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EUR-Lex

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?

Annotations indicate readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds extensive behavioral details beyond annotations: resolution contract (market_match_confidence, suggestions), parent_event extraction, news fallback fields, safety mechanisms (low-confidence short-circuit, closed market handling, wide-spread warnings), and cancellation rule parsing. No contradictions.

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 dense but front-loaded with the core purpose. It is well-structured into sections (classifiers, fan-out examples, response shapes, safety, etc.). Slightly long but earns its length by packing critical behavioral details. Could tighten repetitive elements.

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 no output schema and moderate complexity (3 params, diverse response shapes), the description is exceptionally complete. It explains all key response fields (market, analysis, evidence, market_match_confidence, parent_event, news fields) and edge cases (low confidence, closed markets, wide spreads, cancellation rules). Compensates fully for missing output schema.

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% for 3 parameters. The description adds significant extra meaning: explains how 'market' can be slug, URL, or question text; describes 'depth' enum behavior (quick vs thorough); details 'include_raw' impact on response size. This goes beyond the schema's basic descriptions.

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 starts with a clear verb+resource: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It includes classifiers, fan-out examples, and response shapes, distinguishing it from sibling tools like polymarket_edges or polymarket_arbitrage which serve different purposes.

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 cases: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' Provides extensive examples (e.g., BTC bet, Fed bet). Lacks explicit when-not-to-use or alternative tool references, but the sibling list provides context for differentiation.

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

A3.7/5.0
Disambiguation1/5

Multiple tools appear to do nearly the same thing: ask_pipeworx, ask_ipeworx_beta (explicitly identical at the moment), ask_pipeworx_grounded, deep_research, and validate_claim all route questionanswering in a very similar way. Even with long descriptions, the sheer number of overlapping query/research/analysis tools (ai_visibility_check vs scan_comperitor_ai_presence, all polymarket_*) would make an agent uncertain which to call.

Naming Consistency2/5

The set uses snake_case everywhere but that is the only consistent part. There is a mess of verb_noun patterns, noun_verb patterns (cjeu_search vs search_legislation, cj_judgment vs get_document), bare noun phrases (entity_profile, compliance_index, pipeworx_feedback, polymarket_edges), and verb phrases (ask_ipeworx, generate_elms_txt, resolve_entry). A user cannot predict whether the noun comes first, so naming is readable but not predictable.

Tool Count2/5

39 tools is far over the 25+ threshold for a coherent set, and a large number of them (predictor markets ten, AI visibility, memory, subscriptions, pipework meta-tools) are outside the EUR-Lex legal research domain. The total count suggests a bundled everything-server rather than a focused legal-research MCP. It is not extreme enough for a 1 because 39 is still within a region where a broader meta-pipework suite could plausibly exist — but it's still too many.

Completeness4/5

For the EUR-Lex domain, the legal tools are nearly complete: search_legislation + compliance_index locate acts, get_metadata/list_articles/get_article/get_document read them, and cjeu_search/cjeu_judgment cover case law. Missing links that would make it fully seamless are amendment tracking, cross-references and direct CELEX/EURL-Lex citation search integration, but all basic 'find and read an act or judgment' workflows are supported.

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