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bankstatemently

bankstatemently

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    • A
      license
      A
      quality
      D
      maintenance
      Converts PDF bank statements into structured data (Markdown, JSON, CSV, JSONL) with verified transactions and balance checks, enabling agents to audit numbers.
      5
      0
      MIT
    • A
      license
      A
      quality
      A
      maintenance
      Enables AI assistants such as Claude, Cursor and VS Code to turn PDF bank statements into structured transactions or import-ready CSV, OFX, QFX, QBO and Xero CSV files. Handles password-protected, scanned and multi-page statements, returning the data or saving the converted file next to the original PDF.
      5
      90 npm
      1
      MIT
    • A
      license
      C
      quality
      D
      maintenance
      Provides deterministic data parsing and enrichment for AI agents, including bank statements, trade history, EDI, PDFs to structured markdown, and atomic enrichment for amounts, dates, and addresses, with strict schema enforcement to prevent hallucinations.
      37
      33 npm
      2
      ISC
    • A
      license
      A
      quality
      C
      maintenance
      Converts customer-supplied PDF bank statements into checked Excel, CSV, or JSON with balance validation. Runs locally with your own MainBook API key or against MainBook's hosted endpoint, and it never connects to bank accounts.
      5
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Deterministic bank-statement parsing for AI agents: messy CSV/OFX exports to clean, categorized ledger rows. In-memory only, no storage, no external calls, no LLM in the loop.
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Lets AI assistants and editors read documents on the local machine, turning PDFs, scans, invoices, receipts and bank statements into structured fields, line items and entities over stdio. Runs fully self-hosted with no cloud calls or API keys, so sensitive files never leave the user's machine.
      2
      MIT

    TDQS

    A3.9/5.0

    Scored across 18 tools

    Disambiguation4/5

    Each tool targets a distinct resource or action, but the five transaction-analytics tools (aggregate, compare, time_series, group_by, top_n) overlap in scope/metric handling and require careful reading to choose correctly. The transfer tools (list_transfers, adjudicate_transfers, list_outflows) also need deliberate distinction, though descriptions clarify their boundaries.

    Naming Consistency4/5

    All names use snake_case, and most follow a verb_noun pattern (list_transactions, get_statement, convert_statement). Minor deviations like bare verbs (aggregate, compare) and non-verb constructions (time_series, group_by, top_n) break the pattern slightly but remain readable and consistent in casing.

    Tool Count3/5

    18 tools is on the heavy side for the domain. Several analytics tools (aggregate, compare, time_series, group_by, top_n) could potentially be consolidated into a parameterized analysis tool, suggesting some over-fragmentation, though each tool does cover a distinct operation.

    Completeness4/5

    The surface covers conversion, retrieval, categorization, analytics, transfer matching, upload, credits, rating, and benchmark evaluation, giving strong lifecycle coverage. Minor gaps remain: no direct account-listing tool and dismiss_statement is a soft hide rather than a hard delete, but these are workable.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues