap conceptually and could cause misselection. Detailed descriptions largely compensate, but the boundaries between some meta-tools (e.g., ask_pipeworx vs deep_research vs bet_research) require careful reading.

Naming Consistency3/5

All names are snake_case, but conventions are mixed: verb-first (ask_pipeworx, compare_entities, discover_tools), noun-first compounds (entity_profile, polymarket_arbitrage), and single-word nouns (event, events, rss). The pattern is predictable for common actions but inconsistent across the set, making it harder to guess names for related tasks.

Tool Count2/5

At 35 tools, the count is heavy and the server named 'Gdacs' includes many unrelated tools (Polymarket, npm scanning, AI visibility), indicating scope creep. Several tools could be consolidated (e.g., the ask_pipeworx family and multiple pattern-market scanners), and the breadth dilutes the disaster-alerting focus implied by the server name.

Completeness4/5

The surface covers core workflows: data querying (ask/research/validate), entity profiling, prediction-market analysis (arbitrage/edges/fill risk), and subscription management (create/list/cancel). Minor gaps exist, such as no direct fetch tool for a specific Pipeworx pack and limited GDACS event management (only read operations), but these are workable with the provided meta-tools.

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