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Glama

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / examples
      Previous value: -[
      -  {
      -    "market_input": "will fed cut rates in june 2026"
      -  },
      -  {
      -    "market_input": "https://polymarket.com/event/will-bitcoin-hit-150k-by-june-30-2026"
      -  }
      -]New value: +[
      +  {
      +    "market": "will-bitcoin-reach-100k-in-july-2026"
      +  },
      +  {
      +    "market": "https://polymarket.com/event/will-bitcoin-hit-150k-by-june-30-2026"
      +  }
      +]
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
      +  {
      +    "market_input": "will fed cut rates in june 2026"
      +  },
      +  {
      +    "market_input": "https://polymarket.com/event/will-bitcoin-hit-150k-by-june-30-2026"
      +  }
      +]
  3. Changed1 schema field changed
    • addedInput schema / properties / include_raw
      Added value: +{
      +  "description": "Default 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.",
      +  "type": "boolean"
      +}
  4. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds extensive behavioral context: low-confidence short-circuiting, closed market detection, wide spread warnings, resolution-rule risk parsing, and parent event extraction. 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 long and dense but well-structured with clear section headings (RESOLVER CONTRACT, PARENT_EVENT EXTRACTOR, etc.). Front-loaded with core purpose. While verbose, it earns its length by covering essential details for safe and effective tool use; minor redundancy in examples could be trimmed.

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?

Despite no output schema, the description thoroughly documents response shapes, match confidence levels, parent events, news fallback fields, and cancellation rules. Covers edge cases (low confidence, closed markets, wide spreads, resolution risk) and provides actionable instructions (e.g., ALWAYS inspect resolver contract fields).

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% with descriptions for all three parameters (market, depth, include_raw). The description adds examples (slug, URL, question text), default values, and recommendations (e.g., 'Default thorough', 'Default false (recommended)'), slightly enhancing clarity beyond the schema.

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 a Polymarket bet by pulling Pipeworx data, including specific inputs (slug, URL, question text) and outputs (evidence packet, market-vs-model comparison). It distinguishes from sibling tools like polymarket_edges by stating use cases like 'should I bet on X' versus pure edge computation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit guidance on when to use ('should I bet on X', 'what does the data say about Y', 'is there edge in Z') and when not (low-confidence, closed markets, wide spreads). Includes warnings to inspect resolver contract fields and cancellation rules before trusting analysis, with detailed fallback behavior and market status handling.

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 clearly distinct purposes, but several overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical (only differing in verification/depth), and polymarket_edges / polymarket_arbitrage / polymarket_edge_tracker / polymarket_fill_risk / polymarket_kalshi_spread all target similar prediction-market signals, which could cause mis-selection without careful reading.

Naming Consistency4/5

Most names follow a clear verb_noun pattern (resolve_entity, query_table, remember, recall, forget, subscribe, unsubscribe, validate_claim, compare_entities, search_within), but there are exceptions like ai_visibility_check (adjective_noun), generate_llms_txt (verb_noun with dot), and several polymarket_* names that are fine but inconsistent with the snake_case verb-first convention.

Tool Count5/5

35 tools is a large but reasonable surface for a broad data/serach platform covering company financials, economics, prediction markets, memory, subscriptions, and discovery. The count is justified by the wide domain, and the set is not bloated with trivial duplicates.

Completeness4/5

The surface covers core CRUD for entities (resolve, profile, compare, search, query) and memory (remember/recall/forget), plus subscriptions and meta-tools. Minor gaps: no explicit tool for updating/creating entities (understandable for a read-only data service), and no tool for listing all available table schemas beyond discovery (subjects covers this). Overall strong coverage.