Reducto MCP Server

The Reducto MCP server connects AI agents directly to Reducto. Once installed, agents in Claude Desktop, Claude Code, Codex, Cursor, VS Code, Windsurf, or any other Model Context Protocol client can classify, parse, extract, split, and edit documents as part of their reasoning loop, without custom integration code.

Agent quick path

If you are setting this up for a coding agent, choose local MCP for local files and hosted MCP for public URLs.

uvx mcp-server-reducto --login
claude mcp add -s user reducto -- uvx mcp-server-reducto

[mcp_servers.reducto]
command = "uvx"
args = ["mcp-server-reducto"]

{
  "mcpServers": {
    "reducto": {
      "type": "http",
      "url": "https://mcp.reducto.ai/mcp",
      "headers": {
        "Authorization": "Bearer your-api-key"
      }
    }
  }
}

After setup, ask the agent to call:

parse_document(document_url="https://cdn.reducto.ai/samples/fidelity-example.pdf")

For local files, ask the agent to call upload_file("./document.pdf") first, then pass the returned reducto:// URL to parse_document.

What is MCP?

The Model Context Protocol is an open standard that lets AI agents call external tools. An MCP server exposes a set of tools (here, Reducto’s APIs) that the agent can invoke as part of its reasoning loop. You install the server once, point your client at it, and the agent figures out which tool to call and how to chain results. If you’ve never used MCP before, the mental model is simple: you describe what you want in plain English, and the agent decides which Reducto tool to call, when to upload a file, and how to pass results between steps.

When to use the MCP server

Use the MCP server when you want an agent to:

Answer questions about a PDF, spreadsheet, or scanned document inside Claude Desktop or another chat client.
Read documents and write code against the result inside Claude Code, Cursor, or VS Code Copilot.
Run multi-step workflows (parse, then extract, then split) without you writing glue code.
Prototype a Reducto integration by letting the agent show you the right API calls, schemas, and response shapes.

For non-agent workflows (scripting, batch jobs, CI), use the Reducto CLI or one of the SDKs instead.

Quick Start

Pick one of two options. The hosted server is fastest to set up. The local server lets agents read files directly from your machine.

Option A: Hosted server (no install)

The hosted server runs at https://mcp.reducto.ai/mcp. Add this block to your MCP client config and replace your-api-key with a key from studio.reducto.ai/api-keys:

{
  "mcpServers": {
    "reducto": {
      "type": "http",
      "url": "https://mcp.reducto.ai/mcp",
      "headers": {
        "Authorization": "Bearer your-api-key"
      }
    }
  }
}

The hosted server runs in the cloud, so it cannot read files from your local filesystem. upload_file only accepts public URLs through the hosted server. To upload local files directly, use Option B.

Option B: Local server (runs on your machine)

The local server runs via uvx and accepts local file paths, public URLs, and reducto:// references.

Install uv

curl -LsSf https://astral.sh/uv/install.sh | sh

Authenticate

uvx mcp-server-reducto --login

This opens an OAuth device-code flow in your browser. Once approved, your API key is saved to ~/.reducto/config.yaml with chmod 600.If you already use the Reducto CLI, you’re authenticated already. The MCP server reads from the same credential file.

Add to your MCP client

{
  "mcpServers": {
    "reducto": {
      "command": "uvx",
      "args": ["mcp-server-reducto"]
    }
  }
}

No API key in the config: the server reads it from ~/.reducto/config.yaml automatically.

To pass the key explicitly instead (for CI or shared machines), use the env field:

{
  "mcpServers": {
    "reducto": {
      "command": "uvx",
      "args": ["mcp-server-reducto"],
      "env": { "REDUCTO_API_KEY": "your-api-key" }
    }
  }
}

Hosted vs local

	Hosted (`mcp.reducto.ai`)	Local (`uvx mcp-server-reducto`)
Install	None	Python 3.11+, `uvx`
Auth	Bearer token in headers	Browser login or env var
Local file uploads	Public URLs only	Local paths supported
Shares creds with `reducto` CLI	No	Yes
Best for	Web/Studio users, quick demos	Desktop agents, local workflows

Client Setup

Most MCP clients use the same JSON shape, but the config file location differs.

Claude Desktop

Edit the config file at ~/Library/Application Support/Claude/claude_desktop_config.json on macOS, or %APPDATA%\Claude\claude_desktop_config.json on Windows:

{
  "mcpServers": {
    "reducto": {
      "command": "uvx",
      "args": ["mcp-server-reducto"]
    }
  }
}

Restart Claude Desktop after saving. Reducto appears in the MCP servers list.

Claude Code

Add to your project’s .mcp.json to share the server with collaborators, or run claude mcp add -s user reducto -- uvx mcp-server-reducto to enable it across all projects (stored in ~/.claude.json):

{
  "mcpServers": {
    "reducto": {
      "command": "uvx",
      "args": ["mcp-server-reducto"]
    }
  }
}

Codex

Codex uses TOML, not JSON. Edit ~/.codex/config.toml (or a project-scoped .codex/config.toml) to add the local server:

[mcp_servers.reducto]
command = "uvx"
args = ["mcp-server-reducto"]

To use the hosted server instead, configure it as a Streamable HTTP server. Set REDUCTO_API_KEY in your environment first, then add:

[mcp_servers.reducto]
url = "https://mcp.reducto.ai/mcp"
bearer_token_env_var = "REDUCTO_API_KEY"

You can also add the local server via the CLI:

codex mcp add reducto -- uvx mcp-server-reducto

Cursor

Add to .cursor/mcp.json in your project root:

{
  "mcpServers": {
    "reducto": {
      "command": "uvx",
      "args": ["mcp-server-reducto"]
    }
  }
}

VS Code (Copilot)

Add to .vscode/mcp.json in your project root:

{
  "servers": {
    "reducto": {
      "command": "uvx",
      "args": ["mcp-server-reducto"]
    }
  }
}

Windsurf

Add to ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "reducto": {
      "command": "uvx",
      "args": ["mcp-server-reducto"]
    }
  }
}

HTTP transport (self-hosted)

To run the server in HTTP mode for shared use on a single machine:

REDUCTO_API_KEY=your-key \
REDUCTO_MCP_TRANSPORT=http \
REDUCTO_MCP_PORT=8000 \
uvx mcp-server-reducto

Connect MCP clients to http://your-host:8000/mcp.

Concepts

A few small ideas to internalize before reading the tool reference. Most chained operations rely on these.

Document URL schemes

Every document_url parameter accepts one of four schemes:

Scheme	Meaning	Where it comes from
`https://` / `http://`	A public URL Reducto can fetch	You provide it
`reducto://`	A file in Reducto’s temporary storage (24-hour TTL)	Returned by `upload_file`
`jobid://`	A reference to a previous processing job	Returned by `parse_document`, `extract_data`, `split_document`, `classify_document`, `edit_document`

Other schemes (s3://, file://, raw paths) fail validation. Use upload_file to bring local files or other URLs into Reducto.

Job chaining with `jobid://`

Every processing tool returns a job_id. Pass jobid://<job_id> as document_url to a follow-up tool to reuse the work, with no re-upload and no re-parse:

upload_file("./report.pdf")            →  reducto://abc       (file_id)
parse_document("reducto://abc")        →  jobid://xyz123      (job_id)
extract_data("jobid://xyz123", ...)    →  reuses parsed text
split_document("jobid://xyz123", ...)  →  reuses parsed text

This is the cheapest and fastest way to run multiple operations against the same document.

Response shape

Every tool returns a JSON object with a consistent set of fields. A representative parse response:

{
  "job_id": "xyz123",
  "duration_seconds": 4.1,
  "studio_link": "https://studio.reducto.ai/jobs/xyz123",
  "usage": { "num_pages": 12, "credits": 12 },
  "num_blocks": 42,
  "block_type_counts": { "Text": 30, "Table": 6, "Figure": 6 },
  "blocks": [],
  "next_steps": "Use jobid://xyz123 as document_url for extract_data, split_document, or classify_document."
}

Common fields:

job_id: pass to get_job later, or chain via jobid://<job_id>.
studio_link: open in Reducto Studio to inspect the result visually.
usage: pages and credits consumed.
next_steps: a hint the server adds describing the recommended follow-up call.

Large or async results

When a result is too large to inline, the response contains a URL-backed payload:

{
  "job_id": "xyz123",
  "result_type": "url",
  "result_url": "https://...",
  "result_access_warning": "Result content is URL-backed; call get_job(job_id='xyz123') before reading it."
}

When result_type is "url", call get_job(job_id=...) to fetch the materialized result before reading any fields. Do not read result directly.

Response truncation

If a response (typically the blocks array from parse_document) exceeds REDUCTO_MCP_MAX_RESPONSE_SIZE (defaults to 50000 characters), the server truncates it and adds:

{
  "truncated": true,
  "truncation_note": "Response truncated (showing 20 of 150 blocks). Use get_job(job_id='xyz123') for full results, or narrow with page_range."
}

When truncated is true, call get_job(job_id=...) for the full result, or re-run with a narrower page_range.

The `options` escape hatch

Most tools accept an options parameter for any Reducto API field not exposed as a top-level argument.

Top-level params win. When a top-level argument and options set the same key, the top-level value takes precedence. Nested config dicts get a one-level shallow merge.
JSON strings are accepted. Some MCP clients struggle to pass nested JSON. options, schema, and categories therefore each accept either a native object or a JSON-encoded string. Both work identically.

Page range syntax

page_range accepts a string with 1-based page numbers:

"1-5": pages 1 through 5
"3,7,10-12": pages 3, 7, 10, 11, and 12

Tools

Which tool to use

If you need to…	Use
Look up working SDK or REST examples for any Reducto endpoint	`get_documentation`
Bring a local file or arbitrary URL into Reducto	`upload_file`
Get text, tables, figures, and layout from a document	`parse_document`
Pull specific fields into JSON using a schema	`extract_data`
Divide a document into named page-range sections	`split_document`
Categorize a document into one of N labels	`classify_document`
Fill a form or modify a PDF or DOCX	`edit_document`
Fetch a full, URL-backed, truncated, or async result	`get_job`
Inspect recent jobs	`list_jobs`

`get_documentation`

Returns Reducto SDK and REST documentation, including install commands, auth setup, working code examples, and response shapes for a specific topic. Call this before writing any Reducto integration code so the agent works from the current API surface, not training memory.

Name	Type	Required	Description
`topic`	string	Yes	One of: `quickstart`, `parse`, `extract`, `split`, `classify`, `edit`, `upload`, `auth`
`language`	string	No	`node`, `python`, or `http`. Omit to receive all three.

`parse_document`

Parses a document into structured text, tables, and figures. Supports PDFs, images, spreadsheets, DOCX, PPTX, and 30+ other formats.

Name	Type	Required	Description
`document_url`	string	Yes	`https://`, `reducto://`, or `jobid://` URL
`table_output_format`	string	No	`html`, `json`, `md`, `csv`, `dynamic`, `jsonbbox` (defaults to `dynamic`)
`page_range`	string	No	e.g. `"1-5"` or `"3,7,10-12"` (1-based)
`chunk_mode`	string	No	`disabled`, `variable`, `section`, `page`. See parse reference.
`agentic`	list[string]	No	Subset of `["text", "table", "figure", "layout"]`. Improves quality on hard documents (handwriting, complex tables) at higher latency.
`add_page_markers`	bool	No	Insert page-boundary markers in the output
`return_images`	list[string]	No	Subset of `["figure", "table", "page"]` to return as images
`options`	dict / JSON string	No	Any other `ParseOptions` field

`extract_data`

Extracts structured data from a document using a JSON schema.

Name	Type	Required	Description
`document_url`	string	Yes	Pass `jobid://<parse_job_id>` to skip re-parsing
`schema`	dict / JSON string	Yes	JSON Schema describing the target fields
`system_prompt`	string	No	Custom extraction instructions
`citations`	bool	No	Include source references for each extracted field
`array_extract`	bool	No	Deprecated flag for repeating items. Prefer `deep_extract`, or model the array directly in your schema.
`deep_extract`	bool	No	Iterative agentic refinement for harder documents
`include_images`	bool	No	Add page images to the LLM context
`page_range`	string	No	Limit pages to process
`options`	dict / JSON string	No	Any other `ExtractOptions` field

`split_document`

Segments a document into labeled sections by topic. Returns each section’s name, page range, and a confidence score.

Name	Type	Required	Description
`document_url`	string	Yes	Document to split
`categories`	list / JSON string	Yes	`[{"name": "Disclosures", "description": "Legal and risk disclosures"}, ...]`
`split_rules`	string	No	Natural-language splitting guidance
`page_range`	string	No	Limit pages
`options`	dict / JSON string	No	Any other `SplitOptions` field

`classify_document`

Categorizes a document into one of the provided categories.

Name	Type	Required	Description
`document_url`	string	Yes	Document to classify
`categories`	list / JSON string	Yes	`[{"category": "invoice", "criteria": ["has billing info", "has line items"]}, ...]`
`page_range`	string	No	Defaults to first 5 pages
`document_metadata`	string	No	Additional context to bias classification

`edit_document`

Fills forms or modifies a document (PDF or DOCX). Returns a download URL for the edited file plus a form_schema describing the fields it found. The first edit on a new form returns a form_schema and a hint:

{
  "document_url": "https://.../edited.pdf",
  "form_schema": {},
  "form_schema_note": "Cache this form_schema and pass it via options.form_schema for repeated edits."
}

For repeated edits to the same form template, cache form_schema and pass it back via options.form_schema to skip re-detection.

Name	Type	Required	Description
`document_url`	string	Yes	Document to edit
`edit_instructions`	string	Yes	Natural-language instructions
`options`	dict / JSON string	No	`edit_options`, `form_schema`, `priority`

`upload_file`

Uploads a document to Reducto’s temporary storage (24-hour TTL) and returns a reducto:// URL for use in other tools. Accepts:

Local file paths: ./report.pdf, /Users/me/docs/x.pdf, ~/inbox/y.pdf (local server only).
Public URLs: https://example.com/report.pdf (downloaded server-side, then uploaded).

The hosted server (mcp.reducto.ai) does not support local paths. Use a public URL or run the server locally.

Name	Type	Required	Description
`file_url`	string	Yes	Local file path or public URL

`get_job`

Returns the status and result of a previous processing job. Use this to fetch URL-backed results, full content for truncated responses, or to poll an async job.

Name	Type	Required	Description
`job_id`	string	Yes	Job ID returned by any processing tool

`list_jobs`

Returns recent processing jobs.

Name	Type	Required	Description
`limit`	int	No	Max jobs to return (defaults to `10`)

Usage Patterns

Basic parse

“Parse this PDF and show me the content.”

The agent calls parse_document(document_url="https://example.com/report.pdf").

Extract with a schema

“Extract the invoice number, date, and total from this document.”

The agent calls extract_data with a JSON schema describing the three fields.

End-to-end chain

A full workflow using upload_file, parse_document, and chained extract_data:

1. upload_file("./contracts/q4-msa.pdf")
   → { "file_id": "reducto://abc", "next_steps": "Pass reducto://abc as document_url ..." }

2. parse_document("reducto://abc", agentic=["text"])
   → { "job_id": "xyz123", "studio_link": "...", "next_steps": "Use jobid://xyz123 ..." }

3. extract_data(
     "jobid://xyz123",
     schema={
       "type": "object",
       "properties": {
         "effective_date": {"type": "string"},
         "parties": {"type": "array", "items": {"type": "string"}},
         "termination_clauses": {"type": "array", "items": {"type": "string"}}
       },
       "required": ["effective_date", "parties"]
     },
     citations=True
   )
   → structured fields, with citations

The same jobid://xyz123 can also be reused by split_document or classify_document without re-parsing.

Triage a mixed document set

“Classify each of these PDFs as invoice, contract, or lab_report, then run the right extraction schema for each.”

The agent calls classify_document per file, then routes each to extract_data with a category-specific schema.

Fill a recurring form

“Fill out a W-9 for each of these vendors using the data in vendors.json.”

The first call to edit_document returns a form_schema. Subsequent calls pass the cached form_schema via options to skip re-detection.

Handle large or async results

If a response includes result_type: "url", call get_job before reading fields:

parse_document("reducto://big-doc")
  → { "job_id": "j1", "result_type": "url", "result_url": "...",
      "result_access_warning": "Result content is URL-backed; call get_job(job_id='j1') before reading it." }
get_job(job_id="j1")
  → full materialized result

If a response includes truncated: true, either call get_job(job_id=...) for the full result or re-run with a narrower page_range.

Authentication

The server resolves API keys in this order:

REDUCTO_API_KEY environment variable (highest priority). Useful for CI or explicit config.
~/.reducto/config.yaml, the shared credential store with the Reducto CLI.

Three ways to authenticate:

# Browser login (recommended)
uvx mcp-server-reducto --login

# Reuse an existing CLI session
reducto login

# Set the env var directly
export REDUCTO_API_KEY=your-key

--login runs an OAuth device-code flow: it prints a code, opens your browser to the verification page, and waits for you to approve. Once approved, the key is written to ~/.reducto/config.yaml with chmod 600. Use --login --force to replace an existing saved key. The hosted server (mcp.reducto.ai) does not use this flow. Pass your key as Authorization: Bearer <key> in your client config instead.

Environment Variables

Variable	Required	Default	Description
`REDUCTO_API_KEY`	No*	none	API key (or authenticate via `--login`)
`REDUCTO_BASE_URL`	No	`https://platform.reducto.ai`	Override the API base URL (EU region, on-prem)
`REDUCTO_MCP_MAX_RESPONSE_SIZE`	No	`50000`	Response truncation threshold in characters
`REDUCTO_MCP_TIMEOUT`	No	`300`	Request timeout in seconds
`REDUCTO_MCP_TRANSPORT`	No	`stdio`	Transport mode: `stdio` or `http`
`REDUCTO_MCP_PORT`	No	`8000`	Port when using HTTP transport
`REDUCTO_TELEMETRY`	No	`1`	Set to `0` to opt out of anonymous usage telemetry

*Required only if you have not run mcp-server-reducto --login or reducto login.

Telemetry

The MCP server sends a small amount of anonymous usage telemetry to PostHog so the team can prioritize improvements. Collected:

Lifecycle events: mcp.installed (once per machine, on first run) and mcp.start (once per server boot).
Per-tool invocation events: tool.<name>.invoked with tool name, status (ok, error, or exception), and latency_ms.
Environment fingerprint on every event: client name and version, transport (stdio or hosted), Python version, OS platform.

Never collected:

Tool arguments (document URLs, file IDs, schemas, prompts, page ranges).
Tool responses or document content.
API keys. The user identifier on each event is sha256(api_key)[:16], which is one-way.

To opt out, set REDUCTO_TELEMETRY=0 in the environment that runs the MCP server.

Debugging

MCP Inspector

Test the server interactively:

npx @modelcontextprotocol/inspector -- uvx mcp-server-reducto

This opens a web UI where you can discover tools, call them, and inspect responses. Requires prior authentication via --login.

Studio links

Most parse and extract responses include a studio_link. Open it to inspect the same job interactively in Reducto Studio. This is the fastest way to compare parser output side by side with the original document.

Logs

The server logs to stderr. Stdout is reserved for the MCP transport. Set LOG_LEVEL=DEBUG for verbose output.

Troubleshooting

No Reducto API key found

Problem: The server starts but every tool call fails with an authentication error.Solution: Run mcp-server-reducto --login, or set REDUCTO_API_KEY in your client config’s env block.

Invalid URL scheme

Problem: A tool call fails with a validation error about document_url.Solution: document_url must start with https://, http://, reducto://, or jobid://. For local files, call upload_file first to get a reducto:// URL.

Local file paths are not supported on the hosted server

Problem: You passed a local path to upload_file while connected to mcp.reducto.ai.Solution: Switch to the local server (Option B above), or pass a public URL.

Tools not appearing in the client

Problem: After editing your client config, no Reducto tools show up.Solution: Restart your MCP client. Then test the server directly by running uvx mcp-server-reducto in your terminal to confirm it starts cleanly.

Timeout on large documents

Problem: A long-running parse or extract returns a timeout error.Solution: Increase REDUCTO_MCP_TIMEOUT (defaults to 300 seconds), or narrow the request with page_range.

Result not visible in the response

Problem: A response references content but the result field is empty or missing.Solution: Check for result_type: "url" or truncated: true. In either case, call get_job(job_id=...) to fetch the full materialized result.

Next Steps

Reducto CLI

Run parse, extract, and edit from your terminal.

Agent Guide

A self-contained API reference designed for AI coding agents.

Parse Overview

How parse turns documents into structured text, tables, and figures.

Extract Overview

Schema-driven structured data extraction.

​Agent quick path

​What is MCP?

​When to use the MCP server

​Quick Start

​Option A: Hosted server (no install)

​Option B: Local server (runs on your machine)

​Hosted vs local

​Client Setup

​Claude Desktop

​Claude Code

​Codex

​Cursor

​VS Code (Copilot)

​Windsurf

​HTTP transport (self-hosted)

​Concepts

​Document URL schemes

​Job chaining with jobid://

​Response shape

​Large or async results

​Response truncation

​The options escape hatch

​Page range syntax

​Tools

​Which tool to use

​get_documentation

​parse_document

​extract_data

​split_document

​classify_document

​edit_document

​upload_file

​get_job

​list_jobs

​Usage Patterns

​Basic parse

​Extract with a schema

​End-to-end chain

​Triage a mixed document set

​Fill a recurring form

​Handle large or async results

​Authentication

​Environment Variables

​Telemetry

​Debugging

​MCP Inspector

​Studio links

​Logs

​Troubleshooting

​Next Steps

Reducto CLI

Agent Guide

Parse Overview

Extract Overview

Agent quick path

What is MCP?

When to use the MCP server

Quick Start

Option A: Hosted server (no install)

Option B: Local server (runs on your machine)

Hosted vs local

Client Setup

Claude Desktop

Claude Code

Codex

Cursor

VS Code (Copilot)

Windsurf

HTTP transport (self-hosted)

Concepts

Document URL schemes

Job chaining with `jobid://`

Response shape

Large or async results

Response truncation

The `options` escape hatch

Page range syntax

Tools

Which tool to use

`get_documentation`

`parse_document`

`extract_data`

`split_document`

`classify_document`

`edit_document`

`upload_file`

`get_job`

`list_jobs`

Usage Patterns

Basic parse

Extract with a schema

End-to-end chain

Triage a mixed document set

Fill a recurring form

Handle large or async results

Authentication

Environment Variables

Telemetry

Debugging

MCP Inspector

Studio links

Logs

Troubleshooting

Next Steps