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Glama

Celestrak

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

A4.9/5.0
Behavior5/5

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

The description details behavioral traits beyond annotations: low-confidence matches short-circuit, closed markets skip fan-out, wide-spread markets get illiquidity notes, cancellation rules are parsed, and news fallback fields are documented. These are not in readOnlyHint/openWorldHint, adding significant value.

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 verbose but well-structured with clear sections (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES). Every sentence adds necessary detail for a complex tool; however, slight trimming could improve conciseness for LLM token efficiency.

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, the description comprehensively explains return shapes, resolver contract, parent event extraction, news fields, and safety/edge cases. It covers all necessary context for agent invocation.

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?

Despite 100% schema coverage, the description adds context for each parameter: market accepts slug/URL/text, depth has quick/thorough with default, include_raw keeps responses under ~20KB and explains when to use true. Examples complement the schema.

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 in one call, specifying verb, resource, and action. It distinguishes from sibling tools like polymarket_arbitrage by focusing on research rather than edge detection.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit use cases ('should I bet on X', 'what does the data say about Y', 'is there edge in Z') and detailed fan-out examples per category. It also advises checking market_match_confidence before trusting analysis, offering clear guidance on when results are reliable.

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
Disambiguation2/5

The tool set mixes several distinct domains (satellite orbital data, Pipeworx data routing, prediction-market analysis, memory management, subscriptions), but within the Pipeworx umbrella there is heavy overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route natural-language questions to the same underlying 5,756 tools. An agent could easily misselect between them, especially since ask_pipeworx and ask_pipeworx_beta are described as currently identical.

Naming Consistency3/5

Many tools follow a clear verb_noun pattern (list_subscriptions, create... none, but compare_entities, resolve_entity, generate_llms_txt, scan_dependency, subscribe/unsubscribe, remember/recall/forget), yet the naming is inconsistent across the set: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, get_satellite, get_group, recent_alerts, recent_changes, entity_profile, and deep_research do not share a uniform convention. CamelCase appears in polymarket_arbitrage, polymarket_edges, etc. while most others are snake_case, and the satellite tools (get_satellite, get_group, search_by_name) form a distinct sub-pattern that clashes with the Pipeworx meta-tools.

Tool Count2/5

34 tools is on the heavy side, and the effective surface is bloated: there are three variants of ask_pipeworx, four polymarket_* tools, three satellite-specific tools that are unrelated to the server's apparent core purpose, and several meta/utility tools (remember, recall, forget, pipeworx_feedback, pipeworx_trending, suggest_questions) that could be consolidated or are only tangentially related. The count itself is not extreme, but the scope is muddled: the server claims the name Celestrak (satellite tracking) while the overwhelming majority of tools are for Pipeworx data access and prediction markets, making the tool count feel inappropriate for either purpose.

Completeness3/5

For the Pipeworx data-access domain, the tool set is quite thorough: natural-language routing, grounded answers, deep research, entity profiling, entity comparison, claim verification, semantic search, and tool discovery are all present. However, there are notable gaps: the subscription lifecycle lacks an update/resume mechanism, and the satellite domain (the server's namesake) is severely incomplete — only three lookup tools with no live tracking, no group listing beyond a handful of groups, and no clear lifecycle CRUD. The memory tools (remember/recall/forget) are minimal but complete for their narrow scope.

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