bet_research · Mcp Semanticscholar MCP: Risk & Policy

WHAT IT DOES

What bet_research does on Mcp Semanticscholar

AI agents call bet_research to retrieve information from Mcp Semanticscholar without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.

PARAMETERS

Parameter	Type	Required	Description
`depth`	string	—	quick = 2-3 evidence sources, thorough = full fan-out. Default thorough.
`market`	string	Yes	Polymarket 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_raw`	boolean	—	Default false. When false (recommended), FRED/FDA/GDELT/Federal-Register evidence is summarized to the few fields agents actually use — keeps responses under ~2

Parameters from the server's own tool schema.

WHY IT NEEDS A POLICY

Why bet_research needs a policy

This tool queries and retrieves scholarly/market data to support decision-making but does not execute trades, place bets, or move funds. While it provides data for financial decisions (Polymarket bets), the tool itself is read-only analysis. Severity is medium because misuse could lead to poor financial decisions based on AI-selected data or manipulated queries, but the tool has no direct financial impact.

From the tool's definition Tool performs data retrieval and analysis: 'Research a Polymarket bet by pulling the relevant Pipeworx data', 'resolves the market, classifies the bet, fans out to category-specific data packs in parallel, and returns an evidence packet + simple…

Attacks that exploit this kind of access

Questions about bet_research

What does the bet_research tool do? +

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. It is categorised as a Read tool in the Mcp Semanticscholar MCP Server, which means it retrieves data without modifying state.

What parameters does bet_research accept? +

bet_research accepts 3 parameters: depth, market, include_raw. Required: market. The full parameter table on this page comes from the server's own tool schema.

How do I enforce a policy on bet_research? +

Register the Mcp Semanticscholar MCP server in PolicyLayer and add a rule for bet_research: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Mcp Semanticscholar. Nothing to install.

What risk level is bet_research? +

bet_research is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit bet_research? +

Yes. Add a rate_limit block to the bet_research rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block bet_research completely? +

Set action: deny in the PolicyLayer policy for bet_research. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides bet_research? +

bet_research is provided by the Mcp Semanticscholar MCP server (https://gateway.pipeworx.io/semanticscholar/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.