"Finding or Accessing CSV Data" matching MCP tools:

• convert_data

  mcp

  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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• gribstream_build_runs_request

  GribStream MCP

  NO AUTH / PUBLIC / READ-ONLY. Builds and validates a copy-pasteable authenticated /api/v2/{dataset}/runs HTTP request without sending it. This tool does not execute the request, query weather values, or return forecast data. Use gribstream_query_runs when the user asks for actual model-run forecast data or CSV/JSON/NDJSON data. Generated direct API requests include Accept-Encoding: gzip, and generated curl commands use --compressed so large responses can be transferred compressed when the client supports it. The request body must use exact selectors discovered from the catalog or shared-parameter tools, with coordinates in request.coordinates and selectors in request.variables.

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• analyze_qbo_journal_anomalies

  HelloBooks AI Agents

  Scan a QuickBooks Online "Journal Entries" CSV export for anomalies — currently round-number lines (debit or credit amounts that are exact multiples of $1,000, above a $1,000 materiality threshold). Round numbers are statistically rare in real bookkeeping and frequently indicate estimates, plugs, or fraud signals worth review. Input is raw CSV text from QBO Reports → Accountant → Journal. Max 5,000 rows; max 5 MB. Returns flagged lines with severity ($100K+ high, $10K+ medium, else low) and a shareable URL. Use this when a user pastes QBO data and asks "any anomalies?", "look for round numbers", or "anything suspicious". Tier-0 subset — HelloBooks Phase 3.0 anomaly detection in the paid product additionally catches GL outliers vs entity history, vendor-history mismatches, archived-vendor activity, and AI-narrated suspicious lines (which require the live HelloBooks account).

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• analyze_xero_journal_cleanup

  HelloBooks AI Agents

  Scan a Xero "Manual Journals" CSV export for cleanup issues — unbalanced journals, duplicate journals (same date + same totals), and schema problems (invalid dates, malformed amounts, missing account code/name, missing group key). Input is the raw CSV content the user pastes after exporting from Xero via Accounting → Advanced → Manual Journals → Export. Xero-specific idioms handled: signed Amount column (positive = credit, negative = debit), explicit Debit/Credit fallback shape, Reference-or-Narration+Date grouping, account code preferred over name. Max 5,000 rows; max 5 MB. Returns structured flags with severity, a roll-up summary, parse diagnostics, and a shareable URL at agents.hellobooks.ai/r/{slug}. Use this when a user pastes Xero manual-journal data, asks "check my Xero books", or "find issues in my Xero journal". The funnel CTA routes to /migrate/from-xero for users who want to fix at scale.

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• analyze_profit_loss

  HelloBooks AI Agents MCP Server

  Take a Profit & Loss / Income Statement CSV export from QuickBooks Online, Xero, Zoho Books, or Wave (source auto-detected from section names) and run three checks: (1) pnl.subtotal_mismatch — each "Total Section" subtotal equals the sum of its preceding line items (catches missing or duplicated rows); (2) pnl.negative_expense — flags expense-section line items with negative amounts (usually sign-flips or refunds posted to the wrong side); (3) pnl.margin_red_flag — gross-profit margin < 5% or > 95%, or negative total revenue. Input is raw CSV text of a P&L report (Reports → Profit and Loss in QBO / Xero / Zoho / Wave). Max 5,000 rows; max 5 MB. Returns flags with severity, a summary with totalRevenue / totalCogs / grossProfit / grossMarginPct / netIncome (when detected), and a shareable URL at agents.hellobooks.ai/r/{slug}. Use this when a user pastes a P&L and asks "does my P&L look right?", "any sign errors?", "what is my gross margin?", or "anything suspicious in my income statement?". For period-over-period comparison use analyze_journal_variance with two periods of journal-entry data; this tool is single-period only.

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• nonprofit_get_filings

  nonprofit-explorer-mcp-server

  All Form 990 filings for a tax-exempt org by EIN: year-by-year revenue, expenses, assets, program-expense ratio (with inputs shown), executive compensation, and source PDF/XML links. Use for trend analysis, due diligence, and accessing primary 990 documents. The filing year (tax_prd_yr) is the fiscal year of the return — data lags 1–2 years; always cite the year. Program expense ratio is computed as (total_expenses − officer comp − other wages − fundraising) / total_expenses for 990/990-EZ; not available for 990-PF. Also returns filings_pdf_only — older filings with a PDF but no extracted financial data. Data from ProPublica Nonprofit Explorer, sourced from IRS Form 990 filings.

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• import_contacts_to_list

  socialloop-mcp

  Import contacts into one of your distribution lists (your network) — give a list_name (created if new) plus a contacts array ({name,email,phone}) and/or raw csv text (header row auto-mapped). Normalizes + dedupes, skips unreachable/duplicate, respects your plan cap. Requires a connected account.

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• analyze_qbo_journal_cleanup

  HelloBooks AI Agents

  Scan a QuickBooks Online "Journal Entries" CSV export for cleanup issues — unbalanced journals (debits ≠ credits, with severity by deviation), duplicate journals (same date + same totals, likely posted twice), and schema problems (invalid dates, malformed amounts, missing accounts, missing journal numbers). Input is the raw CSV content the user pastes after exporting from QBO via Reports → Accountant → Journal → Export. Max 5,000 rows; max 5 MB. Returns a structured flag list with severity (high/medium/low), a roll-up summary by category and severity, parse diagnostics (column mapping + unmapped columns), and a shareable URL at agents.hellobooks.ai/r/{slug} (7-day TTL) that renders a branded analysis page suitable for sending to a CA or bookkeeper. Use this when a user pastes QBO journal data, asks "check my books", "find issues in my QBO journal", or "what is wrong with my journal entries". Each flag includes a `fixableInHellobooks` boolean — true means HelloBooks can resolve it automatically in the paid product.

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• analyze_qbo_journal_cleanup

  HelloBooks AI Agents MCP Server

  Scan a QuickBooks Online "Journal Entries" CSV export for cleanup issues — unbalanced journals (debits ≠ credits, with severity by deviation), duplicate journals (same date + same totals, likely posted twice), and schema problems (invalid dates, malformed amounts, missing accounts, missing journal numbers). Input is the raw CSV content the user pastes after exporting from QBO via Reports → Accountant → Journal → Export. Max 5,000 rows; max 5 MB. Returns a structured flag list with severity (high/medium/low), a roll-up summary by category and severity, parse diagnostics (column mapping + unmapped columns), and a shareable URL at agents.hellobooks.ai/r/{slug} (7-day TTL) that renders a branded analysis page suitable for sending to a CA or bookkeeper. Use this when a user pastes QBO journal data, asks "check my books", "find issues in my QBO journal", or "what is wrong with my journal entries". Each flag includes a `fixableInHellobooks` boolean — true means HelloBooks can resolve it automatically in the paid product.

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• get_extract

  webbersites-x402-data-api

  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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• append_csv_to_dataset

  CatchAll

  Append entities from a CSV file to an existing dataset. Parses the CSV and appends its entities to the dataset. Each row must have a `name` column; include a `domain` or `description` column (or both) for meaningful enrichment. Duplicate rows (by name) are skipped. To create a new dataset from a CSV, use `create_dataset_from_csv` instead.

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• validate_import

  Timix.AI

  Validate a bulk-import CSV without persisting anything. Returns row-level errors and a summary so you can fix the file before calling commit_import.

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• analyze_profit_loss

  HelloBooks AI Agents

  Take a Profit & Loss / Income Statement CSV export from QuickBooks Online, Xero, Zoho Books, or Wave (source auto-detected from section names) and run three checks: (1) pnl.subtotal_mismatch — each "Total Section" subtotal equals the sum of its preceding line items (catches missing or duplicated rows); (2) pnl.negative_expense — flags expense-section line items with negative amounts (usually sign-flips or refunds posted to the wrong side); (3) pnl.margin_red_flag — gross-profit margin < 5% or > 95%, or negative total revenue. Input is raw CSV text of a P&L report (Reports → Profit and Loss in QBO / Xero / Zoho / Wave). Max 5,000 rows; max 5 MB. Returns flags with severity, a summary with totalRevenue / totalCogs / grossProfit / grossMarginPct / netIncome (when detected), and a shareable URL at agents.hellobooks.ai/r/{slug}. Use this when a user pastes a P&L and asks "does my P&L look right?", "any sign errors?", "what is my gross margin?", or "anything suspicious in my income statement?". For period-over-period comparison use analyze_journal_variance with two periods of journal-entry data; this tool is single-period only.

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• export_last_worker_run_results

  mcp-coreclaw

  Export the current user's most recent CoreClaw run results. WHEN TO USE: Use when the user asks to download/export the latest run as CSV or JSON. 中文触发: 当用户要在 CoreClaw 中查询、运行、重跑、停止、导出或查看对应 worker/run/task 数据时使用。 WHEN NOT TO USE: Do not use public web search or code search for private CoreClaw platform data. Do not call excluded internal worker-version or internal-detail APIs. RETURNS: JSON with a temporary download_url. WORKFLOW: Call after get_last_worker_run shows status succeeded.

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• analyze_xero_journal_anomalies

  HelloBooks AI Agents MCP Server

  Scan a Xero "Manual Journals" CSV export for anomalies — currently round-number lines (debit or credit amounts that are exact multiples of $1,000, above a $1,000 materiality threshold). Input is raw CSV text from Xero Accounting → Advanced → Manual Journals → Export. Max 5,000 rows; max 5 MB. Returns flagged lines with severity ($100K+ high, $10K+ medium, else low) and a shareable URL. Use this when a user pastes Xero data and asks "any anomalies?", "look for round numbers", or "anything suspicious". Same Tier-0 / paid-product split as the QBO variant — history-aware anomaly checks (GL outliers, vendor history, archived-vendor activity, LLM-narrated suspicious) live in the authenticated MCP / paid product.

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• create_dataset_from_csv

  CatchAll (by NewsCatcher)

  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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• export_search_csv

  MCP Market Russia

  Export search results as CSV text (UTF-8 with BOM, Excel-friendly). entity: 'companies' or 'projects' query: free-text search in name/description category, region: filter fields budget_max: for companies, cap on min_project_price limit: 1..2000 rows (default 500) Returns CSV text ready to save as .csv and open in Excel.

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• create_dataset_from_csv

  CatchAll

  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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• dataset_details

  Dati It

  Full dataset record by id or slug (CKAN package_show), including its resources. Each resource carries a "url" you can fetch directly to download the data (CSV/JSON/etc.) — this portal has no datastore, so there is no row-level query API.

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• get_dataset

  Healthdata

  Get full metadata for a HealthData.gov dataset by its package ID. Returns title, description, organization, resources (CSV/JSON download links), update frequency, and tags.

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