What can you do with this server?

The Fetch JSONPath MCP server is a Model Context Protocol server that provides efficient web scraping and data extraction tools designed to reduce LLM token usage and hallucination by extracting only necessary information. * Extract Specific JSON Data: Fetch JSON content from URLs and precisely extract data using basic and extended JSONPath patterns, supporting filtering, arithmetic operations, length checks, and text transformations * Fetch Web Content: Retrieve web content in multiple formats including intelligent Markdown (default), clean plain text, or raw HTML * Batch Processing: Concurrently process multiple URLs for both JSON extraction and text fetching, optimizing performance by fetching unique URLs only once * Multi-Method HTTP Support: Execute requests using GET, POST, PUT, DELETE, PATCH, HEAD, and OPTIONS methods with custom request bodies and headers * Smart Browser Headers: Automatically include browser-like headers merged with custom headers to prevent blocking and ensure reliable web access * Configurable Behavior: Customize server operations through environment variables for timeout, SSL verification, redirect following, and HTTP proxy settings * Efficient Token Usage: Extract only required data to minimize token consumption, reduce costs, and improve model accuracy

How do I use Fetch JSONPath MCP?

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., "@Fetch JSONPath MCP fetch the top 5 trending repositories from GitHub API using pattern 'items[*].full_name'" 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.

Fetch JSONPath MCP

by ackness

Overview Schema Related Servers Score Discussions

Python

Hybrid

Fetch JSONPath MCP

PyPI Downloads 简体中文

A Model Context Protocol (MCP) server that provides tools for fetching JSON data and web content from URLs. Features intelligent content extraction, multiple HTTP methods, and browser-like headers for reliable web scraping.

🎯 Why Use This?

Reduce LLM Token Usage & Hallucination - Instead of fetching entire JSON responses and wasting tokens, extract only the data you need.

Traditional Fetch vs JSONPath Extract

❌ Traditional fetch (wasteful):

// API returns 2000+ tokens
{
  "data": [
    {
      "id": 1,
      "name": "Alice",
      "email": "alice@example.com", 
      "avatar": "https://...",
      "profile": {
        "bio": "Long bio text...",
        "settings": {...},
        "preferences": {...},
        "metadata": {...}
      },
      "posts": [...],
      "followers": [...],
      "created_at": "2023-01-01",
      "updated_at": "2024-01-01"
    },
    // ... 50 more users
  ],
  "pagination": {...},
  "meta": {...}
}

✅ JSONPath extract (efficient):

// Only 10 tokens - exactly what you need!
["Alice", "Bob", "Charlie"]

Using pattern: data[*].name saves 99% tokens and eliminates model hallucination from irrelevant data.

Related MCP server: webclaw

Installation

For most IDEs, use the uvx tool to run the server.

{
  "mcpServers": {
    "fetch-jsonpath-mcp": {
      "command": "uvx",
      "args": [
        "fetch-jsonpath-mcp"
      ]
    }
  }
}

claude mcp add fetch-jsonpath-mcp -- uvx fetch-jsonpath-mcp

{
  "mcpServers": {
    "fetch-jsonpath-mcp": {
      "command": "uvx",
      "args": ["fetch-jsonpath-mcp"]
    }
  }
}

Add this to your Windsurf MCP config file. See Windsurf MCP docs for more info.

Windsurf Local Server Connection

{
  "mcpServers": {
    "fetch-jsonpath-mcp": {
      "command": "uvx",
      "args": ["fetch-jsonpath-mcp"]
    }
  }
}

"mcp": {
  "servers": {
    "fetch-jsonpath-mcp": {
      "type": "stdio",
      "command": "uvx",
      "args": ["fetch-jsonpath-mcp"]
    }
  }
}

Development Setup

1. Install Dependencies

uv sync

2. Start Demo Server (Optional)

# Install demo server dependencies
uv add fastapi uvicorn

# Start demo server on port 8080
uv run demo-server

3. Run MCP Server

uv run fetch-jsonpath-mcp

Demo Server Data

The demo server at http://localhost:8080 returns:

{
  "foo": [{"baz": 1, "qux": "a"}, {"baz": 2, "qux": "b"}],
  "bar": {
    "items": [10, 20, 30], 
    "config": {"enabled": true, "name": "example"}
  },
  "metadata": {"version": "1.0.0"}
}

Available Tools

`fetch-json`

Extract JSON data using JSONPath patterns with support for all HTTP methods.

{
  "name": "fetch-json",
  "arguments": {
    "url": "http://localhost:8080",
    "pattern": "foo[*].baz",
    "method": "GET"
  }
}

Returns: [1, 2]

Parameters:

url (required): Target URL
pattern (optional): JSONPath pattern for data extraction
method (optional): HTTP method (GET, POST, PUT, DELETE, etc.) - Default: "GET"
data (optional): Request body for POST/PUT requests
headers (optional): Additional HTTP headers

`fetch-text`

Fetch web content with intelligent text extraction. Defaults to Markdown format for better readability.

{
  "name": "fetch-text",
  "arguments": {
    "url": "http://localhost:8080",
    "output_format": "clean_text"
  }
}

Returns: Clean text representation of the JSON data

Output Formats:

"markdown" (default): Converts HTML to clean Markdown format
"clean_text": Pure text with HTML tags removed
"raw_html": Original HTML content

Parameters:

url (required): Target URL
method (optional): HTTP method - Default: "GET"
data (optional): Request body for POST/PUT requests
headers (optional): Additional HTTP headers
output_format (optional): Output format - Default: "markdown"

`batch-fetch-json`

Process multiple URLs with different JSONPath patterns concurrently.

{
  "name": "batch-fetch-json",
  "arguments": {
    "requests": [
      {"url": "http://localhost:8080", "pattern": "foo[*].baz"},
      {"url": "http://localhost:8080", "pattern": "bar.items[*]"}
    ]
  }
}

Returns: [{"url": "http://localhost:8080", "pattern": "foo[*].baz", "success": true, "content": [1, 2]}, {"url": "http://localhost:8080", "pattern": "bar.items[*]", "success": true, "content": [10, 20, 30]}]

Request Object Parameters:

url (required): Target URL
pattern (optional): JSONPath pattern
method (optional): HTTP method - Default: "GET"
data (optional): Request body
headers (optional): Additional HTTP headers

`batch-fetch-text`

Fetch content from multiple URLs with intelligent text extraction.

{
  "name": "batch-fetch-text",
  "arguments": {
    "requests": [
      "http://localhost:8080",
      {"url": "http://localhost:8080", "output_format": "raw_html"}
    ],
    "output_format": "markdown"
  }
}

Returns: [{"url": "http://localhost:8080", "success": true, "content": "# Demo Server Data\n\n..."}, {"url": "http://localhost:8080", "success": true, "content": "{\"foo\": [{\"baz\": 1, \"qux\": \"a\"}, {\"baz\": 2, \"qux\": \"b\"}]..."}]

Supports:

Simple URL strings
Full request objects with custom methods and headers
Mixed input types in the same batch

JSONPath Examples

This project uses jsonpath-ng for JSONPath implementation.

Pattern	Result	Description
`foo[*].baz`	`[1, 2]`	Get all baz values
`bar.items[*]`	`[10, 20, 30]`	Get all items
`metadata.version`	`["1.0.0"]`	Get version

For complete JSONPath syntax reference, see the jsonpath-ng documentation.

🚀 Performance Benefits

Token Efficiency: Extract only needed data, not entire JSON responses
Faster Processing: Smaller payloads = faster LLM responses
Reduced Hallucination: Less irrelevant data = more accurate outputs
Cost Savings: Fewer tokens = lower API costs
Better Focus: Clean data helps models stay on task
Smart Headers: Default browser headers prevent blocking and improve access
Markdown Conversion: Clean, readable format that preserves structure

Configuration

Set environment variables to customize behavior:

# Request timeout in seconds (default: 10.0)
export JSONRPC_MCP_TIMEOUT=30

# SSL verification (default: true)
export JSONRPC_MCP_VERIFY=false

# Follow redirects (default: true)
export JSONRPC_MCP_FOLLOW_REDIRECTS=true

# Custom headers (will be merged with default browser headers)
export JSONRPC_MCP_HEADERS='{"Authorization": "Bearer token"}'

# HTTP proxy configuration
export JSONRPC_MCP_PROXY="http://proxy.example.com:8080"

Default Browser Headers: The server automatically includes realistic browser headers to prevent blocking:

User-Agent: Chrome browser simulation
Accept: Standard browser content types
Accept-Language, Accept-Encoding: Browser defaults
Security headers: Sec-Fetch-* headers for modern browsers

Custom headers in JSONRPC_MCP_HEADERS will override defaults when there are conflicts.

Development

# Run tests
pytest

# Check code quality
ruff check --fix

# Build and test locally
uv build

What's New in v1.1.0

✨ Multi-Method HTTP Support: GET, POST, PUT, DELETE, PATCH, HEAD, OPTIONS
🔄 Tool Renaming: get-json → fetch-json, get-text → fetch-text
📄 Markdown Conversion: Default HTML to Markdown conversion with markdownify
🌐 Smart Browser Headers: Automatic browser simulation headers
🎛️ Format Control: Three output formats for text content (markdown, clean_text, raw_html)
🚀 Enhanced Batch Processing: Support for different methods in batch operations

Available Tools

4 tools

batch-fetch-jsonA

Batch extract JSON content from multiple URLs with different extended JSONPath patterns. Supports all JSONPath extensions and optimizes by fetching each unique request only once. Executes requests concurrently for better performance. Supports different HTTP methods.

ParametersJSON Schema

Name	Required	Description	Default
`requests`	Yes	Array of request objects

TDQS

A3.9/5.0

Behavior3/5

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 behaviors: concurrent execution for performance, deduplication optimization, and support for multiple HTTP methods with request bodies. However, it lacks important details like error handling, timeout behavior, authentication requirements, or rate limits that would be needed for a complete behavioral picture.

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 efficiently structured with four focused sentences that each add value: states the core purpose, describes JSONPath support, explains performance optimizations, and notes HTTP method flexibility. There's zero wasted text, and the most important information (batch JSON extraction with JSONPath) appears first.

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?

For a tool with no annotations and no output schema, the description provides good coverage of the core functionality but has significant gaps. It explains what the tool does and some behavioral aspects, but doesn't describe the return format, error responses, or important operational constraints. Given the complexity of batch HTTP operations with JSON processing, more completeness would be expected.

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 schema already documents all parameters thoroughly. The description adds some context about JSONPath extensions and concurrent execution, but doesn't provide significant additional parameter semantics beyond what's in the schema. The baseline of 3 is appropriate when 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 specific action ('batch extract JSON content'), resource ('from multiple URLs'), and mechanism ('with different extended JSONPath patterns'). It distinguishes from sibling tools by specifying JSON extraction (vs. text extraction in batch-fetch-text) and batch processing (vs. single URL in fetch-json).

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 context for when to use this tool (batch JSON extraction with JSONPath patterns) and implicitly distinguishes from batch-fetch-text (JSON vs. text) and fetch-json (batch vs. single). However, it doesn't explicitly state when NOT to use it or name specific alternatives, missing the highest score criteria.

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

batch-fetch-textA

Batch fetch raw text content from multiple URLs using various HTTP methods. Executes requests concurrently for better performance.

ParametersJSON Schema

Name	Required	Description	Default
`requests`	Yes	Array of URLs (strings) or request objects

TDQS

A3.7/5.0

Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: concurrent execution for performance and support for various HTTP methods beyond GET. However, it lacks details on error handling, rate limits, authentication needs, timeout behavior, or what 'raw text content' specifically entails (e.g., encoding, size limits).

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 extremely concise with two sentences that are front-loaded and waste-free. The first sentence covers purpose and scope, while the second adds performance context, with every word earning its place.

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 (batch HTTP operations with multiple methods and output formats), no annotations, and no output schema, the description is incomplete. It doesn't explain return values, error formats, or important behavioral constraints like concurrency limits or timeouts, leaving significant gaps for an AI agent to use it correctly.

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%, providing detailed documentation for the single parameter 'requests' and its nested properties. The description adds minimal value beyond the schema, only implying that requests are executed concurrently. No additional parameter semantics are explained in the description.

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 specific action ('batch fetch raw text content'), target resource ('from multiple URLs'), and method ('using various HTTP methods'). It distinguishes from sibling tools by specifying 'raw text content' rather than JSON, and mentions concurrent execution for performance.

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

Usage Guidelines3/5

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

The description implies usage context through 'batch fetch' and 'multiple URLs,' suggesting this is for bulk operations rather than single requests. However, it doesn't explicitly state when to use this tool versus alternatives like 'batch-fetch-json' or 'fetch-text,' nor does it mention any prerequisites or exclusions.

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

fetch-jsonA

Extract JSON content from a URL using JSONPath with extended features. Supports extensions like len, keys, filtering, arithmetic operations, and more. If 'pattern' is omitted or empty, the entire JSON document is returned. Supports different HTTP methods (default: GET).

ParametersJSON Schema

Name	Required	Description	Default
`url`	Yes	The URL to get raw JSON from
`pattern`	No	Extended JSONPath pattern supporting: Basic: 'foo[].baz', 'bar.items[]'; Extensions: '$.data.`len`', '$.users.`keys`', '$.field.`str()`'; Filtering: '$.items[?(@.price > 10)]', '$.users[?name = "John"]'; Arithmetic: '$.a + $.b', '$.items[].price 1.2'; Text ops: '$.text.`sub(/old/, new)`', '$.csv.`split(",")'
`method`	No	HTTP method to use (GET, POST, PUT, DELETE, PATCH, etc.). Default is GET.	GET
`data`	No	Request body data for POST/PUT/PATCH requests. Can be a JSON object or string.
`headers`	No	Additional HTTP headers to include in the request

TDQS

A3.9/5.0

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 behavioral traits like default HTTP method (GET), handling of omitted patterns, and support for extended JSONPath features. However, it lacks details on error handling, rate limits, authentication needs, or response formats, which are important for a tool making HTTP requests.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by key features and defaults. Every sentence adds value, such as explaining pattern behavior and HTTP method support, with no redundant or wasted information.

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 complexity (5 parameters, HTTP operations, JSONPath features) and no annotations or output schema, the description is partially complete. It covers basic usage and features but lacks details on error cases, authentication, rate limits, or return value structure, which are crucial for an HTTP-based tool with extended functionality.

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 some context, such as the effect of omitting 'pattern' and default HTTP method, but does not provide significant additional meaning beyond the schema. 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: 'Extract JSON content from a URL using JSONPath with extended features.' It specifies the verb ('extract'), resource ('JSON content'), and method ('JSONPath'), and distinguishes itself from sibling tools like fetch-text (which handles text) and batch-fetch-json (which handles multiple URLs).

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 context for usage: 'If 'pattern' is omitted or empty, the entire JSON document is returned' and 'Supports different HTTP methods (default: GET).' It implies when to use this tool (for JSON extraction with JSONPath) versus fetch-text (for text extraction), but does not explicitly name alternatives or state exclusions, such as when to prefer batch-fetch-json for multiple URLs.

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

fetch-textC

Fetch text content from a URL using various HTTP methods. Defaults to converting HTML to Markdown format.

ParametersJSON Schema

Name	Required	Description	Default
`url`	Yes	The URL to get text content from
`method`	No	HTTP method to use (GET, POST, PUT, DELETE, PATCH, etc.). Default is GET.	GET
`data`	No	Request body data for POST/PUT/PATCH requests. Can be a JSON object or string.
`headers`	No	Additional HTTP headers to include in the request
`output_format`	No	Output format: 'markdown' (default), 'clean_text', or 'raw_html'.	markdown

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'various HTTP methods' and 'defaults to converting HTML to Markdown format,' which adds some context about functionality. However, it doesn't cover critical aspects like error handling, rate limits, authentication needs, or what happens with non-HTML content, leaving significant gaps for a tool that interacts with external URLs.

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 a single, efficient sentence that front-loads the core purpose. It avoids unnecessary details, though it could be slightly more structured by explicitly separating key points. Overall, it's concise with minimal waste.

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 (5 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits, error handling, and output specifics, which are crucial for a tool fetching content from URLs. The schema covers parameters well, but the description doesn't compensate for missing annotations or output schema, leaving the agent with insufficient context.

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, mentioning 'various HTTP methods' and 'defaults to converting HTML to Markdown format,' which loosely relates to 'method' and 'output_format' parameters but doesn't provide additional semantics. 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.

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: 'Fetch text content from a URL using various HTTP methods.' It specifies the resource (URL) and action (fetch text content), but doesn't explicitly differentiate from sibling tools like 'fetch-json' or 'batch-fetch-text' beyond mentioning 'text content' and 'Markdown format.' This makes it clear but not fully sibling-distinctive.

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 like 'fetch-json' or 'batch-fetch-text.' It mentions 'various HTTP methods' and 'defaults to converting HTML to Markdown format,' which implies some context, but lacks explicit when-to-use or when-not-to-use statements, leaving the agent to infer usage scenarios.

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.

8 tool updatesv1.0.0Jan 3, 2026
- Addedbatch-fetch-json
- Addedbatch-fetch-text
- Removedbatch-get-json
- Removedbatch-get-text
- Addedfetch-json
- Addedfetch-text
- Removedget-json
- Removedget-text
4 tool updatesAug 17, 2025
- First observedbatch-get-json
- First observedbatch-get-text
- First observedget-json
- First observedget-text

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A3.7/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: fetch-json and fetch-text handle single URL operations with JSONPath and raw text extraction respectively, while batch-fetch-json and batch-fetch-text handle multiple URLs with the same capabilities. There is no overlap or ambiguity between tools.

Naming Consistency5/5

All tool names follow a consistent verb-object pattern with hyphens (fetch-json, fetch-text, batch-fetch-json, batch-fetch-text). The naming convention is perfectly uniform across all four tools.

Tool Count5/5

Four tools is an ideal number for this server's purpose of fetching and extracting content from URLs. It provides both single and batch operations for JSON and text, covering the domain efficiently without being too sparse or bloated.

Completeness4/5

The tool set covers the core fetching operations for JSON and text content with both single and batch capabilities, including support for various HTTP methods and JSONPath extensions. A minor gap is the lack of explicit tools for error handling or retry mechanisms, but agents can work around this.

Maintenance

ActivityInactive

ResponsivenessSyncing

Resources

GitHub Repository

Need Help?

Related Servers

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Looking for Admin?

If you are the server author, to access and configure the admin panel.

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Fetch JSONPath MCP

🎯 Why Use This?

Traditional Fetch vs JSONPath Extract

Installation

Windsurf Local Server Connection

Development Setup

1. Install Dependencies

2. Start Demo Server (Optional)

3. Run MCP Server

Demo Server Data

Available Tools

fetch-json

fetch-text

batch-fetch-json

batch-fetch-text

JSONPath Examples

🚀 Performance Benefits

Configuration

Development

What's New in v1.1.0

Available Tools

Tool Schema Changelog

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

webpeelofficial

Appeared in Searches

Latest Blog Posts

MCP directory API

`fetch-json`

`fetch-text`

`batch-fetch-json`

`batch-fetch-text`