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Steam

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

A4.7/5.0
Behavior5/5

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

Annotations declare readOnlyHint and idempotentHint, but the description adds substantial behavioral detail: low-confidence match short-circuiting, market_closed_or_inactive status, wide-spread illiquidity flags, resolver contract fields, parent_event extraction, news fallback behavior, and cancellation-rule risk. This far exceeds the annotation baseline and accurately describes edge cases without contradicting the read-only hints.

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 well-structured with clear sections (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, RESOLVER CONTRACT, SAFETY, RESOLUTION-RULE RISK). The core purpose is front-loaded in the first sentence. While dense, nearly every sentence delivers actionable detail, so the length is earned.

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?

With no output schema, the description fully explains return values: result.market fields, result.analysis including edge/kelly warnings, result.evidence keying, resolver contract fields, parent_event extraction, news fallback flags, and cancellation-rule risk. It covers normal operation, low-confidence matches, closed markets, illiquidity, and rule-based pitfalls—exceptionally complete for an agent.

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?

Schema coverage is 100% for all three parameters, yet the description adds valuable context: concrete examples of slug/URL/question text for 'market', the meaning of 'depth' (quick = 2-3 sources vs thorough = full fan-out), and 'include_raw' size implications (~20KB vs 50KB-500KB). The fan-out examples also illustrate how depth changes behavior.

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 verb+resource: 'Research a Polymarket bet' and clearly enumerates input formats (slug, URL, question text). It distinguishes the tool from siblings by describing the fan-out to category-specific data packs and the market-vs-model comparison, making its role in evidence gathering explicit.

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 provides explicit when-to-use guidance with 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z"'. It does not explicitly name alternative tools for when not to use it, but the use cases and structure clearly separate it from the sibling arbitrage/edge 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

C2.9/5.0
Disambiguation2/5

Many tools overlap significantly: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical variants, while deep_research, discover_tools, and suggest_questions all serve meta/onboarding purposes. The Steam-specific tools are distinct, but they are drowned out by a large unrelated set (Polymarket, Pipeworx, npm scanning) that makes selection confusing.

Naming Consistency2/5

Tool names follow no single convention: some are verb_noun (resolve_vanity_url, generate_llms_txt), some are noun-only (app_details, player_stats), and others use domain prefixes inconsistently (polymarket_arbitrage, ask_pipeworx_grounded, deep_research). The mix of descriptive and vague names (process, run, execute) adds to the inconsistency.

Tool Count2/5

At 45 tools, the server is heavily overloaded, especially for a server named 'Steam' where only about a third of the tools actually relate to Steam. The rest belong to Pipeworx, Polymarket, and other unrelated domains, making the scope unclear and the tool count far too large for a focused purpose.

Completeness3/5

For the Steam domain, the server covers a reasonable range: app details/news, player counts, friends, owned games, achievements, stats, bans, levels, and summaries. However, notable gaps exist such as store search, reviews, wishlist, or any user inventory/trading features. The non-Steam tools add breadth but do not address these missing Steam operations.

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