Official vendor OpenAPI document · agent-readyData EnrichmentData TransformationapiKey6 EndpointsREST
For Agents
Convert PDF, Word, PowerPoint, Excel, and HTML documents into Markdown in one call, or queue large files for OCR and table extraction and download the result as Markdown or JSON.
Use for: Convert a PDF to Markdown, Turn a Word document into clean Markdown, Queue a scanned document for OCR and table extraction, Download the Markdown result of a finished conversion job
Not supported: Does not handle document storage, e-signatures, or spreadsheet computation. Use for converting documents to Markdown only.
The Flatmark API converts documents into clean Markdown, taking PDF, Word, PowerPoint, Excel, and HTML files and returning agent-ready text in a single call. For large or scanned files it queues the work and runs OCR and table extraction, so complex layouts come back as structured Markdown or JSON rather than a flat dump. It also tracks each conversion as a job you can poll and download, and reports your plan, remaining requests, and remaining credits. Conversion runs on infrastructure hosted in Germany.
Install Jentic One Beta
Connect the flatmark to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the flatmark, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
1
Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fflatmark.dev%2Fflatmark" | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fflatmark.dev%2Fflatmark" | sh
jentic register # connects your agent to your Jentic One instanceJentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.
Capabilities
What an agent can do with flatmark API.
Convert a PDF, Word, PowerPoint, Excel, or HTML file to Markdown in one call
Queue a large document for conversion with OCR and table extraction
Download a finished conversion as Markdown or JSON
List your queued conversion jobs, newest first
Check a single conversion job's status by its id
Read your plan, remaining requests, and remaining credits
Use Cases
Patterns agents use flatmark API for, with concrete tasks.
★ Agent document ingestion
An AI agent connected through Jentic turns a user's uploaded PDF or Office file into Markdown before reasoning over it, calling the single-shot convert endpoint so the document arrives as clean text the model can read, without the developer building a parsing pipeline or handling each file format by hand.
Convert an uploaded PDF to Markdown with the single-call convert endpoint and return the text for the model to read
Batch OCR pipeline
A back-office tool processes scanned contracts by queuing each file for conversion with OCR and table extraction, then polling the job and downloading the Markdown once it is ready, so image-only PDFs become searchable structured text without a local OCR stack.
Queue a scanned PDF for OCR and table extraction, poll the job by its id, and download the Markdown result
RAG content preparation
A retrieval pipeline normalizes a mixed library of Word, PowerPoint, and HTML files into Markdown so every source is chunked and embedded in one consistent format, using the convert endpoint to flatten layout differences before indexing.
Convert a batch of Word and HTML files to Markdown and hand each result to the indexing step
Key Endpoints
6 endpoints — the flatmark api converts documents into clean markdown, taking pdf, word, powerpoint, excel, and html files and returning agent-ready text in a single call.
METHOD
PATH
DESCRIPTION
POST
/v1/convert
Convert a document to Markdown in one call
POST
/v1/convert/jobs
Queue a document for conversion with OCR and tables
GET
/v1/convert/jobs/{job_id}/result
Download a queued job's Markdown or JSON
GET
/v1/jobs
List your queued jobs, newest first
GET
/v1/jobs/{job_id}
Get one queued job by id
GET
/v1/me
Read your plan, remaining requests, and credits
POST
/v1/convert
Convert a document to Markdown in one call
POST
/v1/convert/jobs
Queue a document for conversion with OCR and tables
GET
/v1/convert/jobs/{job_id}/result
Download a queued job's Markdown or JSON
GET
/v1/jobs
List your queued jobs, newest first
GET
/v1/jobs/{job_id}
Get one queued job by id
GET
/v1/me
Read your plan, remaining requests, and credits
Jentic AI Readiness Score
This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.
F
flatmark
- AI-Aware (A-)73/100
93
Foundational Compliance64
Developer Experience & Jentic Compatibility73
AI-Readiness & Agent Experience94
Agent Usability50
Security81
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.2
Show dimension breakdown
93
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: A+Signals: 4
99%
Lint Results
Aggregated quality score from linter diagnostics, weighted by severity.
100%
Resolution Completeness
Percentage of `$ref` references that resolve successfully.
100%
Specification Validity
Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).
75%
Structural Integrity
Structural correctness score based on schema issues using logarithmic dampening.
64
Developer Experience & Jentic Compatibility
Clarity, completeness, and ingestion readiness for developers and tooling.
Grade: BSignals: 4
0%
Example Density
How richly the API is illustrated with examples.
100%
Example Validity
Percentage of examples that conform to their schemas.
54%
Response Coverage
Percentage of operations with complete response definitions (success, client error, server error).
100%
Tooling Readiness
Health of API ingestion, bundling, and resolution within Jentic pipelines.
73
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: A-Signals: 4
61%
Description Coverage
Coverage of descriptions across API elements.
83%
Error Standardization
Coverage of RFC 9457 Problem Details for error responses.
100%
OperationId Quality
Coverage, uniqueness, and casing consistency of operationIds for AI inference.
46%
Summary Coverage
Coverage of summaries across operations/tags/info.
94
Agent Usability
Functional utility, complexity comfort, and AI orchestration readiness.
Grade: A+Signals: 1
94%
Complexity Comfort
Agent comfort level based on API operational and structural complexity.
50
Security
Trust, risk posture, and security compliance.
Grade: C-Signals: 1
50%
Authentication Strength
Average quality of security schemes based on authentication method strength (weakest link for OAuth2).
81
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: ASignals: 1
81%
Descriptive Richness
Clarity and depth of descriptions across API elements.
Score it yourself
Every API in the directory is allowlisted, so you can re-score it with no key required.
npx @jentic/api-scorecard-cli score <openapi-url>Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring Flatmark by hand means handling its API-key header, choosing between the single-call convert path and the queued OCR job flow, and polling jobs for completion yourself. Through Jentic you install once, import Flatmark from the API Directory, store the key once, and your agent calls it.
Permission scoping
Flatmark carries the document in the request body, so rules bound which operations your agent may call, not which file. Limit it to the convert and result-download operations so it can turn documents into Markdown, and leave the account and job-listing operations out unless you add them.
Credential isolation
Your Flatmark API key is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as 'convert a document to markdown' or 'extract tables from a scanned PDF', and Jentic returns the matching Flatmark operation with its input schema so the agent calls the right endpoint without reading the reference docs.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using flatmark API through Jentic.
What authentication does the Flatmark API use?
The Flatmark API reads an API key from the `X-API-Key` header, per its OpenAPI spec. Calls made without a key run anonymously at a lower per-IP rate limit. Through Jentic the key is stored encrypted by your own Jentic One instance and injected at call time, so it never lands in the agent's context.
Is there a Flatmark MCP server?
You don't need an MCP server to give your agent Flatmark. Jentic connects it directly from the API Directory: import it, store your credential once, and your agent calls the convert and job-read operations on demand, without a separate server's tool definitions loaded into its context.
Can I limit what my agent is allowed to do with the Flatmark API?
Yes. Flatmark carries each document inside the request body, so a rule bounds which operations the agent may call rather than which file. Allow only the convert and result-download operations, and the agent can turn documents into Markdown and fetch results but cannot read your account or job history, with every call logged.
Can I extract tables from scanned PDFs with the Flatmark API?
Yes. Queue the file with the OCR conversion endpoint, which runs OCR and table extraction on large or scanned documents, then download the finished job as Markdown or JSON so tables arrive as structured text rather than images.
What are the rate limits for the Flatmark API?
The OpenAPI spec does not state a numeric ceiling, but every response carries `X-RateLimit-Limit`, `X-RateLimit-Remaining`, and `X-RateLimit-Reset` headers, and over-budget calls return a 429 with a `Retry-After` header. Read your live remaining budget from the account endpoint before a large batch.
How do I convert a document to Markdown through Jentic?
Search Jentic for 'convert a document to markdown', which returns the Flatmark convert operation with its input schema, then post your file and read the Markdown back in one call. To run it on your own infrastructure, install Jentic One from its GitHub repo.
GET STARTED