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Audits OHLC bars for the defects that silently invalidate a backtest. Free and indicative feeds often SYNTHESISE the bar's open instead of observing it, which pins |open-close|/range near zero — measured on the same FX pairs, a free feed reads 0.020 against a real broker's 0.464. Anything depending on the open (gap trades, overnight holds, next-bar entries) is invalid on such data. Also detects frozen zero-range bars, duplicate and out-of-order timestamps, and bars whose high/low do not bracket open/close. Returns BROKER-GRADE, SUSPECT or INDICATIVE with the numbers behind it. Use this BEFORE trusting any backtest built on the data.","inputSchema":{"properties":{"bars":{"description":"OHLC bars keyed by instrument. Each bar needs time (ISO-8601), open, high, low, close. 50-3000 bars, one instrument, for the free check.","type":"object"}},"required":["bars"],"type":"object"},"name":"check_price_data"},{"description":"Your t-stat assumes independence; your trades cluster. 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Real relationship, or two autocorrelated series drifting together RUNS NOW: served from a daily free allowance (250 left today), then $0.31 USDC on Base via x402. No account, no API key. Example request: {\"x\": [0.4, -1.1, 0.8, 1.2, -0.6, 0.3, -0.2, 0.9, -1.4, 0.7, 0.5, -0.3], \"y\": [0.3, -0.9, 0.6, 1.0, -0.7, 0.1, -0.4, 0.8, -1.2, 0.5, 0.6, -0.1]}","inputSchema":{"examples":[{"x":[0.4,-1.1,0.8,1.2,-0.6,0.3,-0.2,0.9,-1.4,0.7,0.5,-0.3],"y":[0.3,-0.9,0.6,1.0,-0.7,0.1,-0.4,0.8,-1.2,0.5,0.6,-0.1]}],"properties":{"x":{"description":"example: [0.4, -1.1, 0.8, 1.2, -0.6, 0.3, -0.2, 0.9, -1.4, 0.7, 0.5, -0.3]"},"y":{"description":"example: [0.3, -0.9, 0.6, 1.0, -0.7, 0.1, -0.4, 0.8, -1.2, 0.5, 0.6, -0.1]"}},"required":["x","y"],"type":"object"},"name":"correlation"},{"description":"Is this Sharpe real, or the best of N tries? Deflated for the search you ran. Is this Sharpe real, or the best of N tries? Deflated for the search you ran RUNS NOW: served from a daily free allowance (250 left today), then $0.31 USDC on Base via x402. No account, no API key. Example request: {\"returns\": [0.012, -0.004, 0.021, 0.003, -0.011, 0.017, 0.006, -0.008, 0.014, 0.002, -0.006, 0.019, 0.001, -0.013, 0.009, 0.004], \"n_trials\": 40}","inputSchema":{"examples":[{"n_trials":40,"returns":[0.012,-0.004,0.021,0.003,-0.011,0.017,0.006,-0.008,0.014,0.002,-0.006,0.019,0.001,-0.013,0.009,0.004]}],"properties":{"n_trials":{"description":"example: 40"},"returns":{"description":"example: [0.012, -0.004, 0.021, 0.003, -0.011, 0.017, 0.006, -0.008, 0.014, 0.002, -0.006, 0.019, 0.001, -0.013, 0.009, 0.004]"}},"required":["n_trials","returns"],"type":"object"},"name":"deflatedsharpe"},{"description":"Is this drawdown a broken strategy or a normal bad patch, vs your own shuffled returns. Is this drawdown a broken strategy or a normal bad patch, vs your own shuffled returns RUNS NOW: served from a daily free allowance (250 left today), then $0.31 USDC on Base via x402. No account, no API key. Example request: {\"returns\": [0.9, -1.0, 0.4, -1.0, 1.6, -1.0, 0.7, 0.3, -1.0, 2.1, -1.0, -1.0, 0.8, 1.4, -1.0, 0.5, -1.0, 1.9, 0.2, -1.0, 1.1, -1.0, 0.6, 1.3]}","inputSchema":{"examples":[{"returns":[0.9,-1.0,0.4,-1.0,1.6,-1.0,0.7,0.3,-1.0,2.1,-1.0,-1.0,0.8,1.4,-1.0,0.5,-1.0,1.9,0.2,-1.0,1.1,-1.0,0.6,1.3]}],"properties":{"returns":{"description":"example: [0.9, -1.0, 0.4, -1.0, 1.6, -1.0, 0.7, 0.3, -1.0, 2.1, -1.0, -1.0, 0.8, 1.4, -1.0, 0.5, -1.0, 1.9, 0.2, -1.0, 1.1, -1.0,"}},"required":["returns"],"type":"object"},"name":"drawdown"},{"description":"Verify a claim against 2-4 source URLs: cited passages, cross-source agreement, verdict: consistent | conflicting | insufficient_evidence. Evidence, not truth. Use when your agent needs to verify a claim against independent sources before acting on it. Give a claim plus 2-4 source URLs; each source is extracted (three-fetch cross-check, confidence-scored), the checkable figures are compared across sources, and you get the cited passages plus a verdict: con PAID: $0.03 USDC on Base via x402. Call it to receive the payment challenge. Example request: {\"claim\": \"Acme Corp revenue was $4.2 billion in 2025\", \"urls\": [\"https://example.com\", \"https://example.org\"]}","inputSchema":{"examples":[{"claim":"Acme Corp revenue was $4.2 billion in 2025","urls":["https://example.com","https://example.org"]}],"properties":{"claim":{"description":"example: \"Acme Corp revenue was $4.2 billion in 2025\""},"urls":{"description":"example: [\"https://example.com\", \"https://example.org\"]"}},"required":["claim","urls"],"type":"object"},"name":"factcheck"},{"description":"Triage up to 1,000 backtests in one call: the best-of-N multiple-testing selection correction over every return series — $0.01 per series, survivors ranked best-first. Use when your agent has a FARM of backtests, strategies, or research results — dozens to a thousand — and needs triage, not one-by-one verdicts. One call runs the selection correction over every series: each gets NO (killed), PROVISIONAL (survived this gate), or ERROR (malformed). Results ranked sur PAID: $0.01 USDC on Base via x402. Call it to receive the payment challenge. Example request: {\"series\": [{\"returns\": [0.8, -0.3, 1.2, 0.5, -0.6, 0.9, 0.4, -0.2, 1.1, 0.7, -0.4, 0.6, 0.3, -0.5, 1.0, 0.8, -0.1, 0.5, 0.9, -0.3], \"n_tested\": 20, \"label\": \"weak\"}, {\"returns\": [2.0, 2.1, 1.9, 2.2, 2.0, 2.1, 1.8, 2.0, ","inputSchema":{"examples":[{"series":[{"label":"weak","n_tested":20,"returns":[0.8,-0.3,1.2,0.5,-0.6,0.9,0.4,-0.2,1.1,0.7,-0.4,0.6,0.3,-0.5,1.0,0.8,-0.1,0.5,0.9,-0.3]},{"label":"strong","n_tested":1,"returns":[2.0,2.1,1.9,2.2,2.0,2.1,1.8,2.0,2.1,1.9,2.0,2.2,1.9,2.1,2.0,1.8,2.2,2.0,2.1,1.9]}]}],"properties":{"series":{"description":"example: [{\"returns\": [0.8, -0.3, 1.2, 0.5, -0.6, 0.9, 0.4, -0.2, 1.1, 0.7, -0.4, 0.6, 0.3, -0.5, 1.0, 0.8, -0.1, 0.5, 0.9, -0.3]"}},"required":["series"],"type":"object"},"name":"judge-batch"},{"description":"Is this backtest, strategy or DeFi result real, or the luckiest of N tries? Three gates in one verdict — multiple-testing selection, deflated Sharpe, date-clustered t — built for trading agents. Use when your agent has a backtest, a research result, or any return series that looks good — and needs to know whether it is real or the luckiest of N tries before spending money on it. One call runs the three cheapest high-signal gates: selection correction (family-wise p across your declared n_te PAID: $0.05 USDC on Base via x402. Call it to receive the payment challenge. Example request: {\"returns\": [0.8, -0.3, 1.2, 0.5, -0.6, 0.9, 0.4, -0.2, 1.1, 0.7, -0.4, 0.6, 0.3, -0.5, 1.0, 0.8, -0.1, 0.5, 0.9, -0.3], \"n_tested\": 20, \"dates\": [\"2024-06-03\", \"2024-06-03\", \"2024-06-04\", \"2024-06-04\", \"2024-06-05\", \"20","inputSchema":{"examples":[{"dates":["2024-06-03","2024-06-03","2024-06-04","2024-06-04","2024-06-05","2024-06-05","2024-06-06","2024-06-06","2024-06-07","2024-06-10","2024-06-10","2024-06-11","2024-06-11","2024-06-12","2024-06-12","2024-06-13","2024-06-13","2024-06-14","2024-06-14","2024-06-17"],"n_tested":20,"returns":[0.8,-0.3,1.2,0.5,-0.6,0.9,0.4,-0.2,1.1,0.7,-0.4,0.6,0.3,-0.5,1.0,0.8,-0.1,0.5,0.9,-0.3]}],"properties":{"dates":{"description":"example: [\"2024-06-03\", \"2024-06-03\", \"2024-06-04\", \"2024-06-04\", \"2024-06-05\", \"2024-06-05\", \"2024-06-06\", \"2024-06-06\", \"2024-0"},"n_tested":{"description":"example: 20"},"returns":{"description":"example: [0.8, -0.3, 1.2, 0.5, -0.6, 0.9, 0.4, -0.2, 1.1, 0.7, -0.4, 0.6, 0.3, -0.5, 1.0, 0.8, -0.1, 0.5, 0.9, -0.3]"}},"required":["dates","n_tested","returns"],"type":"object"},"name":"judge-lite"},{"description":"Is this backtest winner real, or just the best of N tries? The multiple-testing selection correction (family-wise p) in one $0.01 call — verdict NO or PROVISIONAL. Use when your agent has a backtest winner, a research result, or any return series that looks good — and needs the cheapest possible check on whether it is real or the luckiest of N tries. One call runs the selection correction: the family-wise p of the series' own t across your declared n_tested. V PAID: $0.01 USDC on Base via x402. Call it to receive the payment challenge. Example request: {\"returns\": [0.8, -0.3, 1.2, 0.5, -0.6, 0.9, 0.4, -0.2, 1.1, 0.7, -0.4, 0.6, 0.3, -0.5, 1.0, 0.8, -0.1, 0.5, 0.9, -0.3], \"n_tested\": 20}","inputSchema":{"examples":[{"n_tested":20,"returns":[0.8,-0.3,1.2,0.5,-0.6,0.9,0.4,-0.2,1.1,0.7,-0.4,0.6,0.3,-0.5,1.0,0.8,-0.1,0.5,0.9,-0.3]}],"properties":{"n_tested":{"description":"example: 20"},"returns":{"description":"example: [0.8, -0.3, 1.2, 0.5, -0.6, 0.9, 0.4, -0.2, 1.1, 0.7, -0.4, 0.6, 0.3, -0.5, 1.0, 0.8, -0.1, 0.5, 0.9, -0.3]"}},"required":["n_tested","returns"],"type":"object"},"name":"judge-nano"},{"description":"You tested N strategies and kept the best; is it noise. Use when you searched many strategies or parameter sets and kept the best one. Quoting that winner's solo p-value is the most common way a backtest lies. Give the number of things you tested and the winner's t-statistic; returns the family-wise p-value, the t you actually needed, and the t that pure RUNS NOW: served from a daily free allowance (250 left today), then $0.20 USDC on Base via x402. No account, no API key. Example request: {\"n_tested\": 1000, \"best_t\": 3.0}","inputSchema":{"examples":[{"best_t":3.0,"n_tested":1000}],"properties":{"best_t":{"description":"example: 3.0"},"n_tested":{"description":"example: 1000"}},"required":["best_t","n_tested"],"type":"object"},"name":"multipletest"},{"description":"11-gate adjudication of a trading signal series. Use when you have OHLC bars and a -1/0/1 signal series and need to know whether a backtest result is real or an artefact. Applies 11 gates most backtests fail: matched random-entry drift control, date-clustered standard errors, entry at the next bar's open, events-not-fills, absolute (not just exces PAID: $5.00 USDC on Base via x402. Call it to receive the payment challenge. 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Example request: {\"url\": \"https://example.com\", \"extraction_target\": \"the page title and the body text\"}","inputSchema":{"examples":[{"extraction_target":"the page title and the body text","url":"https://example.com"}],"properties":{"extraction_target":{"description":"example: \"the page title and the body text\""},"url":{"description":"example: \"https://example.com\""}},"required":["extraction_target","url"],"type":"object"},"name":"v1_extract"}]},"body_sha256":"818a4a20dcea7d1f87f5f5c5e65247bc0a7f47c649cc4629cc7ff70b5ec97185","prev_id":null,"leaf":"0x0b07ec2fe5c895427ad144c4ffee74dd19dabda8b67160fb3ea754b869720dfa","leaf_text":"{\"body\":\"818a4a20dcea7d1f87f5f5c5e65247bc0a7f47c649cc4629cc7ff70b5ec97185\",\"id\":571133,\"kind\":\"mcp.tools\",\"method\":\"probe.mcp/1\",\"observed_us\":1793140064348462,\"prev\":null,\"subject\":\"9aaade35-f03e-40e3-8604-442c18aa085a\",\"subject_kind\":\"endpoint\"}","day":{"day":"2026-10-27","leaves":273461,"root":"0xdd77e5e99da32c3f154180d63e3c3e6fe11e896a2c6896412ef6b5c4bcf7e795","chained":"0x23bb4dfd09f55c15ac3962f53578e02c6114d5bfd9bb451cf8794fb6e2817ef4","anchor_chain":"eip155:5042002","anchor_tx":"0xb2ebd83aafeb765a0ce66e76aefb133ed06606c98038fd56191e2bcd1dcb77b4","anchor_block":69414147},"proof":["0x0b08369a81a5b763473680fe83ca7affa9ab9051cc9c8c2b28f3e005b786f9fe","0x7a343cf261c0f9cc9c4e758959b7edee7c09eb4204ad7e7bd90c06054c55a8ad","0xcd9817bb63e83fc7dd01c55c809775b06f3f75e08b8a6210f6bc92340523ba92","0x16e2890cfd21413bb75bb627a0f9f60f9e4b607ac0855b11f58858d2d17b4583","0xfe6fefbda0daa89dc3905d9def6b36492d4d550d2a7e86663b8759658543ee51","0x4b1d4201e04ac84efb9f4c7eec7132674f9e1579826a0c268cb0abd8edf80e3b","0xd4e714cccbf060e52e46237be87f686aaa7b99577936f93b35c92f87d397d398","0x6f9b40bc89ed3b360ab45071043bc15b6f86feb9d878dd00c01a75f3908c195a","0x0b71566d473211a0e78fa32d572bb046d75715d9d3e9faf6aaaf5336042215c5","0xb6a4450b4a9940eedd2b07861f4e3bb98cbc22cfb025b648245e62a4512ea7b3","0xab9a818db8b8604daa1d300b5336a7ecdc718ee9de04e347884c3c2ff6e455d3","0x7189e38499a0d36b6f3d244260e4b1d8d20ec79407f4be030531131834122360","0x74ffd6144e8464591ea30392b981f197426cfd361178829fb8a66915e2f727b0","0x3e0ec8a2a8a954695a9b53a4a48a7656d7e3fd6a8795c73b49e2ef05b201ec3f","0x2cf27e8a181d60d0dd27c186c2eddfcbb4e8fa71cf66feeb55a4d730e15ee8c4","0x518b93854db03dc9fc938883abf30da6eaae5ae0d4336903968fd32a7df8c71b","0x22c1fb9487ee0b9732fa017a0cc321eb27a60c08450d112f755089796e12cb1c","0xa20d48798a34aba12eb9c7297722982d910f06c148f5e27ac59307422964a585","0x9fc354c0ea3efc3f1b6548bb77ea902a50f2c9647647ea61592efce2bd795699"]}