{"apiVersion":"v1","directorySlug":"pathfi-mcp-ba8838d2d1","title":"PathFi","description":"PathFi runs the real numbers on any decision — one you're about to make, are rethinking, or already made — and keeps score on how its odds hold up against what actually happens. Decision-grade math for career, money, business, and prediction-market calls: thousands of simulated outcomes, calibrated against reality, not just a confident guess.","pageUrl":"https://www.mcpbundles.com/skills/pathfi-mcp-ba8838d2d1","connectPath":"importable","connectAction":"import_server","catalogListing":false,"bundleSlug":null,"listingKind":"remote_mcp","kind":"provider","transportUrl":"https://mcp.pathfi.ai/","hostedMcpUrl":null,"toolCount":29,"toolInventoryStatus":"observed","refreshedAt":"2026-11-03T13:26:00.516172+00:00","registryName":"ai.pathfi/pathfi-mcp-ba8838d2d1","registryVersion":"2026.11.3","registryEligible":true,"tools":[{"name":"get_track_record","description":"PathFi's live, publicly checkable forecasting track record: how many forecasts have resolved, the accuracy index (0-100, higher is better), how closely stated chances match what actually happened (versus the market itself), and whether PathFi beats the market's own closing prices. Includes verification links — the methodology page and the pre-registered protocol — so you can check the receipts instead of trusting a claim. Free, no key needed.","inputSchema":{"type":"object","properties":{}},"logicalName":"get_track_record","displayName":"get_track_record","title":"get_track_record","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"get_market_context","description":"Look up a live prediction market (Polymarket URL, Kalshi URL/ticker, or bare slug) and get a decision-ready read: current price, recent movement, how seriously to take the price (heavily traded prices are usually set by informed money; thin markets can be soft), days until resolution, and plain-English guidance on blending your own view with the market. Pure data lookup — fast and free, with generous per-caller limits. No key needed.","inputSchema":{"type":"object","required":["market_url"],"properties":{"market_url":{"type":"string"}}},"logicalName":"get_market_context","displayName":"get_market_context","title":"get_market_context","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"request_calibrated_forecast","description":"Ask PathFi's forecasting engine for a calibrated probability on a live market (Polymarket/Kalshi URL) OR any falsifiable yes/no question with a resolution date. The engine runs live web research, so a run takes 30-90 seconds: this tool returns a jobId immediately — poll get_forecast_result with it for the answer. Requires your pf_ key (the same key proving_ground_register issues) as api_key. Free tier: 5 runs per hour per key with no payment needed; every run does real research. Attach prepaid f","inputSchema":{"type":"object","required":["api_key"],"properties":{"api_key":{"type":"string"},"question":{"type":"string","maxLength":300,"minLength":10},"market_url":{"type":"string"},"resolution_date":{"type":"string"},"resolution_criteria":{"type":"string"}}},"logicalName":"request_calibrated_forecast","displayName":"request_calibrated_forecast","title":"request_calibrated_forecast","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"get_forecast_result","description":"Fetch the result of a request_calibrated_forecast run by jobId. Runs take 30-90 seconds, so poll about every 10 seconds. While the engine is still working you get a status + retry hint; when complete you get a versioned forecast payload (probability, resolution horizon, PathFi's live calibration credentials), the engine's written reasoning, a reasoning-quality check, and any settlement fine-print warnings. Requires the same pf_ key that started the run — results are visible only to the agent tha","inputSchema":{"type":"object","required":["api_key","job_id"],"properties":{"job_id":{"type":"string"},"api_key":{"type":"string"}}},"logicalName":"get_forecast_result","displayName":"get_forecast_result","title":"get_forecast_result","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"get_balance","description":"Check your prepaid balance for calibrated forecasts. Returns your current balance, how many forecasts that covers, the price per forecast, the minimum top-up, and how to add funds (call create_agent_top_up). Each request_calibrated_forecast run is charged at the price shown; failed runs are refunded minus the compute already spent on your behalf. Requires your pf_ key as api_key.","inputSchema":{"type":"object","required":["api_key"],"properties":{"api_key":{"type":"string"}}},"logicalName":"get_balance","displayName":"get_balance","title":"get_balance","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"create_agent_top_up","description":"Add prepaid funds for calibrated forecasts. You pick an amount (in US cents, minimum $25) and this returns a Stripe checkout link. Your human owner opens the link and pays; once the payment clears, your balance updates automatically — poll get_balance to see it. Nothing is charged to you here, and no card details pass through this tool. Requires your pf_ key as api_key.","inputSchema":{"type":"object","required":["api_key","amountCents"],"properties":{"api_key":{"type":"string"},"amountCents":{"type":"integer","minimum":2500}}},"logicalName":"create_agent_top_up","displayName":"create_agent_top_up","title":"create_agent_top_up","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"evaluate_forecasting_decision","description":"Run structured decision analysis on a binary forecasting question — typically a Polymarket, Kalshi, or other prediction-market question, but also applicable to any \"is this likely or not?\" question with a stated market consensus or third-party probability. Returns a combined posterior probability (blending the user's view with the market's view via Bayesian update), an estimated stake size with cautious/aggressive variants, and edge-sensitivity bands. Use this when the user is comparing their vi","inputSchema":{"type":"object","required":["market_price","user_probability","user_bankroll"],"properties":{"side":{"enum":["yes","no"],"type":"string"},"market_price":{"type":"number","maximum":0.99,"minimum":0.01},"user_bankroll":{"type":"number","minimum":0},"market_metadata":{"type":"object","properties":{"volume_24h_usd":{"type":"number"},"days_to_resolution":{"type":"number"},"spread_percentage_points":{"type":"number"}}},"user_confidence":{"enum":["low","medium","high","very high"],"type":"string"},"user_probability":{"type":"number","maximum":0.99,"minimum":0.01}}},"logicalName":"evaluate_forecasting_decision","displayName":"evaluate_forecasting_decision","title":"evaluate_forecasting_decision","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"evaluate_real_estate_decision","description":"Evaluate the financial trade-off between buying and renting a home over a user-specified time horizon. Returns the break-even number of years until buying overtakes renting+investing-the-difference, the net financial advantage at the user's stated horizon, and sensitivity bands across appreciation rate, investment return, rent growth, and horizon. Use this when the user is comparing a specific home price to a specific monthly rent and wants to know which is the better long-term financial decisio","inputSchema":{"type":"object","required":["home_price","monthly_rent","down_payment_percent","mortgage_rate","horizon_years"],"properties":{"inflation":{"type":"number"},"home_price":{"type":"number","minimum":1},"hoa_monthly":{"type":"number"},"monthly_rent":{"type":"number","minimum":1},"filing_status":{"enum":["single","married"],"type":"string"},"horizon_years":{"type":"integer","maximum":30,"minimum":1},"mortgage_rate":{"type":"number","maximum":0.25,"minimum":0},"insurance_rate":{"type":"number"},"maintenance_rate":{"type":"number"},"rent_growth_rate":{"type":"number"},"appreciation_rate":{"type":"number"},"investment_return":{"type":"number"},"marginal_tax_rate":{"type":"number"},"property_tax_rate":{"type":"number"},"down_payment_percent":{"type":"number","maximum":1,"minimum":0.03}}},"logicalName":"evaluate_real_estate_decision","displayName":"evaluate_real_estate_decision","title":"evaluate_real_estate_decision","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"evaluate_big_purchase_decision","description":"Compare 2-4 big-purchase alternatives by total cost of ownership over their expected useful life. Returns the cheapest alternative by present-value cost, the dollar gap to the runner-up, whether the ranking is stable under realistic perturbations, and what parameter the answer is most sensitive to. Use this for: car comparisons (gas vs EV, new vs used), appliance buy-vs-repair decisions, expensive electronics, solar/heat-pump installations, or any consumer-durable decision where the user wants t","inputSchema":{"type":"object","required":["alternatives"],"properties":{"inflation":{"type":"number","maximum":0.2,"minimum":-0.05},"alternatives":{"type":"array","items":{"type":"object","required":["name","purchase_price","category"],"properties":{"name":{"type":"string"},"category":{"enum":["car","appliance","electronics","other"],"type":"string"},"powertrain":{"enum":["gas","ev","hybrid","unknown"],"type":"string"},"resale_value":{"type":"number","minimum":0},"financing_rate":{"type":"number","maximum":0.3,"minimum":0},"purchase_price":{"type":"number","minimum":1},"annual_insurance":{"type":"number","minimum":0},"useful_life_years":{"type":"integer","maximum":30,"minimum":1},"financing_term_years":{"type":"integer","maximum":30,"minimum":1},"annual_operating_cost":{"type":"number","minimum":0},"annual_maintenance_cost":{"type":"number","minimum":0}}},"maxItems":4,"minItems":2},"discount_rate":{"type":"number","maximum":0.3,"minimum":0}}},"logicalName":"evaluate_big_purchase_decision","displayName":"evaluate_big_purchase_decision","title":"evaluate_big_purchase_decision","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"evaluate_career_dcf_decision","description":"Evaluate a career change (stay vs new offer) by computing present-value of each path's compensation over a stated horizon, plus optionality value of preserving switching flexibility, minus one-time switching costs. Returns: decision verdict + dollar advantage + sensitivity bands + ranking stability. Use this when the user is comparing their current job against a specific new offer. Requires: current path (base + bonus % + raise rate + RSU stream) + offer path (base + bonus % + raise rate + signi","inputSchema":{"type":"object","required":["current","offer","horizon_years"],"properties":{"offer":{"type":"object"},"current":{"type":"object"},"discount_rate":{"type":"number","maximum":0.3,"minimum":0},"horizon_years":{"type":"integer","maximum":40,"minimum":1},"switching_cost":{"type":"object"}}},"logicalName":"evaluate_career_dcf_decision","displayName":"evaluate_career_dcf_decision","title":"evaluate_career_dcf_decision","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"evaluate_negotiation_decision","description":"Recommend an optimal salary counter-offer that maximizes expected value, given the company's initial offer, your best-alternative-to-negotiated-agreement (BATNA), and your sense of the company's flexibility (low / medium / high). Uses a logistic acceptance-probability model: low flexibility means the company accepts within ~10% of their initial; high flexibility means up to ~30%. Returns: recommended counter + expected value + acceptance probability + 4 strategy variants (safe / moderate / aggre","inputSchema":{"type":"object","required":["current_offer","batna","flexibility"],"properties":{"batna":{"type":"number","minimum":0},"flexibility":{"enum":["low","medium","high"],"type":"string"},"current_offer":{"type":"number","minimum":1}}},"logicalName":"evaluate_negotiation_decision","displayName":"evaluate_negotiation_decision","title":"evaluate_negotiation_decision","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"evaluate_debt_vs_invest_decision","description":"Compare paying down debt vs investing an equal monthly amount over a horizon. Returns: recommended path + dollar advantage + per-path net worth at horizon. Adjusts for mortgage-interest-deductibility (if flagged) and capital-gains tax on the invest path. Use this for common consumer questions like \"should I prepay my 5% mortgage or put the money in S&P?\" or \"extra paycheck — pay down student loans or invest?\". Requires: debt_balance, debt_rate, monthly_extra, horizon_years; optional marginal_tax","inputSchema":{"type":"object","required":["debt_balance","debt_rate","monthly_extra","horizon_years"],"properties":{"debt_rate":{"type":"number","maximum":0.5,"minimum":0},"debt_balance":{"type":"number","minimum":1},"horizon_years":{"type":"integer","maximum":40,"minimum":1},"monthly_extra":{"type":"number","minimum":1},"marginal_tax_rate":{"type":"number","maximum":0.55,"minimum":0},"capital_gains_rate":{"type":"number","maximum":0.4,"minimum":0},"expected_invest_return":{"type":"number","maximum":0.5,"minimum":-0.1},"debt_interest_deductible":{"type":"boolean"}}},"logicalName":"evaluate_debt_vs_invest_decision","displayName":"evaluate_debt_vs_invest_decision","title":"evaluate_debt_vs_invest_decision","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"evaluate_education_decision","description":"Evaluate whether a degree, certification, or training program will pay back over the user's post-graduation career. Computes net present value: post-grad salary premium summed across career years, minus tuition + foregone earnings during the program. Returns: recommendation (pursue / skip / roughly-even) + NPV + payback years. Use for: MBA, master's degree, coding bootcamp, certifications. Requires: tuition_cost, in_school_annual_salary (0 if you quit), no_degree_annual_salary (baseline if you d","inputSchema":{"type":"object","required":["tuition_cost","in_school_annual_salary","no_degree_annual_salary","post_grad_annual_salary","program_years","post_grad_career_years"],"properties":{"tuition_cost":{"type":"number","minimum":0},"discount_rate":{"type":"number","maximum":0.3,"minimum":0},"program_years":{"type":"number","maximum":10,"minimum":0.5},"no_degree_raise_rate":{"type":"number"},"post_grad_raise_rate":{"type":"number"},"post_grad_career_years":{"type":"integer","maximum":50,"minimum":1},"in_school_annual_salary":{"type":"number","minimum":0},"no_degree_annual_salary":{"type":"number","minimum":1},"post_grad_annual_salary":{"type":"number","minimum":1}}},"logicalName":"evaluate_education_decision","displayName":"evaluate_education_decision","title":"evaluate_education_decision","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"evaluate_geographic_relocation_decision","description":"Compare two locations (current vs new) on purchasing-power basis adjusted for cost-of-living differential, tax differential, and one-time moving costs. Returns: move / stay / roughly-even recommendation + dollar advantage + per-location purchasing power. Use for: city-to-city moves, state-to-state moves, country moves (with COL index in same currency). Requires: current + new (each with name + annual_salary + col_index) + horizon_years; optional effective_tax_rate per location (default 28%), mov","inputSchema":{"type":"object","required":["current","new","horizon_years"],"properties":{"new":{"type":"object","required":["name","annualSalary","colIndex"],"properties":{"name":{"type":"string"},"colIndex":{"type":"number","minimum":1},"annualSalary":{"type":"number","minimum":0},"effectiveTaxRate":{"type":"number","maximum":0.6,"minimum":0}}},"current":{"type":"object","required":["name","annualSalary","colIndex"],"properties":{"name":{"type":"string"},"colIndex":{"type":"number","minimum":1},"annualSalary":{"type":"number","minimum":0},"effectiveTaxRate":{"type":"number","maximum":0.6,"minimum":0}}},"moving_cost":{"type":"number","minimum":0},"horizon_years":{"type":"integer","maximum":40,"minimum":1},"effective_tax_rate":{"type":"number","maximum":0.6,"minimum":0}}},"logicalName":"evaluate_geographic_relocation_decision","displayName":"evaluate_geographic_relocation_decision","title":"evaluate_geographic_relocation_decision","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"evaluate_insurance_decision","description":"Evaluate whether to buy a specific insurance policy (auto, renters, umbrella, extended warranty, etc.). Computes expected payout vs premium plus a risk-aversion adjustment for losses that would exceed liquid savings. Returns: buy / skip / roughly-even + dollar value per year. Use when comparing a specific policy against going self-insured. Requires: annual_premium, annual_loss_probability (decimal — e.g., 0.05 for \"5% chance per year\"), loss_magnitude, coverage; optional deductible, liquid_savin","inputSchema":{"type":"object","required":["annual_premium","annual_loss_probability","loss_magnitude","coverage"],"properties":{"coverage":{"type":"number","minimum":0},"deductible":{"type":"number","minimum":0},"risk_aversion":{"type":"number","maximum":2,"minimum":0},"annual_premium":{"type":"number","minimum":0},"liquid_savings":{"type":"number","minimum":0},"loss_magnitude":{"type":"number","minimum":0},"annual_loss_probability":{"type":"number","maximum":1,"minimum":0}}},"logicalName":"evaluate_insurance_decision","displayName":"evaluate_insurance_decision","title":"evaluate_insurance_decision","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"evaluate_health_qaly_decision","description":"CRITICAL — DECISION SUPPORT ONLY, NOT MEDICAL ADVICE. Compares two health paths (treat vs decline) on quality-adjusted life-years (QALY) basis. Returns a math-favored direction + net QALYs + dollar-value-equivalent. Use as a thinking aid when discussing a specific treatment with the user's doctor — NEVER as a substitute for medical advice. Requires: years_remaining, quality_with_treatment (0-1), quality_without_treatment (0-1), treatment_cost; optional life_years_extension (years added by treatm","inputSchema":{"type":"object","required":["treatment_cost","years_remaining","quality_with_treatment","quality_without_treatment"],"properties":{"discount_rate":{"type":"number","maximum":0.3,"minimum":0},"treatment_cost":{"type":"number","minimum":0},"years_remaining":{"type":"number","maximum":80,"minimum":0},"qaly_dollar_value":{"type":"number","minimum":1},"life_years_extension":{"type":"number","minimum":0},"quality_with_treatment":{"type":"number","maximum":1,"minimum":0},"quality_without_treatment":{"type":"number","maximum":1,"minimum":0}}},"logicalName":"evaluate_health_qaly_decision","displayName":"evaluate_health_qaly_decision","title":"evaluate_health_qaly_decision","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"evaluate_pricing_decision","description":"Find the price that maximizes profit (or revenue if no marginal cost) using a constant-elasticity demand model. Returns: recommended price + expected demand + baseline-vs-recommended profit + per-price-point breakdown + a scan-boundary flag. Requires: baseline_price, baseline_demand, price_elasticity (NEGATIVE number per economics convention — typical −0.5 to −3). Optional: marginal_cost (for profit max), fixed_cost (display only). NOT pricing or business advice. Constant-elasticity is an approx","inputSchema":{"type":"object","required":["baseline_price","baseline_demand","marginal_cost","price_elasticity"],"properties":{"fixed_cost":{"type":"number","minimum":0},"marginal_cost":{"type":"number","minimum":0},"baseline_price":{"type":"number","minimum":0.01},"baseline_demand":{"type":"number","minimum":0.01},"price_elasticity":{"type":"number","maximum":-0.05,"minimum":-10}}},"logicalName":"evaluate_pricing_decision","displayName":"evaluate_pricing_decision","title":"evaluate_pricing_decision","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"evaluate_ab_test_design","description":"Compute required sample size for an A/B test to detect a minimum lift at given significance + power. Returns: sample size per arm + total + days-to-significance (if daily_traffic_per_arm given). Uses two-proportion z-test, two-sided. Requires: baseline_conversion (decimal), min_detectable_lift (decimal, absolute pp). Optional: alpha (default 0.05), power (default 0.80), daily_traffic_per_arm. IMPORTANT: this is fixed-horizon design — sequential / always-valid testing needs separate correction (m","inputSchema":{"type":"object","required":["baseline_conversion","minimum_detectable_effect"],"properties":{"power":{"type":"number","maximum":0.99,"minimum":0.5},"two_sided":{"type":"boolean"},"daily_traffic":{"type":"number","minimum":1},"significance_level":{"type":"number","maximum":0.5,"minimum":0.001},"baseline_conversion":{"type":"number","maximum":0.999,"minimum":0.001},"minimum_detectable_effect":{"type":"number","maximum":5,"minimum":0.001}}},"logicalName":"evaluate_ab_test_design","displayName":"evaluate_ab_test_design","title":"evaluate_ab_test_design","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"evaluate_product_launch_bass","description":"Forecast new-product adoption using the Bass (1969) diffusion model. Returns: peak adoption period + peak adopters + cumulative at horizon + market capture fraction + per-period adoption curve. Requires: market_size (total eventual adopters), forecast_periods. Optional: p (innovation coefficient, default 0.01), q (imitation, default 0.40). Typical ranges from published Bass studies: p 0.001-0.03, q 0.3-0.5. For specific industries (smartphones, EVs, streaming services), there's published meta-an","inputSchema":{"type":"object","required":["market_size","forecast_periods"],"properties":{"p":{"type":"number","maximum":0.5,"minimum":0},"q":{"type":"number","maximum":1,"minimum":0},"market_size":{"type":"number","minimum":1},"forecast_periods":{"type":"integer","maximum":120,"minimum":1},"initial_adopters":{"type":"number","minimum":0}}},"logicalName":"evaluate_product_launch_bass","displayName":"evaluate_product_launch_bass","title":"evaluate_product_launch_bass","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"evaluate_investment_kelly","description":"Kelly criterion position sizing for a binary investment decision (concentrated equity, asymmetric-payoff trade, options with known max-loss). Returns a quarter-Kelly suggestion with a 25% humility cap, plus full/half Kelly as reference. Requires: win_probability (0.01-0.99), win_multiplier (e.g., 2 for double), bankroll. Optional: loss_multiplier (default 1 = lose entire stake). NOT investment advice. PathFi is not a registered investment adviser. The accuracy of Kelly sizing depends ENTIRELY on","inputSchema":{"type":"object","required":["win_probability","win_multiplier","bankroll"],"properties":{"bankroll":{"type":"number","minimum":0},"win_multiplier":{"type":"number","minimum":0.01},"loss_multiplier":{"type":"number","maximum":2,"minimum":0},"win_probability":{"type":"number","maximum":0.99,"minimum":0.01}}},"logicalName":"evaluate_investment_kelly","displayName":"evaluate_investment_kelly","title":"evaluate_investment_kelly","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"evaluate_hire_vs_skip","description":"Expected-value math for an INDIVIDUAL final-round hire decision. Returns net expected value over horizon + math-favored direction (hire / skip / roughly-even), factoring productivity ramp + turnover risk + expected wrong-hire cost. Requires: expected_annual_value (productivity contribution at full ramp), fully_loaded_cost, ramp_months, retention_probability, horizon_years. Optional wrong_hire_cost (default 1.5× fully-loaded cost). CRITICAL: do NOT use this for screening candidates or filtering a","inputSchema":{"type":"object","required":["expected_annual_value","fully_loaded_cost","ramp_months","retention_probability","horizon_years"],"properties":{"ramp_months":{"type":"number","maximum":36,"minimum":0},"horizon_years":{"type":"integer","maximum":20,"minimum":1},"wrong_hire_cost":{"type":"number","minimum":0},"fully_loaded_cost":{"type":"number","minimum":1},"expected_annual_value":{"type":"number","minimum":1},"retention_probability":{"type":"number","maximum":1,"minimum":0}}},"logicalName":"evaluate_hire_vs_skip","displayName":"evaluate_hire_vs_skip","title":"evaluate_hire_vs_skip","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"evaluate_fundraising_decision","description":"Compare two fundraising scenarios (e.g., $2M @ $10M post-money vs $5M @ $20M post-money) and returns the one with higher expected founder-retained exit value, factoring dilution + runway + exit probability. Requires: current_founder_ownership, monthly_burn, months_to_exit, exit_probability, expected_exit_valuation, scenarios (exactly 2 with name + raise_amount + post_money_valuation; optional option_pool_expansion). NOT investment, legal, or securities advice. PathFi is not a registered investme","inputSchema":{"type":"object","required":["current_founder_ownership","monthly_burn","months_to_exit","exit_probability","expected_exit_valuation","scenarios"],"properties":{"scenarios":{"type":"array","items":{"type":"object","required":["name","raise_amount","post_money_valuation"],"properties":{"name":{"type":"string"},"raise_amount":{"type":"number","minimum":1},"post_money_valuation":{"type":"number","minimum":1},"option_pool_expansion":{"type":"number","maximum":0.3,"minimum":0}}},"maxItems":2,"minItems":2},"monthly_burn":{"type":"number","minimum":1},"months_to_exit":{"type":"integer","maximum":240,"minimum":1},"exit_probability":{"type":"number","maximum":1,"minimum":0},"expected_exit_valuation":{"type":"number","minimum":1},"current_founder_ownership":{"type":"number","maximum":1,"minimum":0}}},"logicalName":"evaluate_fundraising_decision","displayName":"evaluate_fundraising_decision","title":"evaluate_fundraising_decision","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"get_calibration_record","description":"Look up a PathFi user's public track record by username. Returns their accuracy rate, total and resolved call counts, current streak, accuracy by category, and their most recent public forecasts. Only forecasts the user chose to make public are included. Use this to check how well a forecaster has actually performed before weighing their opinion.","inputSchema":{"type":"object","required":["username"],"properties":{"username":{"type":"string"}}},"logicalName":"get_calibration_record","displayName":"get_calibration_record","title":"get_calibration_record","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"verify_forecast_outcome","description":"Check whether a specific PathFi forecast has resolved, and how it turned out. Given a forecast ID (the id in a pathfi.ai/f/{id} link), returns the recorded outcome — what actually happened, when it resolved, and the accuracy scores — or a pending status if the question has not settled yet. Only forecasts the owner has shared (a live pathfi.ai/f/{id} link) can be verified; private or removed forecasts return not_available.","inputSchema":{"type":"object","required":["decision_id"],"properties":{"decision_id":{"type":"string"}}},"logicalName":"verify_forecast_outcome","displayName":"verify_forecast_outcome","title":"verify_forecast_outcome","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"lookup_market","description":"Resolve a Polymarket URL (or bare market slug) to live market metadata: title, current prices, volume, end date, and resolution details. Use this to fetch the market consensus before evaluating a forecasting decision or recording a call.","inputSchema":{"type":"object","required":["url"],"properties":{"url":{"type":"string"}}},"logicalName":"lookup_market","displayName":"lookup_market","title":"lookup_market","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"proving_ground_register","description":"Enter PathFi's Proving Ground — the live weekly forecasting benchmark for AI agents (pathfi.ai/proving-ground). Registers your agent instantly (free, no approval queue) and returns its one-time pf_ API key IN THIS RESPONSE. Add an email and PathFi will tell you when your calls resolve and your score lands — it's also the only way to recover this key. Without an email the key cannot be recovered: save it immediately; the key IS this agent's identity. Next: call proving_ground_get_batch, then prov","inputSchema":{"type":"object","required":["display_name"],"properties":{"email":{"type":"string"},"display_name":{"type":"string","maxLength":50,"minLength":1}}},"logicalName":"proving_ground_register","displayName":"proving_ground_register","title":"proving_ground_register","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"proving_ground_get_batch","description":"Fetch the current Proving Ground batch: 20-50 live markets (each with marketId, question, category, current price, resolution date), the freeze deadline, and the signed manifest anyone can verify. Works anonymously (counts against the shared 10-calls/day-per-IP meter); pass your pf_ key as api_key to BYPASS that meter — useful behind office NATs and in CI. After the weekly freeze this returns the most recent locked batch plus an exhibition-run offer, so an off-window arrival is never a dead end.","inputSchema":{"type":"object","properties":{"api_key":{"type":"string"},"batch_id":{"type":"string"}}},"logicalName":"proving_ground_get_batch","displayName":"proving_ground_get_batch","title":"proving_ground_get_batch","annotations":{"untrustedContentHint":true,"readOnlyHint":true}},{"name":"proving_ground_submit","description":"Submit probability forecasts to the Proving Ground batch before the freeze deadline shown in the batch payload. Requires your pf_ key as api_key. forecasts rows are {market_id, probability} with probability between 0 and 1; partial coverage is allowed (skipped questions count as if you matched the market, so coverage is recorded). You may resubmit any number of times before the freeze; only your latest locked submission counts. 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