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Arcgis Lacounty

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 already mark it read-only and idempotent, but the description adds substantial behavioral detail: parallel fan-out, low-confidence short-circuiting with status 'low_confidence_match', 'market_closed_or_inactive' handling, wide-spread illiquidity flags, and cancellation-rule parsing with EV impact. It transparently warns about resolution-rule risks and blocking behavior.

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 headers (RESPONSE SHAPES, RESOLVER CONTRACT, etc.) and each section adds critical operational details. No filler exists, but the length could be trimmed for agents with limited context windows. Still, it earns its length through specificity.

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 compensates comprehensively. It documents response shapes, resolver contract fields, parent event extraction, news fallback fields, safety statuses, and cancellation-rule semantics. It also lists the full classifier taxonomy and includes examples of fan-out for different bet categories, covering edge cases like wide spreads and closed markets.

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

Parameters3/5

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

The schema already covers all three parameters with 100% description coverage, including formats and defaults. The tool description reiterates the market input forms but adds no new meaning for parameters. It does provide context for how fan-out works, but that is not directly tied to parameter semantics.

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 a specific action: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It explains the process (resolve, classify, fan out, return evidence packet) and distinguishes from siblings like polymarket_edges or polymarket_arbitrage, which focus on opportunity scanning rather than per-bet research.

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 explicitly tells when to use it: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also describes blocking behaviors for low-confidence and closed markets, helping the agent decide if the tool is appropriate and how to interpret results.

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
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim all handle natural-language data queries, with ask_pipeworx_beta currently identical to ask_pipeworx. discover_tools and suggest_questions both exist to help agents find tools, and the five Polymarket tools have subtle, hard-to-distinguish boundaries. Agents will frequently select the wrong tool without careful reading.

Naming Consistency4/5

Tool names are mostly lowercase snake_case with verb-noun structure (query_layer, search_datasets, resolve_entity), which is consistent and readable. However, some names break the pattern (entity_profile, layer_info, recent_changes, pipeworx_feedback) and the prefixes are not uniform. Still, the convention is predictable enough to navigate.

Tool Count2/5

34 tools is a heavy count, and the vast majority are unrelated to the server's stated 'Arcgis Lacounty' purpose. Only three tools (search_datasets, query_layer, layer_info) serve the named GIS domain, while the rest form a sprawling collection of data-lookup, prediction-market, and utility tools. This is a severe scope mismatch.

Completeness1/5

The tool surface is severely incomplete for an ArcGIS LA County server: no layer listing beyond keyword search, no metadata endpoints, no editing, no spatial operations. The broader tool set lacks a coherent domain, making coverage impossible to assess beyond noting the glaring absence of core GIS functionality.

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