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Run Disco on tabular data to find novel, statistically validated patterns. This is NOT another data analyst — it's a discovery pipeline that systematically searches for feature interactions, subgroup effects, and conditional relationships nobody thought to look for, then validates each on hold-out data with FDR-corrected p-values and checks novelty against academic literature. This is a long-running operation. Returns a run_id immediately. Use discovery_status to poll and discovery_get_results to fetch completed results. Use this when you need to go beyond answering questions about data and start finding things nobody thought to ask. Do NOT use this for summary statistics, visualization, or SQL queries. Public runs are free but results are published. Private runs cost credits. Call discovery_estimate first to check cost. Private report URLs require sign-in — tell the user to sign in at the dashboard with the same email address used to create the account (email code, no password needed). Call discovery_upload first to upload your file, then pass the returned file_ref here. Args: target_column: The column to analyze — what drives it, beyond what's obvious. file_ref: The file reference returned by discovery_upload. analysis_depth: Search depth (1=fast, higher=deeper). Default 1. visibility: "public" (free) or "private" (costs credits). Default "public". title: Optional title for the analysis. description: Optional description of the dataset. excluded_columns: Optional JSON array of column names to exclude from analysis. column_descriptions: Optional JSON object mapping column names to descriptions. Significantly improves pattern explanations — always provide if column names are non-obvious (e.g. {"col_7": "patient age", "feat_a": "blood pressure"}). author: Optional author name for the report. source_url: Optional source URL for the dataset. use_llms: Slower and more expensive, but you get smarter pre-processing, summary page, literature context and pattern novelty assessment. Only applies to private runs — public runs always use LLMs. Default false. api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

One tool's full risk classification on a published MCP server: category, severity, risk analysis and evidence, OWASP classes, parameter schema and the recommended policy default. Use when deciding whether to allow a specific tool call, e.g. "should execute_sql on this server be permitted?"

Exécute une requête SQL directe sur la base DuckDB (read-only). Tables principales : - general_results : résultats généraux par bureau de vote - candidats_results : résultats par candidat par bureau de vote - elections_disponibles : liste des élections (id_election, nb_bureaux, total_inscrits) - candidats_avec_nuances : candidats avec famille politique (si nuances enrichies) - resultats_par_famille : résultats agrégés par famille politique - communes, epci, departements, regions : divisions administratives - bv_sociodemographique : données socio-démo par bureau de vote ATTENTION — colonnes de general_results (noms avec espaces, à quoter) : id_election, id_brut_miom, "Code du département", "Code de la commune" (SANS préfixe dept, ex: '055' pour Marseille), "Libellé de la commune", Inscrits, Abstentions, Votants, Blancs, Nuls, Exprimés. PRÉFÉRER query_elections pour les analyses standard — query_sql est pour les requêtes ad hoc avancées. Exemple : SELECT id_election, ROUND(100.0 * SUM(Abstentions) / NULLIF(SUM(Inscrits), 0), 1) as abstention_pct FROM general_results WHERE "Code du département" = '13' AND "Code de la commune" = '055' GROUP BY id_election ORDER BY id_election DESC LIMIT 5

Get ONE audit-trail row in FULL by its `id` (from list_audit_events) — including the fields the list omits: the REDACTED `detail` (old→new diffs for allowlisted keys, redacted at write time — never raw args/secrets) and `statement_excerpt` (the left-400-char SQL for a direct-PG write). Owner-scoped: a row whose actor isn't YOU returns not-found (found:false) unless you hold audit:view (operators can read any row) — an out-of-reach row is never distinguished from a non-existent one. Pass `at` (the row's timestamp from the list) to prune the partition for a faster lookup. Returns { ok, found, event }.

Use this when a user pastes messy or minified SQL and wants it pretty-printed into a readable, canonical layout, or wants keyword casing normalized. Deterministic: same input, same output. Each clause keyword (SELECT, FROM, WHERE, GROUP BY, ...) goes on its own line with its arguments indented beneath it, commas break columns onto new lines, JOIN/AND/OR start fresh lines, and short parenthesised groups stay inline. Optional dialect hint affects identifier quoting (e.g. tsql [brackets]); indent is a spaces count or "tab" (default 2); keywordCase is upper/lower/preserve (default upper). Example: "select id from t" -> formatted "SELECT\n id\nFROM\n t". Returns an error for empty or oversized (>200000 chars) input.

Comprehensive safety scan for injection attacks and dangerous patterns. Detects: prompt injection, jailbreak/DAN attempts, role hijacking, SQL injection (UNION/DROP/OR 1=1), XSS, Python/JS/Shell code injection, path traversal, oversized payloads, null bytes. Returns safe=true/false with finding list and block/allow decision.