JSON Skeleton MCP Server
Enables running the MCP server directly from the GitHub repository without installation using the uvx command.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@JSON Skeleton MCP Servercreate a skeleton of my large API response file with type-only mode"
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 Skeleton MCP Server
A lightweight MCP (Model Context Protocol) server that creates compact "skeleton" representations of large JSON files, helping you understand JSON structure without the full data payload.
Features
Lightweight JSON Skeleton: Preserves structure with truncated string values
Configurable String Length: Customize max string length (default: 200 chars)
Type-Only Mode: Ultra-compact output showing only data types
Smart Array Deduplication: Keeps only unique DTO structures in arrays
Efficient Processing: Handles massive JSON files that exceed AI model context limits
Related MCP server: JSON Mapping & Context MCP Servers
Installation
Quick Start with uvx (Recommended)
You can run the MCP server directly without installation using uvx:
# Run from GitHub
uvx --from git+https://github.com/jskorlol/json-skeleton-mcp.git json-skeleton
# Run from local directory
uvx --from /path/to/json-skeleton-mcp json-skeletonTraditional Installation
Clone this repository:
git clone https://github.com/jskorlol/json-skeleton-mcp.git
cd json-skeleton-mcpCreate a virtual environment and install:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -e .Usage
As MCP Server in Claude Desktop
Add to your Claude Desktop configuration:
Using uvx (Recommended):
{
"mcpServers": {
"json-skeleton": {
"command": "uvx",
"args": ["--from", "git+https://github.com/jskorlol/json-skeleton-mcp.git", "json-skeleton"]
}
}
}Using local installation:
{
"mcpServers": {
"json-skeleton": {
"command": "uvx",
"args": ["--from", "/path/to/json-skeleton-mcp", "json-skeleton"]
}
}
}Available Tool
json_skeleton
Creates a lightweight skeleton of a JSON file with the following parameters:
file_path(required): Path to the JSON file to processmax_length(optional, default: 200): Maximum length for string valuestype_only(optional, default: false): Return only value types instead of values (most compact output)
Example 1: Basic Usage
Input: json_skeleton(file_path="/path/to/data.json")
Output: Truncated JSON with strings limited to 200 charactersExample 2: Custom String Length
Input: json_skeleton(file_path="/path/to/data.json", max_length=50)
Output: More aggressively truncated JSON with 50-char limitExample 3: Type-Only Mode (Most Compact)
Input: json_skeleton(file_path="/path/to/data.json", type_only=true)
Output:
{
"name": "str",
"age": "int",
"active": "bool",
"balance": "float",
"notes": "null",
"items": [
{
"id": "int",
"label": "str"
}
]
}Programmatic Usage
from json_skeleton import SkeletonGenerator
# Initialize generator
generator = SkeletonGenerator(max_value_length=200)
# Process a file
result = generator.process_file("large_data.json")
print(result['skeleton'])
# Process with custom length
result = generator.process_file("large_data.json", max_length=50)
print(result['skeleton'])
# Process in type-only mode
result = generator.process_file("large_data.json", type_only=True)
print(result['skeleton'])
# Or process data directly
data = {"key": "very long value" * 50, "items": [1, 2, 3, 1, 2, 3]}
skeleton = generator.create_skeleton(data)
print(skeleton)How It Works
Array Deduplication
The tool intelligently deduplicates array items by comparing their DTO (Data Transfer Object) structure:
For primitive arrays: Keeps up to 3 unique values
For object arrays: Keeps one example of each unique structure
Structure comparison is based on keys and value types, not actual values
In type-only mode: Shows only the type of the first array element
Value Processing
Normal Mode: Strings longer than max_length are truncated with "...(truncated)" suffix
Type-Only Mode: All values replaced with their type names (str, int, float, bool, null)
Numbers, booleans, and nulls are preserved as-is in normal mode
Use Cases
Understanding API Responses: Quickly grasp the structure of large API responses without processing megabytes of data
Documentation: Generate structure examples for API documentation
Development: Work with data structure without handling large payloads
Token Optimization: Reduce token usage when working with AI models
Schema Discovery: Use type-only mode to understand data types in complex JSON structures
Testing
Run the test scripts to see the tool in action:
# Test basic functionality
python test_skeleton.py
# Test with different max_length values
python test_max_length.py
# Test type-only mode
python test_type_only.pyRequirements
Python 3.10+
MCP library
License
MIT License
Available Tools
1 tooljson_skeletonA
Create a lightweight JSON skeleton that preserves structure with truncated values and deduplicated arrays. Useful when encountering 'File content exceeds maximum allowed size' errors with large JSON files.
| Name | Required | Description | Default |
|---|---|---|---|
| file_path | Yes | Path to the JSON file to process | |
| max_length | No | Maximum length for string values (default: 200) | |
| type_only | No | Return only value types instead of values. Most compact output. (default: false) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the tool creates a transformed version of JSON files (preserving structure while truncating values and deduplicating arrays), addresses size constraints, and mentions the specific error scenario it helps resolve. It doesn't cover all potential behavioral aspects like error handling or performance characteristics, but provides substantial operational context beyond basic functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise with two sentences that each serve distinct purposes: the first explains what the tool does and its key features, the second provides the specific usage context. There's zero wasted language, and the most important information (what it creates and why) is front-loaded. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description provides good contextual completeness. It explains the tool's purpose, when to use it, and key behavioral characteristics. The main gap is the lack of information about the output format - what exactly the 'JSON skeleton' looks like, whether it maintains the original file structure completely, or if there are any limitations on the transformations. However, for a tool with good schema coverage and clear purpose, it's mostly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema - it doesn't explain how 'max_length' relates to 'truncated values' or how 'type_only' affects the deduplication process. However, it provides overall context about what the parameters collectively achieve, which maintains the baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('create', 'preserves', 'truncated', 'deduplicated') and resources ('JSON skeleton', 'large JSON files'). It distinguishes itself by addressing a specific error scenario ('File content exceeds maximum allowed size') and explains what makes the output 'lightweight' - truncated values and deduplicated arrays. This goes beyond just restating 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: 'when encountering File content exceeds maximum allowed size errors with large JSON files.' This gives a specific trigger condition. However, it doesn't mention when NOT to use it or discuss alternatives (though there are no sibling tools listed, so this limitation is understandable). The guidance is explicit but lacks exclusion criteria.
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 tool update
- First observed
json_skeleton
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap with other tools. The tool's purpose is clearly defined and distinct by default.
A single tool inherently has consistent naming, as there are no other tools to compare against. The name 'json_skeleton' follows a clear noun-based pattern.
One tool is too few for most server purposes, making the set feel thin and potentially incomplete. While the tool addresses a specific need, a server typically benefits from more functionality to handle related tasks.
The tool covers a specific use case for handling large JSON files, but the domain of JSON manipulation is broad. There are obvious gaps, such as tools for parsing, validating, or modifying JSON, which limits the server's utility for comprehensive JSON workflows.
Maintenance
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