Skip to main content
Glama
flexorch-mcp

flexorch-mcp

Official
by flexorch

Related Servers

Alternatives to flexorch-mcp

No user-submitted related servers found.

    Related Servers

    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables MCP-compatible agents to run the complete document extraction loop as remote tools: upload files, submit extraction jobs with plain-language instructions, answer clarifying questions, inspect review-needed rows and failed pages, and download results as spreadsheets or JSON.
      MIT
    • F
      license
      Not graded
      quality
      D
      maintenance
      Enables AI agents to extract structured data from PDFs with confidence scores and provenance, and to search, review, and correct documents via MCP tools, resources, and prompts.
      -
    • A
      license
      Not graded
      quality
      D
      maintenance
      Enables querying enterprise documents (DOCX, PDF, PPTX) using natural language, with hybrid search and MCP integration for Claude Desktop and other agents.
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables vector search over documents, text extraction and chunking of files into Markdown, and private deep research generation through Vectorize pipelines. It exposes these retrieval, extraction, and research capabilities as MCP tools for clients like Claude, Cursor, and VS Code.
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables document ingestion and typed knowledge graph queries through Claude MCP tools, allowing agents to extract, store, and retrieve typed entities and relations from documents.
      2
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables Claude to query client and document data in plain English with cited answers, enforce per-client access, safely file incoming documents, and record audited actions through MCP tools.
      MIT

    TDQS

    A4.8/5.0

    Scored across 8 tools

    Disambiguation5/5

    Each tool has a clearly distinct role in the pipeline: document.process starts a job, job.status polls it, job.result retrieves extracted fields, and dataset.* tools handle building, exporting, searching, indexing, and chunk retrieval. Even job.status and job.result are clearly separated by their polling vs. result-reading purposes.

    Naming Consistency5/5

    All tools follow a consistent <domain>.<operation> pattern (document., job., dataset.) with lowercase snake_case. The operations mix verbs (process, build, export, index) and nouns (status, result, search, chunks), but the pattern is uniform and predictable, making it easy to infer the tool's function from its name.

    Tool Count5/5

    Eight tools is well-scoped for the server's purpose: a document processing and RAG preparation pipeline. Each tool covers a necessary step in the workflow, with no redundancy and no unnecessary additions.

    Completeness5/5

    The toolset fully covers the document processing lifecycle: submit document, monitor job, retrieve results, build dataset, export dataset, plus additional search/index/chunks capabilities for RAG. The workflow is clearly described with numbered steps, and there are no dead ends—every tool's output feeds into the next appropriate tool.

    Maintenance

    ActivityNo data
    ResponsivenessOutdated data