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Glama

Ai Briefing

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. 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. Added

TDQS

A4.6/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint=true, idempotentHint=true), the description extensively discloses behavior: low-confidence resolutions short-circuit with status, closed/dead markets return 'market_closed_or_inactive', wide-spread markets carry tradeability flags, and cancellation rules are parsed. It also explains resolver contract, parent event extractor, and news field fallback logic, providing comprehensive transparency.

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 verbose with multiple paragraphs and capitalized sections. While well-structured and front-loaded with the core purpose, it contains extensive details (e.g., cancellation rule parsing, news fields) that could be condensed. Not every sentence is essential for an AI agent to select and invoke the tool.

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?

Without an output schema, the description thoroughly explains return shapes: result.market, result.analysis, result.evidence, resolver contract, parent event, news fields, and edge cases (low confidence, closed markets, wide spreads, cancellation rules). It covers all necessary behavioral and response details for correct usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the description adds significant meaning: it explains the three accepted input formats for 'market', the 'depth' enum values ('quick' vs 'thorough' with default), and 'include_raw' flag behavior (summarized vs full payloads). This contextualizes parameters beyond the schema, aiding correct invocation.

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, specifying input formats (slug, URL, question text) and actions (resolve, classify, fan-out, return evidence packet and comparison). It distinguishes from sibling tools like polymarket_edges or polymarket_arbitrage by focusing on comprehensive research for a single bet.

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 states use cases: 'should I bet on X', 'what does the data say about Y', 'is there edge in Z'. It provides detailed fan-out examples by category, offering clear context. However, it does not specify when not to use the tool or mention alternatives beyond the implicit differentiation from sibling tools.

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, such as multiple tools for querying Pipeworx data (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim) and numerous tools for AI news/tools (get_ai_news, get_ai_toolbelt, get_briefing, get_model_landscape, etc.). This will cause an agent to frequently misselect the appropriate tool.

Naming Consistency4/5

Most tools follow a verb_noun pattern in snake_case (e.g., compare_entities, discover_tools, get_briefing). However, a few deviate like 'bet_research' (noun_verb) and 'what_happened' (phrase), but overall the pattern is largely consistent.

Tool Count2/5

38 tools is excessive for a server called 'Ai Briefing', which suggests a focused purpose. The tool count spans multiple domains (AI visibility, Pipeworx queries, Polymarket betting, memory, subscriptions) making it feel overstuffed and unfocused.

Completeness3/5

The tool set covers many aspects of its broad domain (querying, comparing, subscribing, memory), but there are notable gaps: no tool for modifying subscriptions, no user profile management, and the AI news tools overlap rather than cover distinct needs.

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