{"call_id":"call_b77cfb7a","run_id":"20261015T171401Z-suite-tool_selection-baseline-a1426d/tool_selection.rdd_manipulated.test.s1000/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.baseline","version":"","code_version":"2925aeab2262","inputs":{"tools":["calc__annuity","calc__breakeven","calc__combine","calc__discount","calc__fiscal_impact","core__atkinson","core__equivalent_variation","core__fgt","core__gini","core__indirect_incidence","core__kakwani","core__mvpf","core__palma","core__quantile_shares","core__reynolds_smolensky","core__scale_effect","core__theil","core__welfare_change","data__cite","data__describe","data__siconfi","data__sidra","evidence__pool_dl","infer__ate","infer__density_test","infer__did_2x2","infer__did_staggered","infer__event_study","infer__fe","infer__fisher_exact","infer__iv_2sls","infer__linear_hypothesis","infer__mde","infer__neyman","infer__ols","infer__power","infer__randomization_inference","infer__rdd","infer__sample_size","infer__twfe","infer__wald_late","submit_answer"],"exemplars":[]},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:14:02.216851Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"rules-1","prompt":"# Role\nYou are MAGI's economist. A municipal analyst in Brazil asks a question; you answer it by choosing registered tools and their arguments. The tools compute every number. Your job is to recognise how the data were generated, pick the tool whose design matches, read what it reports, and say what the result means for the analyst.\n\n# Rules\n1. You never compute a number. Every number in your answer must have been stated by a tool call in this run (a figure, its standard error or interval, a diagnostic, a table cell) or written in the question. No mental arithmetic: for a ratio, difference, sum, product, percent change or share of figures already computed, call calc.combine; to restate a log-point coefficient as a percent or an effect in standard deviations, call core.scale_effect.\n2. Before estimating, work out the design: how units came to be treated (a lottery, an offer with partial take-up, a threshold of a running variable, a policy that started on one date or on different dates, self-selection), what a row is, and which columns hold the outcome, treatment, unit, time, running variable, instrument, cluster and survey weight. Call data.describe when the columns are unclear. Then choose the tool whose \"Use when\" matches and heed its \"Avoid when\".\n3. Read the diagnostics, warnings and \"Check after\" items a result returns before you trust a figure. If a diagnostic says the design fails — a weak instrument, sorting at the cutoff, few clusters, non-parallel pre-trends — say so plainly and do not present the estimate as causal.\n4. If no tool fits the design, or the data cannot identify what is asked, decline and say what is missing. Never invent a design, a column or a number.\n5. A tool that errors or declines returns its reason; fix the arguments and call again, or choose another tool.\n6. Before each tool call, write one sentence on why this tool and these arguments fit the design. It is recorded as your rationale.\n7. Finish with submit_answer. Write the answer in the language of the question (Portuguese: decimal comma), short and direct: the estimate with its uncertainty, the design that identifies it, and what could undermine it. List in figure_refs every figure you quote, as '<call_id>:<key>' (each tool result carries its call_id), and put diagnostics that limit the conclusion in caveats. Write numbers as the tools stated them; do not quote rule-of-thumb thresholds as numbers.\n\n# Datasets\nPass a dataset's id as the `dataset` argument.\n{\n \"id\": \"dados\",\n \"rows\": 3000,\n \"description\": \"Uma linha por partido numa eleição municipal. margem: margem de votos do partido (positiva quando venceu, negativa quando perdeu; zero é o empate); vencedor: 1 se o partido venceu a eleição; reeleito: índice de desempenho do partido na eleição seguinte (maior é melhor).\",\n \"columns\": {\n  \"margem\": \"float64\",\n  \"reeleito\": \"float64\",\n  \"vencedor\": \"int64\"\n }\n}\n\n# Question\nHá suspeita de que partidos que perderiam por pouco conseguiram virar o resultado na apuração. A comparação entre quem venceu e quem perdeu por pouco ainda é confiável?","response":"Suspeita de manipulação da variável de corte: teste de densidade.\ninfer__density_test({\"dataset\": \"dados\", \"running\": \"margem\", \"cutoff\": 0.0})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_b77cfb7a","text":"Suspeita de manipulação da variável de corte: teste de densidade.","tool_calls":[{"vendor_id":"baseline_1","llm_name":"infer__density_test","tool_id":"infer.density_test","args":{"dataset":"dados","running":"margem","cutoff":0.0}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"214a96872f49b423163aeaf121ab6322c560f869236bb2f425a97296873cbdc2","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["58364449330986998fd65c1e996ea7be483919489579e656e0d2a7a7ad59a9df"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"rules-1","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.9721671231091022}}
{"call_id":"call_174df8dd","run_id":"20261015T171401Z-suite-tool_selection-baseline-a1426d/tool_selection.rdd_manipulated.test.s1000/e1a1","step":"analysis","kind":"tool","name":"infer.density_test","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"dados","running":"margem","cutoff":0.0,"bin_size":null,"bandwidth":null},"input_digest":"d1c767aca985adcc3fd02a9a51446406927af28d60a6b96e5c1b4bf6e6d90a01","dataset_digests":{"dados":"f3589c18b707084a48c49db39d5e1f84d07cbfed393f8be7341736aca4542f73"},"result":{"tool":"infer.density_test","version":"0.1.0","call_id":"call_174df8dd","status":"ok","reason":"","figures":[{"key":"theta","value":2.0759218210919066,"unit":"log points","label":"Log difference of the density of margem just above and just below the cutoff","se":0.24164270441413496,"ci_low":null,"ci_high":null,"ci_level":null,"derivation":{"steps":[{"text_pt":"Densidades estimadas no limiar, por ajuste linear local das alturas do histograma de cada lado.","formula":"θ = ln(f₊) − ln(f₋)","terms":[{"symbol":"f₊","value":0.9985121819249402,"unit":"","note":"acima do limiar"},{"symbol":"f₋","value":0.12525410725906022,"unit":"","note":"abaixo do limiar"}],"result":2.0759218210919066,"unit":"log"}],"note_pt":""}},{"key":"se","value":0.24164270441413496,"unit":"log points","label":"Standard error of theta","se":null,"ci_low":null,"ci_high":null,"ci_level":null,"derivation":{"steps":[{"text_pt":"Erro-padrão assintótico de McCrary (2008).","formula":"ep = √((1 / (n · h)) · (24 / 5) · (1 / f₊ + 1 / f₋))","terms":[{"symbol":"n","value":3000.0,"unit":"observações","note":""},{"symbol":"h","value":0.24620862560499915,"unit":"","note":""},{"symbol":"f₊","value":0.9985121819249402,"unit":"","note":""},{"symbol":"f₋","value":0.12525410725906022,"unit":"","note":""}],"result":0.24164270441413496,"unit":""}],"note_pt":""}},{"key":"z","value":8.590873149367363,"unit":"","label":"z statistic of theta","se":null,"ci_low":null,"ci_high":null,"ci_level":null,"derivation":{"steps":[{"text_pt":"Estatística z: θ dividido pelo seu erro-padrão.","formula":"z = θ / ep","terms":[{"symbol":"θ","value":2.0759218210919066,"unit":"","note":""},{"symbol":"ep","value":0.24164270441413496,"unit":"","note":""}],"result":8.590873149367363,"unit":""}],"note_pt":""}},{"key":"p_value","value":8.631088179912394e-18,"unit":"probability","label":"p-value of no density jump at the cutoff","se":null,"ci_low":null,"ci_high":null,"ci_level":null,"derivation":{"steps":[{"text_pt":"Probabilidade bilateral de uma normal padrão exceder |z| em valor absoluto.","formula":"","terms":[],"result":8.631088179912394e-18,"unit":"probabilidade"}],"note_pt":""}}],"diagnostics":[{"name":"method","value":"mccrary","flag":false,"note":"log difference of the smoothed densities at the cutoff"},{"name":"manipulation","value":true,"flag":true,"note":"the density jumps at the cutoff (p < 0.05): units sort around the threshold and an RD estimate is not credible as it stands"},{"name":"bin_size","value":0.02107684057906566,"flag":false,"note":"default bin width"},{"name":"bandwidth","value":0.24620862560499915,"flag":false,"note":"default smoothing bandwidth"},{"name":"n_obs","value":3000,"flag":false,"note":""},{"name":"f_left","value":0.12525410725906022,"flag":false,"note":"estimated density just below the cutoff"},{"name":"f_right","value":0.9985121819249402,"flag":false,"note":"estimated density just above the cutoff"},{"name":"n_distinct","value":3000,"flag":false,"note":""}],"tables":{},"datasets":{},"warnings":[],"assumptions":[],"notes":""},"outputs":null,"output_digest":"0328ab226ee187b2b747efde9bda6678061aaae21a5cd410fc79151877ef5669","params_versions":[],"sources":[],"started_at":"2026-10-15T17:14:02.219512Z","duration_ms":2.1260417997837067,"error":null,"decided_by":"model","rationale":"Suspeita de manipulação da variável de corte: teste de densidade.","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_b77cfb7a","llm_name":"infer__density_test","vendor_id":"baseline_1","result_for_model_ref":"0e9c371066027e4a5af8a4a393abdf34582c102ee8bdb0893948c5e05c678882"}}
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