
ModelContextProtocol (MCP) Server Integration
The ModelContextProtocol (MCP) Server acts as a bridge between AI agents and external data sources, APIs, and services, enabling FlowHunt users to build context...
Query, filter, and transform JSON data sources with FlowHunt’s JSON MCP Server—enabling rapid prototyping, data analytics, and automation in your AI workflows.
The JSON MCP Server is a Model Context Protocol (MCP) server that enables AI assistants and LLMs to interact with, query, and manipulate JSON data sources using standardized tools and operations. By serving as a bridge between AI clients and external JSON-based data, the server enhances development workflows for tasks that require structured data access, advanced querying, or transformation. It supports powerful features such as JSONPath-based queries, filtering with conditions, array and string operations, date and numeric computations, and aggregation. Developers can leverage the server to perform database-like operations, data analysis, and data-driven automation without needing to build custom integrations for each data source.
No explicit prompt templates are mentioned in the repository or documentation.
No explicit resources are documented in the repository or README. The server operates on external JSON data via URLs, but does not list predefined resources.
query
Query JSON data using JSONPath syntax with extended operations.
Input:
url
(string): URL of the JSON data sourcejsonPath
(string): JSONPath expression with optional operationsfilter
Filter JSON data using conditions.
Input:
url
(string): URL of the JSON data sourcejsonPath
(string): Base JSONPath expressioncondition
(string): Filter conditionData Analytics on JSON APIs
Enables developers or LLMs to run complex queries, aggregations, and statistical computations directly on remote JSON API responses, streamlining data analysis workflows.
Automated Data Transformation
Automate mapping, filtering, and transformation of large JSON datasets for ETL (Extract, Transform, Load) pipelines, saving developer time on custom scripts.
Dynamic Dashboard Creation
Supports the backend for dashboards that need to aggregate and visualize statistics from various JSON endpoints by providing sorting, grouping, and aggregation tools.
Rapid Prototyping with Live Data
Allows LLMs or users to quickly query and manipulate live JSON data for proof-of-concept applications or exploratory data analysis.
Rule-Based Data Filtering
Empowers developers to filter and extract relevant information from JSON feeds based on dynamic, programmable conditions.
mcpServers
object:{
"json": {
"command": "npx",
"args": [
"@gongrzhe/server-json-mcp@latest"
]
}
}
claude_desktop_config.json
file.{
"json": {
"command": "npx",
"args": [
"@gongrzhe/server-json-mcp@1.0.3"
]
}
}
{
"json": {
"command": "npx",
"args": [
"@gongrzhe/server-json-mcp@latest"
]
}
}
{
"json": {
"command": "npx",
"args": [
"@gongrzhe/server-json-mcp@latest"
]
}
}
If your JSON endpoints require authentication, set API keys via environment variables and reference them in your server configuration. Example:
{
"json": {
"command": "npx",
"args": [
"@gongrzhe/server-json-mcp@latest"
],
"env": {
"API_KEY": "${API_KEY}"
},
"inputs": {
"api_key": "${API_KEY}"
}
}
}
Replace ${API_KEY}
with your actual environment variable or secret management strategy.
Using MCP in FlowHunt
To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:
Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:
{
"json": {
"transport": "streamable_http",
"url": "https://yourmcpserver.example/pathtothemcp/url"
}
}
Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “json” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.
Section | Availability | Details/Notes |
---|---|---|
Overview | ✅ | |
List of Prompts | ⛔ | No prompt templates found |
List of Resources | ⛔ | No explicit resources listed |
List of Tools | ✅ | query, filter |
Securing API Keys | ✅ | Example provided in setup instructions |
Sampling Support (less important in evaluation) | ⛔ | Not mentioned |
JSON MCP Server is a focused and practical MCP server for JSON data processing, offering powerful tools for querying and filtering, but lacks advanced features like prompt templates, explicit resource definitions, and sampling/roots support. It scores well for utility and simplicity, especially for data-centric workflows.
MCP Score: 6/10
Has a LICENSE | ✅ (MIT) |
---|---|
Has at least one tool | ✅ |
Number of Forks | 13 |
Number of Stars | 52 |
The JSON MCP Server is a Model Context Protocol server that allows AI agents and developers to query, filter, and manipulate JSON data sources using standardized tools like JSONPath. It provides database-like operations on JSON APIs or files, supporting advanced data analytics, transformation, and automation workflows.
The server offers two primary tools: 'query' (for JSONPath-based data querying and transformation) and 'filter' (for extracting subsets of JSON data using programmable conditions).
Common use cases include data analytics on JSON APIs, automated data transformation for ETL pipelines, dynamic dashboard backends, rapid prototyping with live data, and rule-based filtering of JSON feeds.
You can add the JSON MCP Server to your preferred client (Windsurf, Claude, Cursor, Cline) by editing the configuration file and specifying the server details using the provided JSON snippet. Restart the client to enable the server.
Set sensitive API keys in environment variables and reference them in your MCP server configuration using the 'env' and 'inputs' fields, ensuring credentials remain secure.
No, the server does not include prompt templates or explicit resource definitions. It operates on any external JSON data provided via URL.
The JSON MCP Server scores 6/10, excelling at utility and simplicity for data-centric workflows, but lacks features like prompt templates and sampling support.
Supercharge your AI workflows with powerful JSON querying and automation. Experience seamless integration by adding the JSON MCP Server to your FlowHunt flows.
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