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After you connect your AI tool to the Transform MCP server, to send your files to Transform for processing, you can give your AI tool or agent a simple prompt such as the following, depending on where your files are stored: Somewhere on your local filesystem: Replace <local-paths-to-your-files> in the following prompt with the paths to your target files: 
Use the Unstructured Transform MCP server to parse the files at <local-paths-to-your-files>
Within a project: For project-based AI sessions, you can use this simple prompt instead: 
Use the Unstructured Transform MCP server to parse the attached files in the associated project.
Attached to a chat: If you have added your target files directly to an AI chat session, you can use this simple prompt instead: 
Use the Unstructured Transform MCP server to parse the attached files.
Transform will parse your files by using its default file parsing options, as described in the next section. You can change any or all of these options.

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Default file parsing options

By default, when the Transform MCP server parses your files: You can change any or all of these options, as described in the next section.

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Specify file parsing options

To learn how to specify file parsing options, after you connect your AI tool to the Transform MCP server, ask your AI tool the following: 
What are all of the available options that I can specify when asking the 
Unstructured Transform MCP server to parse my files? Give me an example 
prompt for each of these options.
The following core options are available:
OptionValues / formatDefaultExample prompt
Partitioning strategyauto, fast, hi_res, or vlmautoPartition using the hi_res strategy. Learn about partitioning.
LanguagesISO-639 language codesNo language biasBias for OCR for en, es, and fr.
Page rangesOne or more page rangesAll pagesParse only pages 1 through 5 and page 10.
Enrichments (must specify hi_res partitioning first)image_description, table_description, table_to_html, ner, and generative_ocrNo enrichmentsAdd the table_to_html and image_description enrichments. Learn about enrichments.
Chunking strategychunk_by_character, chunk_by_title, chunk_by_page, or chunk_by_similarity, with chunking options specified (see the next section)No chunkingSplit into chunks based on character counts. Learn about chunking.
EmbeddingAn available provider and model (see later on this page)openai and text-embedding-3-smallLearn about embedding.
Output formatmd, json, html, txtmdReturn the results as md.
Only the json output format produces Unstructured document elements and metadata.
For chunking, the following options are available. Learn about chunking.
OptionApplies toExample prompt
combine_text_under_n_charschunk_by_title onlyCombine multiple elements into the current chunk if possible, with a maximum chunk length of 800 characters.
max_charactersAllNo chunk may ever exceed 800 characters.
new_after_n_charsAll but chunk_by_similarityClose the current element after reaching 800 characters.
overlapAll but chunk_by_similarityTake the last 100 characters of each chunk and add it to the beginning of its immediate next chunk only.
similarity_thresholdchunk_by_similarity onlyUse a similarity threshold of 0.75.
For available embedding provider and model options, enter the following prompt: 
For the Unstructured Transform MCP server, for embeddings, which available embedding provider and model combinations can I specify?

For each available model, list the number of dimensions that it supports. Use the format ([number] dimensions).

List the default embedding provider and model combination, if I do not otherwise specify one. Give me an example prompt phrase so that I can specify one of these combinations in an ideal way.
Learn about embedding.

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File parsing prompt examples

Additional partitioning strategy prompt examples:
  • Partition using the default auto strategy.
  • Use fast partitioning.
  • Use hi_res parsing.
  • Use vlm parsing.
Additional enrichment prompt examples:
  • Add image descriptions for figures and diagrams.
  • Add table descriptions for the tables.
  • Convert tables to structured HTML.
  • Extract named entities and their relationships.
  • Use generative OCR because the document has handwriting.
Additional chunking strategy prompt examples:
  • Chunk by title, combining any sections under 200 characters into the next one.
  • Split this into chunks of 1000 characters each, with a 100-character overlap between chunks.
  • Chunk this by similarity, starting a new chunk whenever the similarity threshold drops below 0.75.
  • Chunk by character, starting a new chunk after 1500 characters, but cap each chunk at 2000 characters maximum.
Additional embedding prompt examples:
  • Embed this using OpenAI's text-embedding-3-small model.
  • Embed these contracts using Voyage AI's voyage-law-2 model.
  • Chunk this by title, then embed it with IBM's all-minilm-l6-v2 model.
Additional output format prompt examples:
  • Return the parsed results as Markdown.
  • Return the parsed results as JSON.
  • Return the parsed results as HTML.
  • Return the parsed results as plain text.
Complete example prompts:
  • Partition in hi-res mode, optimizing for French and English. Enrich it with table-to-HTML and generative OCR. Chunk it by title, combining sections under 500 characters. Embed it with Voyage AI's voyage-3-large model. Give me the final output as JSON.
  • Use hi-res partitioning on this contract, pages 1 through 5 only. Run named entity recognition to tag parties and dates. Chunk it by character with a max of 1200 characters and a 150-character overlap. Embed using Voyage AI's voyage-law-2 model. Return the result as Markdown.
  • Partition this technical manual with the VLM strategy. Enrich it with image descriptions and table descriptions. Chunk it by page. Embed with OpenAI's text-embedding-3-large model. Output it as HTML.
  • Partition these plain text files using the fast strategy. Skip enrichments. Chunk it by similarity with a threshold of 0.75. Embed it using OpenAI's text-embedding-3-small model. Give me plain text output.
  • Partition these filings in hi-res mode, pages 1 through 30 only. The filings are in German. Enrich with table-to-HTML and table descriptions. Chunk it by title, capping chunks at 1000 characters. Embed it with Voyage AI's voyage-finance-2 model. Return the output as JSON, keeping full Base64 for image elements.

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Next steps

  • Transform quickstart: Install the Unstructured Transform MCP server, drag and drop files, and have Transform start producing partitioned and enriched data based on your files in minutes.
  • Control Transform generated sample code: Control how the Unstructured Transform MCP server generates sample curl or Python code that demonstrates how to use Transform to partition, enrich, chunk, and embed the data based on your files.