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Glama

Odds Api

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

A4.5/5.0
Behavior5/5

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

The description extensively discloses behavioral traits beyond annotations. It covers response shapes (result.market, result.analysis, result.evidence), resolver contract (confidence, alternatives, suggestions), parent_event extractor, news fallback fields, safety features (low-confidence short-circuit, closed markets blocking), wide-spread warnings, and cancellation rule parsing. All these details align with readOnlyHint and destructiveHint false.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

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

The description is extremely verbose and runs on with many details that could be structured in the output schema or separate documentation. While it is organized with section headers (RESPONSE SHAPES, RESOLVER CONTRACT, etc.), the sheer length (over 200 words) makes it less concise than ideal. Several sentences provide highly specific edge-case information that may not be immediately relevant for initial tool selection.

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's complexity (multiple data sources, category fan-outs, various response fields, edge cases) and the absence of an output schema, the description is remarkably complete. It covers input variations, processing logic, response structure with all subfields, safety mechanisms (low-confidence, closed markets, wide spreads), and cancellation rules. An agent can fully understand the tool's behavior without external documentation.

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?

Although schema coverage is 100%, the description adds significant value. For 'market', it explains accepted formats (slug, URL, question text). For 'depth', it clarifies that 'quick' uses 2-3 sources and 'thorough' uses full fan-out, with default 'thorough'. For 'include_raw', it details when to set true vs false and the size implications. These additions go beyond mere parameter names.

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's purpose: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies input types (slug, URL, question text) and output goals (evidence packet, market-vs-model comparison). It also distinguishes from sibling tools by framing its usage for betting research, e.g., 'Use for "should I bet on X"'. This is specific and actionable.

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 provides usage scenarios: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also lists classifier categories and fan-out examples, helping the agent understand when this tool is appropriate. However, it does not explicitly state when not to use it or mention alternative tools for different use cases, which keeps it from a perfect 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

A3.8/5.0
Disambiguation3/5

Most tools have distinct scopes, and the long cross-referencing descriptions help a lot. However, ask_pipeworx_beta is currently an exact duplicate of ask_pipeworx by the server's own description, and the polymarket_* family plus ask_pipeworx/ask_pipeworx_grounded/deep_research have overlapping boundaries that could mislead an agent.

Naming Consistency4/5

The vast majority follow a clear verb_noun snake_case pattern like get_odds, list_sports, resolve_entity, and subscribe. A few exceptions such as odds_api_quota, pipeworx_feedback, polymarket_arbitrage, and recall break the pattern slightly, but the overall convention is predictable.

Tool Count2/5

37 tools is well past the 25+ threshold and feels bloated for a server named 'Odds Api'. Many tools are meta-platform utilities — memory, feedback, trending, dependency scanning, llms.txt generation — that have no obvious connection to an odds API and make the surface hard to navigate.

Completeness4/5

The odds domain is well covered: list_sports, get_events, get_odds, get_event_odds, get_scores, quota tracking, and subscriptions form a coherent read/monitor workflow. Minor gaps exist — no historical odds or a single-event detail endpoint — but agents can complete core odds research and monitoring tasks without dead ends.

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