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Textin MCP Server

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TextIn OCR MCP

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TextIn OCR MCP Server

TextIn MCP Server is a tool for extracting text and performing OCR on documents, including document text recognition, ID recognition, and invoice recognition. It also supports converting documents into Markdown format.

Tools

  • recognition_text

    • Text recognition from images, Word documents, and PDF files.

    • Inputs:

      • path (string, required): file path or a URL (HTTP/HTTPS) pointing to a document

    • Return: Text of the document.

    • Supports conversion for:

      • PDF

      • Image (Jpeg, Jpg, Png, Bmp)

  • doc_to_markdown

    • Convert images, PDFs, and Word documents to Markdown.

    • Inputs:

      • path (string, required): file path or a URL (HTTP/HTTPS) pointing to a document

    • Return: Markdown of the document.

    • Supports conversion for:

      • PDF

      • Microsoft Office Documents (Word, Excel)

      • Image (Jpeg, Jpg, Png, Bmp)

  • general_information_extration

    • Automatically identify and extract information from documents, or identify and extract user-specified information.

    • Inputs:

      • path (string, required): file path or a URL (HTTP/HTTPS) pointing to a document

      • key (string[], optional): The non-tabular text information that the user wants to identify, input format is an array of strings.

      • table_header (string[], optional): The table information that the user wants to identify, input format is an array of strings.

    • Return: The key information JSON.

    • Supports conversion for:

      • PDF

      • Microsoft Office Documents (Word, Excel)

      • Image (Jpeg, Jpg, Png, Bmp)

When the input is a URL, it does not support handling access to protected resources.

Related MCP server: MCP Docling Server

Setup

APP_ID and APP_SECRET

Click here to register for a TextIn account.

Get Textin APP_ID and APP_SECRET by following the instructions here.

NPX

{
  "mcpServers": {
    "textin-ocr": {
      "command": "npx",
      "args": [
        "-y",
        "@intsig/server-textin"
      ],
      "env": {
        "APP_ID": "<YOUR_APP_ID>",
        "APP_SECRET": "<YOUR_APP_SECRET>",
        "MCP_SERVER_REQUEST_TIMEOUT": "600000"
      },
      "timeout": 600
    }
  }
}

License

This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.

Available Tools

3 tools
doc_to_markdownB

Convert images, PDFs, and Word documents to Markdown.

ParametersJSON Schema
NameRequiredDescriptionDefault
pathYesRead the complete contents of a file from the file system.

TDQS

B3/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, and the description is minimal. It does not disclose behavioral traits such as file size limits, supported formats beyond those listed, or preservation of formatting during conversion.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence, but it lacks structure or breakdown of details. It communicates the core function without additional context, which is adequate but not exemplary.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple input schema and lack of output schema, the description should provide more context on return values, error handling, or supported file specifics. It is underspecified for complete understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There is only one parameter 'path', and its description in the schema is already clear. The tool description adds no additional meaning beyond the schema, so baseline score of 3 applies given 100% schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool converts images, PDFs, and Word documents to Markdown. It specifies the input types and output format, distinguishing it from sibling tools like 'recognition_text' which likely focus on text extraction rather than conversion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. No prerequisites, limitations, or exclusions are mentioned, leaving the agent without context for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

general_information_extrationC

Automatically and intelligently extract key information from documents.

ParametersJSON Schema
NameRequiredDescriptionDefault
pathYesRead the complete contents of a file from the file system.

TDQS

C2.4/5.0
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, and the description fails to disclose any behavioral traits such as mutation, idempotency, error handling, or output format. 'Automatically and intelligently' is uninformative.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, which is concise but overly terse. It lacks structure and fails to provide essential details, sacrificing completeness for brevity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (extracting key information), the lack of output schema, and no annotations, the description is grossly inadequate. It does not explain what information is extracted, how it works, or any limitations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the input parameter 'path' is clearly documented in the schema. The tool description adds no new meaning, meeting the baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool extracts key information from documents, which distinguishes it from sibling tools like doc_to_markdown (conversion) and recognition_text (text recognition). However, 'key information' is vague.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus its siblings. The description does not mention prerequisites, contexts, or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

recognition_textC

Text recognition from images, Word documents, and PDF files.

ParametersJSON Schema
NameRequiredDescriptionDefault
pathYesRead the complete contents of a file from the file system.

TDQS

C2.8/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, and the description only states the action without disclosing outcomes, side effects, or requirements. It does not indicate whether the tool reads and returns text or modifies files.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single concise sentence that properly front-loads the purpose. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description should explain what the tool returns (e.g., extracted text) but does not. Missing details on error handling or supported file types specifically.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the parameter description ('Read the complete contents of a file from the file system') is generic and adds little beyond the schema. It does not specify allowed file extensions or constraints beyond the tool's general purpose.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool performs text recognition from images, Word documents, and PDF files. It distinguishes from siblings by specifying input formats, though it could be more explicit about the output.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. It does not mention when not to use it or any prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 3 tool updatesv1.0.0
    • First observeddoc_to_markdown
    • First observedgeneral_information_extration
    • First observedrecognition_text
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TDQS

C2.8/5.0
Disambiguation5/5

Each tool targets a distinct document processing task: converting to Markdown, extracting key information, and recognizing text. There is no clear overlap, so an agent can easily distinguish them.

Naming Consistency2/5

Tool names mix patterns: 'doc_to_markdown' uses verb_noun, 'general_information_extration' uses adjective_noun_noun (with a misspelling), and 'recognition_text' uses noun_noun. The inconsistency and spelling error reduce coherence.

Tool Count3/5

Three tools is on the lower end for a document processing server. While it covers basic operations, the set feels thin and could benefit from additional tools like format conversion or OCR options.

Completeness3/5

The server covers core document tasks (conversion, extraction, recognition) but lacks common operations like document classification, summarization, or editing, leaving some gaps for complex workflows.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

Unclaimed servers have limited discoverability.

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