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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.

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. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Even with comprehensive annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false), the description adds substantial behavioral detail: fan-out mechanism by category, low-confidence short-circuit, closed market handling, wide-spread liquidity 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.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is comprehensive but verbose, with long paragraphs and many examples. It front-loads the purpose but includes extensive detail on classifiers, fan-out examples, response shapes, and edge cases that could be condensed. It earns its place but could be more concise without losing clarity.

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 complexity (3 parameters, no output schema, rich behavior), the description is exceptionally complete. It covers all major aspects: input formats, fan-out logic, response structure (market, analysis, evidence, resolver contract), parent event extraction, news fallback mechanisms, safety blocking, and resolution-rule risk. Agents have full context for correct invocation.

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%, so baseline is 3. The description adds value beyond the schema by explaining the market parameter as accepting slug, URL, or question text; providing examples; detailing the depth enum behavior (quick vs thorough) and include_raw trade-offs (payload size). The parameter descriptions in the schema are already good, but the narrative adds context.

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?

Purpose is crystal clear: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies verb (research), resource (Polymarket bet via Pipeworx), and scope (single call, parallel fan-out). The description distinguishes from sibling tools like polymarket_edges and polymarket_arbitrage by focusing on a single bet's evidence packet and market comparison.

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?

Explicit usage scenarios are given: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' The description also provides guidance on inspecting market_match_confidence before trusting analysis, and explains blocking paths for low-confidence/closed markets. However, it does not explicitly contrast with related sibling tools or state when not to use this tool.

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

A3.8/5.0
Disambiguation3/5

Several tools cluster around the same underlying data router (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and the prediction-market family has five overlapping members, so misselection is possible. The descriptions are detailed enough to separate most intents, but ask_pipeworx_beta is explicitly identical to ask_pipeworx right now and ai_visibility_check/scan_competitor_ai_presence are close cousins.

Naming Consistency4/5

All tool names consistently use lowercase snake_case, and most follow a clear verb_noun shape like ask_pipeworx, get_series, subscribe, or validate_claim. A few noun-style names (entity_profile, pipeworx_trending, polymarket_edges) and the bare memory verbs (remember, recall, forget) break the pattern slightly, but the overall convention is predictable.

Tool Count2/5

33 tools is a heavy surface that exceeds the 25-tool threshold where selection cost becomes a real problem for agents. The count is inflated by auxiliary concerns like memory, subscriptions, feedback, trending, and AI-presence scans that sit alongside the core data-access mission.

Completeness4/5

The core data-research workflows are well covered: discovery (discover_tools, suggest_questions), retrieval (ask_pipeworx, deep_research, get_series), entity resolution (resolve_entity), profiles, comparisons, claim validation, and prediction-market analysis all have end-to-end support. Memory and subscription lifecycles are also complete. Minor gaps exist — some sources soft-fail and there is little Argentina-specific tooling beyond the time-series pair — but agents can generally work around them.

Resources