flexorch-mcp
OfficialRelated Servers
Alternatives to flexorch-mcp
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityBmaintenanceEnables 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
- FlicenseNot gradedqualityDmaintenanceEnables 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.-
- AlicenseNot gradedqualityDmaintenanceEnables querying enterprise documents (DOCX, PDF, PPTX) using natural language, with hybrid search and MCP integration for Claude Desktop and other agents.MIT
- AlicenseNot gradedqualityAmaintenanceEnables 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
- AlicenseNot gradedqualityBmaintenanceEnables document ingestion and typed knowledge graph queries through Claude MCP tools, allowing agents to extract, store, and retrieve typed entities and relations from documents.2MIT
- AlicenseAqualityAmaintenanceConvert PDF, DOCX, HTML, and URLs into clean, LLM-ready markdown with tables preserved and boilerplate stripped, through three MCP tools (URL, local file, or raw bytes). Hosted API with no local dependencies; 50 free conversions with a self-serve key, then $0.002 per call.329 npmMIT
TDQS
Scored across 8 tools
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.
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.
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.
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.