flatmark
Server Details
Document to Markdown MCP server: PDF, Word, PowerPoint, Excel and HTML, with OCR for large files.
- Status
- Healthy
- Uptime
- 100.0% over the last 24 hours
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
- Repository
- flatmark-dev/flatmark
- GitHub Stars
- 0
- Server Listing
- flatmark
- ChatGPT
- –
- Claude
- –
TDQS
Scored across 3 tools
convert_document and submit_conversion_job both convert documents, so there is surface overlap, but the descriptions draw a crisp line between synchronous conversion (≤8 MB, no OCR) and queued conversion with OCR/tables, and explicitly cross-reference each other. get_conversion_job is unambiguously the polling counterpart to submit_conversion_job.
All three names follow a clean verb_noun snake_case pattern: convert_document, submit_conversion_job, get_conversion_job. The job-related pair shares the 'conversion_job' noun, making the workflow legible at a glance.
Three tools is a tight, well-scoped set for a document conversion service: one synchronous entry point and an async submit/poll pair. Every tool earns its place with no filler or redundancy.
The core conversion lifecycle (sync convert, queue job, poll status, webhook callback) is fully covered, including error and result-expiry reporting. Minor gaps exist for ancillary operations like canceling a job or listing/deleting past jobs, but agents can work around these.
Available Tools
3 toolsconvert_documentConvert a document to Markdown in one callARead-onlyInspect
Convert a document up to 8 MB to Markdown and return it directly.
Pass the file as base64 in content_base64, or as a download link in
file, and its name in filename. The extension decides the type: .pdf,
.docx, .pptx, .xlsx, .html, .htm or .txt. Returns markdown and meta
(filename, length in characters). PDFs come back as plain text
without headings and without OCR. For scans, tables and page numbers,
use submit_conversion_job. Works without an API key at the anonymous
rate limit.
Credits: 1 per call.
Inputs: filename (required), file, content_base64. Returns: object
get_conversion_jobCheck a queued conversionARead-onlyInspect
Check a queued conversion job you submitted.
Returns job_id and status (queued, running, succeeded or
failed). A succeeded job includes the markdown when it is under
100 KB, otherwise a result_url to download it (also free). A failed job
includes error, one sentence with the reason. Results are deleted
7 days after submission. An expired result is reported in error.
Requires an API key.
Credits: free. This tool is not metered.
Inputs: job_id (required). Returns: object
submit_conversion_jobQueue a document for conversion with OCR and tablesAInspect
Queue a document up to 25 MB and 200 pages for conversion with OCR and tables.
Pass the file as base64 in content_base64, or as a download link in
file, and its name in filename (.pdf, .docx, .pptx, .xlsx, .html,
.htm or .txt). Returns job_id, status and result_url. Call
get_conversion_job with the job_id until status is succeeded or
failed. A conversion may take up to about 2 minutes.
Optional webhook_url: a public http or https URL that receives a POST
with the job's final state. The request carries
X-Appkit-Signature: sha256=<hex>, an HMAC-SHA256 of the raw body keyed
with the returned webhook_secret (shown only once). 3 delivery attempts.
Requires an API key. The credits are refunded if the job fails. The uploaded file is deleted when the job finishes. Results are kept for 7 days after submission.
Credits: 10 per call.
Inputs: filename (required), file, webhook_url, content_base64. Returns: object
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Related MCP Connectors
Document-to-Markdown MCP server — convert PDF, Office and HTML into LLM-ready Markdown.
Document conversion MCP server: PDF to Markdown, image OCR, spreadsheet parsing.
PDF, Word, PowerPoint, Excel, HTML and EPUB to clean Markdown for RAG, with OCR. Pay per document.
PDF, Word, PowerPoint and Excel to clean Markdown plus every table as JSON rows, OCR for scans.
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