What can you do with this server?

This server converts JSON data into CSV format (importable into Excel) using two tools: * Convert JSON strings to CSV (json_to_excel_mcp_from_data): Pass a raw JSON array or object directly as a string and receive a CSV-formatted string ready for Excel import * Convert JSON from a URL to CSV (json_to_excel_mcp_from_url): Fetch a JSON file from a publicly accessible URL and convert it to CSV * Handle various JSON structures: JSON arrays (each object becomes a row) and single JSON objects (converted to key-value pairs) * Customize conversion options: Choose between flat or nested JSON modes, set delimiter characters (., _, __, /) for nested keys, and control maximum depth (1–20 or unlimited) * Automatic data type handling: Numbers, booleans, strings, arrays, and objects are all handled automatically * Free and Pro versions: Free version processes up to 6 rows; a valid Pro Code unlocks unlimited row processing * Error handling: Descriptive errors for invalid JSON, network issues, file not found, and invalid Pro Codes

How do I use JSON to Excel MCP by WTSolutions?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@JSON to Excel MCP by WTSolutions convert this JSON data to CSV: [{"name": "Alice", "age": 28}, {"name": "Bob", "age": 35}]" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

JSON to Excel MCP by WTSolutions

by he-yang

Overview Schema Related Servers Score Discussions

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JSON to Excel MCP by WTSolutions

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Introduction

The JSON to Excel MCP (Model Context Protocol) provides a standardized interface for converting JSON data into CSV format string using the Model Context Protocol. This MCP implementation offers two specific tools for data conversion:

json_to_excel_mcp_from_data: Converts JSON data string into CSV format.
json_to_excel_mcp_from_url: Converts JSON file from a provided URL (.json format) into CSV format string.

JSON to Excel MCP is part of JSON to Excel toolkit by WTSolutions:

JSON to Excel Web App: Convert JSON to Excel directly in Web Browser.
JSON to Excel Excel Add-in: Convert JSON to Excel in Excel, works with Excel environment seamlessly.
JSON to Excel WPS Add-in: Convert JSON to Excel in WPS, works with WPS environment seamlessly.
JSON to Excel API: Convert JSON to Excel by HTTPS POST request.
JSON to Excel MCP Service: Convert JSON to Excel by AI Model MCP SSE/StreamableHTTP request. (<-- You are here.)

Related MCP server: Excel to JSON MCP by WTSolutions

Server Config

Available MCP Servers (SSE and Streamable HTTP):

Using Stdio (NPX)

Server Config JSON:

Case 1 : Free Version (Up to processing 6 Rows of data)

If you are using the free version:

{
  "mcpServers": {
    "json-to-excel-mcp": {
      "args": [
        "json-to-excel-mcp"        
      ],
      "command": "npx"
    }
  }
}

Case 2 : Pro Version

If you are using the pro version (with a valid proCode):

{
  "mcpServers": {
    "json-to-excel-mcp": {
      "args": [
        "json-to-excel-mcp"        
      ],
      "command": "npx",
      "env": {
        "proCode": "type in your proCode here"
      }
    }
  }
}

Using SSE

Not supported starting from v0.3.0

Using Streamable HTTP

Not supported starting from v0.3.0

MCP Tools

json_to_excel_mcp_from_data

Converts JSON data string into CSV format string.

Parameters

Parameter	Type	Required	Description
data	string	Yes	JSON data string to be converted to CSV. Must be a valid JSON array or object.
options	object	No	Optional configuration object for customizing the conversion process.

Note:
Input data must be a valid JSON string. JSON schema available at JSON Schema and validator available at JSON to Excel Web App.
If the JSON is an array of objects, each object will be treated as a row in the CSV.
If the JSON is a single object, it will be converted into a CSV with key-value pairs.
The CSV will include headers based on the keys in the JSON objects.
This tool returns CSV-formatted data that can be easily converted/imported to Excel.

Options Object

The options object can contain the following properties:

Property	Type	Default	Description
proCode	string	""	Pro Code JSON to Excel service.
jsonMode	string	"flat"	Format mode for JSON output: "nested", or "flat"
delimiter	string	"."	Delimiter character for nested JSON keys when using jsonMode: "nested", acceptable delimiters are ".", "_", "__", "/".
maxDepth	string	"unlimited"	Maximum depth for nested JSON objects when using jsonMode: "nested". For maxDepth, "unlimited", "1" ~ "20" acceptable.

Note:

If you do not have a valid Pro Code, max 6 rows will be processed.
Detailed conversion rules can be found in Features.

Example Prompt 1:

Convert the following JSON data into CSV format:

[
  {"Name": "John Doe", "Age": 25, "IsStudent": false},
  {"Name": "Jane Smith", "Age": 30, "IsStudent": true}
]

Example Prompt 2:

Convert the following JSON object into CSV format:

{
  "Name": "John Doe",
  "Age": 25,
  "IsStudent": false,
  "Courses": ["Math", "Science"]
}

json_to_excel_mcp_from_url

Converts JSON data from a provided URL into Excel data.

Parameters

Parameter	Type	Required	Description
url	string	Yes	URL pointing to a JSON file (.json)
options	object	No	Optional configuration object for customizing the conversion process.

Note:
The url should be publicly accessible.
The JSON file should be in .json format.
The JSON file should contain a valid JSON array or object. JSON schema available at JSON Schema and validator available at JSON to Excel Web App.
If the JSON is an array of objects, each object will be treated as a row in the CSV.
If the JSON is a single object, it will be converted into a CSV with key-value pairs.
This tool returns CSV-formatted data that can be easily converted/imported to Excel.

Options Object

The options object can contain the following properties:

Property	Type	Default	Description
proCode	string	""	Pro Code for JSON to Excel service.
jsonMode	string	"flat"	Format mode for JSON output: "nested", or "flat"
delimiter	string	"."	Delimiter character for nested JSON keys when using jsonMode: "nested", acceptable delimiters are ".", "_", "__", "/".
maxDepth	string	"unlimited"	Maximum depth for nested JSON objects when using jsonMode: "nested". For maxDepth, "unlimited", "1" ~ "20" acceptable.

Note:

If you do not have a valid Pro Code, max 6 rows will be processed.
Detailed conversion rules can be found in Pro Features.

Example Prompt 1

Convert JSON file to Excel, file URL: https://mcp.wtsolutions.cn/example.json

Example Prompt 2

(applicable only when you do not have a URL and working with online AI LLM)

I've just uploaded one .json file to you, please extract its URL and send it to MCP tool 'json_to_excel_mcp_from_url', for JSON to Excel conversion.

Response Format

The MCP tools return a JSON object with the following structure:

Field	Type	Description
isError	boolean	Indicates if there was an error processing the request
msg	string	'success' or error description
data	string	Converted CSV data string, '' if there was an error. This CSV data can be easily imported into Excel.

Example Success Response

{
  "content": [{
    "type": "text",
    "text": "{\"isError\":false,\"msg\":\"success\",\"data\":\"Name,Age,IsStudent\nJohn Doe,25,false\nJane Smith,30,true\"}"
  }]
}

Above is the response from MCP tool, and in most cases your LLM should interpret the response and present you with a JSON object, for example as below.

Note, different LLM models may have different ways to interpret the JSON object, so please check if the JSON object is correctly interpreted by your LLM model.

{
  "isError": false,
  "msg": "success",
  "data": "Name,Age,IsStudent\nJohn Doe,25,false\nJane Smith,30,true"
}

Example Failed Response

{
  "content": [{
    "type": "text",
    "text": "{\"isError\": true, \"msg\": \"Invalid JSON format\", \"data\": \"\"}"
  }]
}

Above is the response from MCP tool, and in most cases your LLM should interpret the response and present you with a JSON object, for example as below.

Note, different LLM models may have different ways to interpret the JSON object, so please check if the response is correctly interpreted by your LLM model.

{
  "isError": true,
  "msg": "Invalid JSON format",
  "data": ""
}

or it is also possible that your LLM would say "Invalid JSON format, please provide a valid JSON string" to you.

Data Type Handling

The API automatically handles different data types in JSON:

Numbers: Converted to numeric values in CSV
Booleans: Converted to 'true'/'false' strings
Strings: Escaped and quoted if necessary
Arrays: Converted to JSON.stringify array string
Objects: Converted to JSON.stringify object string

Error Handling

The MCP returns descriptive error messages for common issues:

Invalid JSON format: When input data is not a valid JSON string
Empty JSON data: When input data is an empty JSON string
Network Error when fetching file: When there's an error downloading the file from the provided URL
File not found: When the file at the provided URL cannot be found
Server Internal Error: When an unexpected error occurs
Pro Code Invalid: When the provided Pro Code is invalid or not subscribed
Max 6 rows processed: When no valid Pro Code is provided, only the first 6 rows of data will be processed

Service Agreement and Privacy Policy

By using JSON to Excel MCP, you agree to the service agreement, and privacy policy.

Pricing

Free version, max 6 rows processed. Pro version, unlimited data processing.

Please refer to the pricing page for more details.

Available Tools

2 tools

json_to_excel_mcp_from_dataJSON to Excel MCP by WTSolutions - from dataA

Convert JSON data to CSV data. If you do not have a Pro Code, please pass only the data parameter, and do not pass the options parameter.

ParametersJSON Schema

Name	Required	Description	Default
`data`	Yes	JSON data to be converted to CSV
`options`	No	If you do not have a Pro Code, please do not pass the options parameter in the request.

TDQS

A4.1/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses a key behavioral trait: the requirement for a Pro Code to use the options parameter, which is crucial for access control. However, it lacks details on output format (e.g., CSV structure, error handling, or performance limits), leaving gaps in behavioral context for a mutation-like conversion tool.

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?

The description is highly concise and front-loaded: two sentences with zero waste. The first sentence states the core purpose, and the second provides critical usage guidance. Every sentence earns its place by delivering essential information efficiently.

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

Completeness3/5

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

Given the tool's complexity (conversion with conditional parameters) and lack of annotations and output schema, the description is partially complete. It covers access control and basic usage but misses details on output behavior (e.g., CSV format, error cases) and doesn't fully compensate for the absence of structured output information, leaving some contextual gaps.

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?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by reiterating the Pro Code constraint for the options parameter, but it doesn't provide additional semantic context (e.g., examples of JSON input or CSV output). Baseline 3 is appropriate as the schema does the heavy lifting.

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's purpose: 'Convert JSON data to CSV data.' This is a specific verb ('Convert') and resource ('JSON data to CSV data'), and it distinguishes from the sibling tool 'json_to_excel_mcp_from_url' by specifying it works 'from data' rather than from a URL. The title reinforces this distinction.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool vs. alternatives: 'If you do not have a Pro Code, please pass only the data parameter, and do not pass the options parameter.' This clearly defines usage constraints based on user permissions, helping the agent avoid errors by specifying parameter exclusions for non-Pro users.

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

json_to_excel_mcp_from_urlJSON to Excel MCP by WTSolutions - from urlB

Convert JSON data from publicly accessible URL(.json format) to CSV data. If you do not have a Pro Code, please pass only the url parameter, and do not pass the options parameter.

ParametersJSON Schema

Name	Required	Description	Default
`url`	Yes	Publicly accessible URL of the JSON file
`options`	No	If you do not have a Pro Code, please do not pass the options parameter in the request.

TDQS

B3.3/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions the Pro Code requirement, which is useful behavioral context about authentication/access control. However, it doesn't disclose other important traits: whether this is a read-only or mutation operation, rate limits, error handling, what happens with invalid URLs, or output format details beyond 'CSV data.' For a tool that processes external data with no annotation coverage, this is insufficient.

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

Conciseness4/5

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

The description is appropriately concise with two sentences that each serve a clear purpose: the first states the core functionality, and the second provides important usage guidance. It's front-loaded with the main purpose. However, the second sentence could be slightly more polished (e.g., 'If you do not have a Pro Code, only pass the url parameter and omit options').

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 tool's complexity (processing JSON from URLs with configuration options), no annotations, no output schema, and the description's limited behavioral disclosure, this is incomplete. The description doesn't explain what the CSV output looks like, how nested JSON is handled by default, error conditions, or performance characteristics. For a data conversion tool with multiple configuration options, more context is needed.

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?

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds minimal value beyond the schema: it reinforces the Pro Code guidance for the options parameter but doesn't provide additional meaning about parameter interactions, default behaviors, or practical examples. With comprehensive schema coverage, the baseline 3 is appropriate.

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's purpose: 'Convert JSON data from publicly accessible URL(.json format) to CSV data.' This specifies the verb (convert), resource (JSON data from URL), and output format (CSV). However, it doesn't explicitly differentiate from its sibling tool 'json_to_excel_mcp_from_data' beyond the 'from url' aspect in the name/title.

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

Usage Guidelines4/5

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

The description provides clear guidance on when to use specific parameters: 'If you do not have a Pro Code, please pass only the url parameter, and do not pass the options parameter.' This gives explicit context for parameter usage based on user status. However, it doesn't explain when to use this tool versus its sibling tool or other alternatives.

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.

2 tool updatesDec 29, 2025
- First observedjson_to_excel_mcp_from_data
- First observedjson_to_excel_mcp_from_url

Install Server

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TDQS

A3.7/5.0

Disambiguation5/5

The two tools have clearly distinct purposes: one processes JSON data directly, while the other fetches JSON from a URL before conversion. There is no overlap or ambiguity between them.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern with 'json_to_excel_mcp_from_' prefix, differentiated by 'data' and 'url' suffixes. The naming is perfectly uniform and predictable.

Tool Count3/5

Two tools are borderline for a JSON-to-Excel conversion server, as it feels thin but covers the basic input methods (direct data and URL). It's reasonable but could benefit from additional tools like format customization or batch processing.

Completeness4/5

The server covers the core conversion functionality from JSON to CSV (implied Excel format) for both data and URL inputs, with minor gaps such as lack of output format options or error handling tools, but agents can work around these.

Maintenance

ActivityInactive

ResponsivenessUnresponsive

Resources

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JSON to Excel MCP by WTSolutions

Introduction

Server Config

Using Stdio (NPX)

Using SSE

Using Streamable HTTP

MCP Tools

json_to_excel_mcp_from_data

Parameters

Options Object

Example Prompt 1:

Example Prompt 2:

json_to_excel_mcp_from_url

Parameters

Options Object

Example Prompt 1

Example Prompt 2

Response Format

Example Success Response

Example Failed Response

Data Type Handling

Error Handling

Service Agreement and Privacy Policy

Pricing

Available Tools

TDQS

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

Excel Reader Server

Excel to JSON MCP by

JXLS Excel Template Generator

mcp-xlsx-server

Appeared in Searches

Latest Blog Posts

MCP directory API