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619,939 tools. Updated 2026-09-28 21:07

"Finding or Accessing CSV Data" matching MCP tools:

  • Use this when you need to convert tabular data between JSON (array of objects), CSV, TSV, and XML instead of hand-transforming it. Deterministic: same input, same output. Handles quoted CSV fields (embedded commas, escaped "" quotes), flattens nested objects into dotted keys (b.x), and takes the union of keys across all rows so ragged data still lines up in columns. CSV/TSV input needs a header row plus at least one data row; JSON input must be an array of objects. Example: {from:'csv', to:'json'} on "name,age\nAda,36\nGrace,45" -> rowCount 2 and an output JSON array of two objects. Returns the input/output formats, the parsed row count, and the serialized output document as a string.
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  • Export GAAP financial reports for data room / diligence: trial_balance, balance_sheet, income_statement, cash_flow. format=json (default) returns report JSON from /api/ledger/reports; format=csv returns CSV text; format=pdf returns printable HTML as base64 (open in browser → Print → Save as PDF — same as Accounting → Reports). Requires ledger.view. Use ledger_list_books for bookId (primary GAAP book). Point-in-time reports use asOfDate; income_statement and cash_flow use fromDate + toDate (default: YTD through asOfDate/toDate). cash_flow and income_statement line amounts are period activity for that window. Current holdings or amounts owed: ledger_trial_balance.
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  • Draws a chart from a CSV table and returns it as an image. WHEN TO USE: the user wants to see data as a chart, or needs a chart image for a document, a README, a slide or a chat. HOW IT WORKS: - csv: a header row, then data rows. First column: the category (or x value); the next columns: numbers. Use a comma or semicolon as separator; decimals with a point, or with a comma when the separator is a semicolon. Up to 20,000 characters. - chartType, and what each one needs: bar, horizontalBar, line, area, pie, donut, funnel — one category column + one numeric column; multiline — one x column + one or more numeric columns (one line per column); scatter — two numeric columns (x, y); heatmap — three columns: row, column, value. - title: optional, drawn at the top. RETURNS: - image: a PNG address that IS the chart (rendered on request) — put it straight into Markdown (![title](image)) or an <img>. imageSvg: the same as SVG, sharper where SVG is accepted. Both are null when the CSV is too long to fit in an address; then use svg. - svg: the chart as SVG markup. - link: this tool's web page with the same chart, to edit it. ERRORS say what is wrong with the CSV (missing columns, non-numeric values, too many rows); fix the data and call again.
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  • Upload a list of individual CONTACTS (people) as a Contact List CSV and create a CONTACT_LIST audience on the Metadata platform. ALSO KNOWN AS: "CSV Upload - Contacts", "Contacts List", "Contact List CSV", "CSV Contacts audience", "CSV Upload - Contacts List Audience". AUDIENCE TYPE: Contact List / CSV Upload - Contacts. This tool is ONLY for contact/people-level data (email, first name, last name, job title, etc.). Do NOT use this tool for company/account-level data (company names and websites) — use upload_account_list_csv_audience instead. This tool performs a two-step process: 1. Generates a Contact List CSV file from the provided contact data and uploads it. 2. Creates a CONTACT_LIST audience using the uploaded contact list file. WHEN TO USE: - "Create a CSV Upload - Contacts with the list" - "CSV Upload - Contacts" - "Create a contacts list audience" - "Upload these contacts as an audience" - "Create a contact list audience from these people" - "I have a list of emails and names, create an audience" - "Build a contact list audience" - "Upload contact list CSV" - When the data contains people-level fields: email, first name, last name, job title, company, country WHEN NOT TO USE: - When the user wants to upload company accounts (company name + website) — use upload_account_list_csv_audience. - When the data is account/company-level, not contact/people-level. WORKFLOW: 1. Provide the audience name and an array of contact objects. 2. The tool generates a CSV with the header: email,firstname,lastname,jobtitle,employeecompany,country,appleidfa,googleaid 3. Uploads the CSV, then creates the CONTACT_LIST audience. CRITICAL — DATA MAPPING RULES (READ CAREFULLY BEFORE CALLING THIS TOOL): The "contacts" parameter is a JSON array of objects. Each object represents one contact/person and MUST use these exact field names: - "email" → The contact's email address (REQUIRED per contact) - "firstname" → The contact's first name - "lastname" → The contact's last name - "jobtitle" → The contact's job title / role - "employeecompany" → The company the contact works at - "country" → The contact's country - "appleidfa" → Apple IDFA (advertising identifier), optional - "googleaid" → Google Advertising ID, optional Example: [ { "email": "jane@metadata.io", "firstname": "Jane", "lastname": "Johnson", "jobtitle": "Marketing Manager", "employeecompany": "Metadata.io", "country": "United States", "appleidfa": "EA7583CD-A667-48BC-B806-42ECB2B48606", "googleaid": "" }, { "email": "john@metadata.io", "firstname": "John", "lastname": "Johnson", "jobtitle": "Marketing Manager", "employeecompany": "Metadata.io", "country": "United States", "appleidfa": "", "googleaid": "cdda802e-fb9c-47ad-9866-0794d394c912" } ] IF THE USER PROVIDES A FILE (CSV, XLSX, spreadsheet, or any tabular data): 1. You MUST first read and inspect the file contents. 2. Identify which columns map to: email, firstname, lastname, jobtitle, employeecompany, country, appleidfa, googleaid. - The columns may NOT be literally named as above. They could be named: "Email Address", "E-mail", "First Name", "First", "Last Name", "Surname", "Job Title", "Title", "Role", "Position", "Company", "Organization", "Employer", "Country", "Location", "Apple IDFA", "IDFA", "Google AID", "GAID", or any variation. - Use your best judgment to map the correct columns to the expected field names. - If ambiguous, ask the user to clarify which column maps to which field. 3. Extract every row from the file and build the contacts array yourself, mapping each column value to the correct field name. 4. Do NOT pass raw file contents, column headers, or file paths — always transform into the array-of-objects format described above. 5. Skip rows where email is empty/missing (email is the minimum required field per contact). 6. For any field not present in the source data, omit it or pass an empty string. 7. If the file has no identifiable email column, ask the user which column contains emails. DO NOT: - Use the source file's column names as field names — always normalize to: email, firstname, lastname, jobtitle, employeecompany, country, appleidfa, googleaid. - Send the raw file path or file bytes — extract the data and build the array. - Confuse this with account/company uploads — this is for PEOPLE, not companies. TWO WAYS TO SUPPLY THE CONTACTS — provide EXACTLY ONE of: • `contacts`: an inline JSON array of contact objects. Use this for short ad-hoc lists you have already parsed into context. • `contacts_source_csv_url`: a public URL of a CSV file with header EXACTLY `email,firstname,lastname,jobtitle,employeecompany,country,appleidfa,googleaid` (case-insensitive, whitespace-trimmed). Use this WHENEVER THE USER ATTACHED A CSV to the chat — the URL is surfaced to you via `AudienceBrief.attached_file_urls`; pass it through verbatim. The MCP server downloads + validates + uploads the file without the rows ever travelling through your LLM context (essential for 100k+ row contact files). If both are provided, or neither, the tool errors with a clear message — pick one. CSV-URL HEADER RULES (when you choose the `contacts_source_csv_url` path): - First row of the CSV MUST be exactly `email,firstname,lastname,jobtitle,employeecompany,country,appleidfa,googleaid` (case-insensitive). - Rows with an empty email are dropped server-side before counting. - The 300–300,000 row limit is enforced on the post-filter count. - The server enforces a 50 MB cap on the downloaded file. PARAMETERS: - audience_name: Name for the new contact list audience (required) - contacts: Array of contact objects (optional, mutually exclusive with contacts_source_csv_url). At minimum each contact must have "email". All other fields are optional but recommended. - contacts_source_csv_url: URL of a contacts CSV with the canonical 8-column header (optional, mutually exclusive with contacts). RETURNS: - success: Whether the operation completed successfully - audience_name: The name of the created audience - audience_type: CONTACT_LIST - file_id: The ID of the uploaded contact list file - contacts_count: Number of contacts processed by the backend - contacts_provided: Number of contacts sent in the request IMPORTANT NOTES: - CONTACT LIMITS: Minimum 300 contacts, maximum 300,000 contacts. - Email is the minimum required field per contact — contacts without email are dropped. - The audience type created is CONTACT_LIST, distinct from Account List CSV (FIRMOGRAPHIC_INCLUDE). - appleidfa and googleaid are optional mobile advertising identifiers — leave empty if not available.
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  • Get COMMENT CONTENT (text, likes) for a Tiktok post. Returns the actual comment objects with text and metadata. RETURNS COMMENT DATA: id, text, username, createdAtDate, likeCount. Use for reading what people said. FAST (default, omit responseType or responseType="fast"): Returns up to 300 results directly (use limit param to reduce, e.g. limit=5). Auto API fallback for fresh data. PAGING (responseType="paging"): Async paginated results (100/page), returns operationId for polling via checkOperationStatus. Supports pageNumber/tableName for subsequent pages. CSV (responseType="csv"): Async single CSV download, returns operationId, poll for S3 link. CODE EXECUTION: For csv mode, download CSV and use code execution to analyze full dataset. Ideal for: sentiment analysis, reading discussions, analyzing comment content, engagement patterns. Date filters: OMIT startDate/endDate by default. ONLY pass if user explicitly requests date range. IMPORTANT!!!!!: THE CURRENT YEAR IS 2026. When user requests relative dates (last week, last month), verify the current date from your system context and double-check the calculated dates - models often get the year wrong, searching one year earlier than intended. Optional fields: ["id", "text", "username", "createdAtDate", "likeCount"]. This is a safe, read-only tool for analyzing searchable information.
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  • Create a new data source from an inline base64-encoded file (CSV, TSV, JSON, Excel, TXT, PDF). The file goes through the same validation and preprocessing as a web upload. Returns the data_source_id you can pass to run_analysis as soon as preprocessing completes (poll get_data_source_schema for readiness or pass wait_seconds to block here).
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Matching MCP Servers

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    maintenance
    This read-only MCP Server allows you to connect to CSV Files data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out our MCP Server for CSV Files (https://www.cdata.com/drivers/csv/download/mcp).
    MIT

Matching MCP Connectors

  • Read-only. Use to query Dreamlit analytics for overview metrics, notification rows, recipient engagement, or workflow run rows with filters, sorting, and cursor pagination. Returns bounded structured analytics data, effective query metadata, pagination details when rows are included, and relevant app URLs. Do not use for CSV exports, bulk dumps, workflow edits, publishing, or low-level database access.
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  • Document extraction: fetch a PDF, DOCX, or CSV by URL and get clean Markdown plus structured JSON — PDF text by page with metadata (honestly flags scanned PDFs that would need OCR), DOCX converted to real Markdown, CSV parsed to typed columns + JSON rows + a Markdown table. For agents that need document contents, not bytes. ($0.02 per call, paid via x402)
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  • Convert messy tabular text into clean, typed JSON rows. Auto-detects CSV, TSV, or a Markdown table and returns one JSON object per row plus an inferred column/type summary. Pure deterministic compute — no network or model calls. What it handles: delimiter sniffing (comma/semicolon/tab/pipe), quoted fields with embedded commas and newlines, BOM, ragged rows (padded/truncated), Markdown separator rows and escaped pipes, header auto-detection, and per-column type inference (integer/number/boolean/null/string). When to use: you have CSV/TSV/Markdown-table text (often emitted by tools or LLMs) and want structured, typed rows — optionally validated/coerced against a JSON Schema. When NOT to use: the data is already clean JSON, or it is HTML/xlsx/binary (not supported). Args: - input (string, required): raw tabular text. - format ("auto"|"csv"|"tsv"|"markdown", default "auto"): force a format or auto-detect. - hasHeader ("auto"|"true"|"false", default "auto"): whether the first row is a header. - inferTypes (boolean, default true): coerce cells to number/integer/boolean/null; else keep strings. - schema (object, optional): JSON Schema (draft 2020-12) to validate/coerce each row object against. Returns structuredContent: { "ok": boolean, // false if the input cannot be parsed as a table "format": "csv"|"tsv"|"markdown", "columns": [{ "name": string, "type": string }], "rows": [{ ... }], // one object per row, keyed by column name "rowCount": number, "changed": boolean, // true if any normalization/coercion happened "errors": string[], // actionable messages when ok is false "repairs": string[] // description of each normalization applied }
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  • Generate a complete, ready-to-deploy D365 F&O AOT XML scaffold for any object type. Returns the XML + the correct file path inside the model -- the calling client (VS Extension, Cursor, Copilot Chat) is responsible for writing the result to disk. This tool is fully read-only on the server: it never writes to the filesystem, never mutates external state, and is safe to expose from the cloud MCP. When the knowledge base is available the scaffold is auto-enriched with real metadata (existing field EDTs, related-table validation, auto-detected grid fields, etc.). Supported aotType values ---------------------------------------------- AxTable . AxClass . AxForm . AxEnum AxEdt . AxView . AxQuery . AxDataEntityView AxMenuItemDisplay . AxMenuItemAction . AxMenuItemOutput AxSecurityPrivilege . AxSecurityDuty . AxSecurityRole . AxSecurityPolicy AxReport . AxFormExtension . AxTableExtension . AxClassExtension AxEnumExtension . AxEdtExtension . AxQueryExtension . AxWorkflow Common options (all types) -------------------------------------------- label - human-readable label shown in the UI developerDoc - developer documentation string (tables/classes) AxTable -------------------------------------------------------------------------- tableGroup - Main | WorksheetHeader | WorksheetLine | Transaction | Parameter | Group cacheLookup - None | Found | FoundAndEmpty | NotInTTS | EntireTable fields - csv "Name:Type[:EDT[:Label[:mandatory]]]" Types: String Int Int64 Real Date DateTime Enum Container Guid indexes - csv "IndexName:field1+field2[:unique|:alternatekey]" relations - csv "RelName:RelatedTable:Field:RelatedField" titleField1/2 - field names for the lookup title createdBy / modifiedBy - true | false (default true) generateFind / generateExist / generateValidateWrite / generateInitValue - true | false AxClass -------------------------------------------------------------------------- extends - base class name implements - csv of interfaces abstract / final - true | false pattern - SysOperation | RunBase | Service | EventHandler | none (default) methods - csv of additional method names to stub AxForm ---------------------------------------------------------------------------- dataSourceTable - primary data-source table name pattern - SimpleList | DetailsTransaction | DetailsMaster | ListPage | Dialog gridFields - csv of field names for the grid (auto-detected from KB if omitted) detailFields - csv of field names for the detail group methods - csv of form method names to stub AxEnum ---------------------------------------------------------------------------- style - Ordinary | ComboBox | Radio | CheckBox values - csv "Name[:Label[:intValue]]" e.g. "Draft:Draft:0,Posted:Posted:1" AxEdt ----------------------------------------------------------------------------- extends - base EDT (e.g. Name, Description, Amount) stringSize - integer enumType - base enum for enum EDTs referenceTable - table that provides the lookup AxView ---------------------------------------------------------------------------- dataSources - csv "Table[:Alias]" fields - csv "DataSource.Field[:Alias]" AxQuery ------------------------------------------------------------------------- dataSources - csv "Table[:Alias[:JoinMode]]" AxDataEntityView ----------------------------------------------------------- primaryTable - root table joinTables - csv of additional tables publicEntityName - OData collection name (pluralised entity name) isPublic - true | false (default true) fields - csv "Table.Field[:PublicName]" (auto-detected from KB if omitted) AxMenuItemDisplay / Action / Output ------------------------------------------ objectName - target form / class / report name runOn - Server | Client | Called from (default Server) helpText - tooltip string AxSecurityPrivilege ----------------------------------------------------------- entryPoints - csv "MenuItemName[:ObjectType[:Grant]]" ObjectType: MenuItemDisplay | MenuItemAction | MenuItemOutput Grant: NoAccess | Read | Update | Create | Delete AxSecurityDuty --------------------------------------------------------------------- privileges - csv of privilege names to include AxSecurityRole ---------------------------------------------------------------------- duties - csv of duty names privileges - csv of privilege names (direct assignment -- avoid if possible) AxReport ------------------------------------------------------------------------- query- AxQuery name driving the dataset dataSourceTable - alternative: direct table name (if no query) AxFormExtension ----------------------------------------------------------------- baseForm - name of the standard form to extend fields - csv "ControlName[:EDT]" AxTableExtension --------------------------------------------------------------------- baseTable - name of the standard table to extend fields - csv "Name:Type[:EDT[:Label[:mandatory]]]" indexes - csv "IndexName:field1+field2[:unique|:alternatekey]" AxClassExtension (Chain of Command) ------------------------------------------- baseClass - name of the standard class/table/form to wrap baseType - class | table | form (default class) methods - csv of method names to wrap with CoC AxEnumExtension --------------------------------------------------------------- baseEnum - name of the standard enum to extend values - csv "Name[:Label[:intValue]]" IMPORTANT: start from value 10 or higher to avoid conflicts with standard values (D365 extension contract) AxEdtExtension ---------------------------------------------------------------- baseEdt - name of the standard EDT to extend stringSize - new StringSize (must be <= base EDT limit; omit to inherit) label - override label for this extension AxQueryExtension -------------------------------------------------------------- baseQuery - name of the standard query to extend dataSources - csv "Table[:Alias[:JoinMode]]" (add extra data sources) ranges - csv "DataSource.Field:value" (add filter ranges) AxSecurityPolicy (Row Level Security) ---------------------------------------- constrainedTable - primary table this policy restricts query - AxQuery name that defines the allowed rows operation - Select | Update | Create | Delete | Insert (default Select) enabled - true | false (default true) AxWorkflow --------------------------------------------------------------------- category - category name (links to a module/table) documentTable - table the workflow operates on documentMenuItem - menu item that opens the record
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  • Issue a short-lived signed URL to upload an Excel/CSV file straight into Storage, then call layerz_parse_file with the returned file_id. PUT the file to signed_url directly (e.g. `curl -X PUT "<signed_url>" -H "Content-Type: <content_type>" --data-binary @file`) — the binary never enters the agent context. Limits: 25 MB, MIME whitelist (xlsx, xls, csv, json, txt); the file_id expires after 24h.
    ConnectorOAuth
  • Convert a JSON array of objects into CSV. The union of all keys becomes the header row in first-seen order, missing keys become empty cells, and nested objects or arrays are written back as compact JSON in the cell. Output follows RFC 4180 quoting. Source: https://hopi.co.uk/json-to-csv/
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  • Export the user's full data set — all accounts + all transactions — as CSV strings plus a base64-encoded ZIP bundle. Premium-only. Parity with the REST ``/api/v1/data/export_all/`` ZIP download. The individual ``export_accounts_csv`` / ``export_transactions_csv`` tools are free; this Premium "export everything" call bundles both in one response. Hand the ``zip_base64`` to the user to save a real .zip, or read the CSV strings directly. Returns: ``{"success": True, "accounts_csv": "...", "transactions_csv": "...", "zip_base64": "...", "filename": "zoninga_export_YYYY-MM-DD.zip", "account_count": N, "transaction_count": M}``, or ``{"error": "..."}`` if not Premium.
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  • Get comments (replies) to specific post. FAST (default, omit responseType or responseType="fast"): Returns up to 300 results directly (use limit param to reduce, e.g. limit=5). Auto API fallback for fresh data. Results include guidance for full mode. PAGING (responseType="paging"): Async paginated results (100/page), returns operationId for polling via checkOperationStatus. Supports pageNumber/tableName for subsequent pages. CSV (responseType="csv"): Async single CSV download, returns operationId, poll for S3 link. CODE EXECUTION: For csv mode, download CSV and use code execution to analyze all comments. Ideal for: sentiment analysis, discussion themes, community engagement analysis. First searches database, then external API if data is stale (>10 days). Date filter: OMIT startDate by default. ONLY pass if user explicitly requests filtering from specific date (YYYY-MM-DD format). IMPORTANT!!!!!: THE CURRENT YEAR IS 2026. When user requests relative dates (last week, last month), verify the current date from your system context and double-check the calculated dates - models often get the year wrong, searching one year earlier than intended. Use to analyze community response and discussion. NOT for quotes - use getTwitterPostQuotes. Optional fields parameter for performance: ["id", "text", "authorUsername", "createdAt"]. This is a safe, read-only tool for analyzing searchable information.
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  • <tool_description> Settle pending payments for media buys. Supports manual CSV export, Stripe invoice (Phase 2 stub), and x402 micropayments (Phase 2 stub). </tool_description> <when_to_use> When a publisher wants to collect earned revenue or an advertiser needs to settle outstanding charges. Use method='manual' for CSV export. Stripe and x402 are stubs (Phase 2). </when_to_use> <combination_hints> get_campaign_report → settle (after verifying amounts). Filter by media_buy_id, publisher_id, or period. </combination_hints> <output_format> Settlement totals (gross, platform fee, net), entry count, and method-specific data (CSV for manual). </output_format>
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  • Create a new dataset by uploading a CSV file. The CSV must have at least a `name` column. For meaningful entity enrichment each row should also include a `domain` column or a `description` column (or both) — a row with only a name is accepted but produces lower-quality enrichment. Additional columns are mapped to entity attributes. Max file size is plan-dependent. To add CSV rows to an existing dataset, use `append_csv_to_dataset` instead.
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  • Create a new dataset by uploading a CSV file. The CSV must have at least a `name` column. For meaningful entity enrichment each row should also include a `domain` column or a `description` column (or both) — a row with only a name is accepted but produces lower-quality enrichment. Additional columns are mapped to entity attributes. Max file size is plan-dependent. To add CSV rows to an existing dataset, use `append_csv_to_dataset` instead.
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  • CSV in, JSON out — and back again — Convert between the shapes data actually arrives in: CSV or TSV to JSON, JSON to CSV or TSV, CSV to a Markdown table, Markdown to HTML. Proper RFC 4180 parsing — quoted fields, embedded commas and newlines, doubled quotes — so a spreadsheet exported by a human does not silently come apart. Numbers and booleans get real types (turn it off with typed=false). No model call and no upstream: local parsing only. Required inputs: text, from, to. Priced $0.005 per call over x402 on Base; send a prepaid x-credit-token header for unlimited calls, or get 1 free call/day per tool. No wallet or API key required.
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  • Register an immutable LeanSolver finding against the currently tracked goal contract. subjectRefs must be goal-scoped refs of kind goal, criterion, or assumption. Returns the server-generated finding id to pass later as addressesFindingId; rejects stale contract tracking.
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  • Bulk-import a CSV file into a D365 F&O entity through the Data Management package REST API. Builds the package (Manifest + header + CSV) in memory, uploads it to Azure blob, then calls ImportFromPackage which AUTO-CREATES the data project from the manifest. Runs in batch; the tool polls until completion and returns the final status plus an error-keys file URL when rows fail. Provide either filePath (a .csv on disk) or inline csvContent. Resolve the entity name from the KB (find_entity_for_table) -- do not invent it.
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    Destructive
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