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Glama

Mapbox

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.

Schema Changelog

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

  1. Changed1 schema field changed
    • changedInput schema / examples
      Before
      [
  {
    "market_input": "will fed cut rates in june 2026"
  },
  {
    "market_input": "https://polymarket.com/event/will-bitcoin-hit-150k-by-june-30-2026"
  }
]
      After
      [
  {
    "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
      [
  {
    "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
      {
  "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

A4.6/5.0
Behavior5/5

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

The description goes far beyond the annotations (which only indicate read-only, open-world, idempotent, non-destructive). It meticulously discloses behavioral traits: the resolver contract with confidence levels and alternatives, the low_confidence_match short-circuit that suppresses analysis, the market_closed_or_inactive path, wide-spread illiquidity handling, news fallback behavior with retry_after_sec, and resolution-rule risk parsing (refund_50_50, etc.). This is exemplary transparency about what happens under various conditions, adding substantial context the annotations cannot provide.

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 every section earns its place: purpose, classifiers, fan-out examples, response shapes, resolver contract, parent event, news fields, safety, and resolution-rule risk. It is front-loaded with the core purpose, then systematically detailed. It uses headings and line breaks for readability, and the density is appropriate for the tool's complexity. A slight deduction because it could be tightened without losing value, but it is well organized.

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 tool has no output schema, the description carries the full burden of explaining return shapes and edge cases. It covers result.market, result.analysis, result.evidence, result.market_match_confidence, parent_event, news fallback fields, and both blocking statuses (low_confidence_match and market_closed_or_inactive). It also addresses wide-spread markets and cancellation-rule risk. Very few tools provide this level of contextual completeness, making it effectively self-contained.

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 each parameter already described. The tool description adds extra meaning by giving concrete examples of the 'market' param (slug, URL, question text) and clarifying the intent of 'include_raw' with byte-size ranges and specific use cases. It also explains that 'depth' controls the fan-out scale, though the schema already covers this. This enrichment plus high schema coverage justifies a 4.

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 action: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It clearly identifies the resource (Polymarket bet), the verb (research), and the scope (one call, data pulling). It also distinguishes itself from sibling tools by framing the use case ('should I bet on X') and listing concrete fan-out examples, making the purpose unmistakable.

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 gives explicit usage guidance: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also provides examples of when to apply specific fan-outs (BTC, Fed, Hormuz, etc.) and safety rules like checking cancellation risk before sizing sports bets. However, it does not explicitly state when *not* to use this tool or name alternative tools for different cases, so it falls just 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

A3.6/5.0
Disambiguation2/5

Many tools have overlapping purposes (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim) and several tools serve similar data-retrieval functions, making it difficult for an agent to distinguish which to use.

Naming Consistency4/5

Tool names mostly follow a consistent verb_noun pattern (e.g., geocode_forward, generate_llms_txt, resolve_entity). A few less descriptive names (forget, recall) exist but overall naming is predictable.

Tool Count2/5

38 tools is far too many for a server branded as 'Mapbox'. Only about 8 tools directly relate to map/geospatial functionality; the rest are unrelated (Pipeworx data, Polymarket, memory). The scope is dramatically overextended.

Completeness2/5

The Mapbox-specific tools lack coverage of major features like style management, tilesets, or data upload. The non-Mapbox tools cover their domains moderately, but the server's overall completeness for its named purpose (Mapbox) is severely lacking.

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