Skip to main content
Glama

Senate Lobbying

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?

The description discloses extensive behavioral details beyond annotations: the resolution process (market_match_confidence, fan-out), safety mechanisms (low-confidence short-circuit, closed/dead markets, wide-spread markets), and resolution-rule risk (cancellation_rule parsing). Annotations already indicate readOnly/OpenWorld/idempotent, and the description aligns perfectly, adding 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 long but front-loaded with purpose and use cases. Every section (classifiers, fan-out examples, response shapes, safety) earns its place for a complex tool. It could be slightly more concise, but the detail is justified by the tool's sophistication.

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 (no output schema, 3 parameters, intricate fan-out logic), the description is remarkably complete. It covers inputs, outputs, edge cases (low confidence, closed markets, wide spreads), resolution rules, and interpretation guidance (parent_event, news fields). No obvious gaps remain.

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%, so baseline is 3. The description adds value by explaining the effect of parameters like depth (quick vs. thorough fan-out) and include_raw (summarized vs. full payloads). It also provides context on the market parameter accepting multiple formats (slug, URL, question). This goes beyond the schema's basic type/description.

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 specifies the tool's action: research a Polymarket bet by pulling Pipeworx data. It gives concrete examples of inputs (slug, URL, question text) and outputs (evidence packet, market-vs-model comparison). It also explicitly states use cases ('should I bet on X', etc.), distinguishing it from sibling tools like polymarket_edges.

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 use cases ('Use for...') and classifier examples. It does not explicitly list when not to use or alternatives, but the context of sibling tools implies specialization. The guidance is clear enough for an AI to decide when to invoke this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Senate Lobbying toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

There are several near-duplicate clusters: ask_pipeworx, ask_pipeworx_beta (explicitly described as currently identical), ask_pipeworx_grounded, deep_research, and validate_claim all route to the same underlying data; the six Polymarket tools (bet_research, arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) heavily overlap in purpose and are easy to confuse. Even detailed descriptions do not fully resolve which tool an agent should pick first.

Naming Consistency3/5

All names use snake_case and many follow a verb_noun pattern (search_filings, get_filing, list_issue_codes, resolve_entity), but there is a mix of verb-first names (ask_pipeworx, compare_entities, generate_llms_txt), noun-first names (recent_changes, entity_profile, pipeworx_trending), and brand-prefixed families (pipeworx_*, polymarket_*). The naming is readable but not a single predictable pattern.

Tool Count2/5

A server named 'Senate Lobbying' exposes 34 tools, but only three (search_filings, get_filing, list_issue_codes) relate to LDA lobbying data. The remaining 31 cover generic Pipeworx data lookup, prediction markets, memory, subscriptions, AI visibility, and npm package audits—an extreme overreach for the apparent scope and likely to confuse an agent expecting a focused lobbying toolkit.

Completeness2/5

For the lobbying domain implied by the server name, only search, get-one-filing, and issue-code enumeration exist; there is no aggregation/stats tool, no lobbyist/client entity resolution for LDA, no registrant or foreign-entity browsing, and no coverage of related concepts like lobbying firm hierarchies or spending trends. The generic Pipeworx tools fill a different domain, so the lobbying-specific surface has significant gaps.

Resources