tinyfish-search-fetch-mcp
A lightweight MCP server enabling web search and content fetching via TinyFish Free Access API with support for single and burst URL fetch.
README
TinyFish Search/Fetch MCP Server
A lightweight, stdio-based Model Context Protocol (MCP) server designed to provide search and fetch capabilities through the TinyFish ecosystem.
Note: This MCP server provides access exclusively to the TinyFish Free Access API (Search & Fetch).
Overview
TinyFish Search/Fetch MCP Server is a standalone MCP server that offers web search via the TinyFish Search API and content fetching via the TinyFish Fetch API. It uses stdio transport for seamless integration with MCP-compatible clients such as Claude Desktop, Cursor, Windsurf, or any custom client supporting the stdio protocol.
Features
- Search: Quickly find relevant information using TinyFish search capabilities.
- Fetch: Retrieve content from web resources efficiently.
- Burst Fetch: Fetch and extract clean content from up to 10 URLs in a single request.
- Stdio Transport: Designed for seamless integration with MCP clients (e.g. Claude Desktop, Cursor).
Available Tools
search
Search the web using TinyFish Search API.
Parameters:
| Parameter | Type | Required | Description |
|---|---|---|---|
query |
string | Yes | Search query. Search operators such as site:example.com may be used. |
location |
string | No | Country code, e.g. JP, US, GB, FR. |
language |
string | No | Language code, e.g. ja, en, fr. |
max_results |
integer | No | Local truncation count for returned results. Must be positive. |
Returns: JSON text containing the TinyFish search response.
fetch_content
Fetch and extract clean content from a single URL using TinyFish Fetch API.
Parameters:
| Parameter | Type | Required | Description |
|---|---|---|---|
url |
string | Yes | URL to fetch. Must use http or https scheme. |
format |
string | No | Output format: markdown (default), html, or json. |
links |
boolean | No | Include extracted page links when supported. Default: false. |
image_links |
boolean | No | Include extracted image links when supported. Default: false. |
Returns: JSON text containing the TinyFish fetch response.
fetch_contents
Fetch and extract clean content from up to 10 URLs using TinyFish Fetch API.
Parameters:
| Parameter | Type | Required | Description |
|---|---|---|---|
urls |
string[] | Yes | List of URLs to fetch. Maximum 10. Each must use http or https scheme. |
format |
string | No | Output format: markdown (default), html, or json. |
links |
boolean | No | Include extracted page links when supported. Default: false. |
image_links |
boolean | No | Include extracted image links when supported. Default: false. |
Returns: JSON text containing the TinyFish fetch response.
Tool Schemas
search
{
"name": "search",
"parameters": {
"type": "object",
"additionalProperties": false,
"properties": {
"query": {
"type": "string",
"description": "Search query. Search operators such as site:example.com may be used."
},
"location": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional country code, e.g. JP, US, GB, FR."
},
"language": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional language code, e.g. ja, en, fr."
},
"max_results": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional local truncation count for returned results."
}
},
"required": ["query"]
}
}
fetch_content
{
"name": "fetch_content",
"parameters": {
"type": "object",
"additionalProperties": false,
"properties": {
"url": {
"type": "string",
"description": "URL to fetch. Must be http or https."
},
"format": {
"type": "string",
"enum": ["markdown", "html", "json"],
"default": "markdown",
"description": "Output format: markdown, html, or json."
},
"links": {
"type": "boolean",
"default": false,
"description": "Include extracted page links when supported."
},
"image_links": {
"type": "boolean",
"default": false,
"description": "Include extracted image links when supported."
}
},
"required": ["url"]
}
}
fetch_contents
{
"name": "fetch_contents",
"parameters": {
"type": "object",
"additionalProperties": false,
"properties": {
"urls": {
"type": "array",
"items": {
"type": "string"
},
"description": "URLs to fetch. Maximum 10. Each must be http or https."
},
"format": {
"type": "string",
"enum": ["markdown", "html", "json"],
"default": "markdown",
"description": "Output format: markdown, html, or json."
},
"links": {
"type": "boolean",
"default": false,
"description": "Include extracted page links when supported."
},
"image_links": {
"type": "boolean",
"default": false,
"description": "Include extracted image links when supported."
}
},
"required": ["urls"]
}
}
Comparison with the Official Integration
While TinyFish provides an official MCP integration using OAuth 2.1 for secure authentication (which requires a browser-based flow for initial setup), this implementation uses a single TinyFish API Key. This makes it better suited for:
- Headless Environments: Ideal for servers or environments where no web browser is available.
- Free Access: Optimized specifically for usage with the TinyFish Free Access API.
Learn more about the official integration here.
Prerequisites
- Python >= 3.11
- An MCP-compatible client
Installation
Using uv (Recommended)
For a clean installation as a standalone tool with isolated dependencies, use uv:
uv tool install .
Using pip
If you are already working in a virtual environment:
pip install .
Usage
Before running the server, set your TinyFish API Key as an environment variable.
Linux / macOS (bash, zsh):
export TINYFISH_API_KEY="<your tinyfish api key>"
Windows PowerShell:
$env:TINYFISH_API_KEY="<your tinyfish api key>"
API keys are obtained by logging in at https://agent.tinyfish.ai/. Once your API key is set, the server can be invoked via its command-line entry point:
tinyfish-search-fetch-mcp
Integration with Claude Desktop
To use this server with Claude Desktop, add the following to your claude_desktop_config.json:
{
"mcpServers": {
"tinyfish-search-fetch": {
"command": "tinyfish-search-fetch-mcp",
"env": {
"TINYFISH_API_KEY": "<your tinyfish api key>"
}
}
}
}
Note: If you have already defined TINYFISH_API_KEY in your shell environment (e.g. via ~/.bashrc, ~/.zshrc), the "env" block above is optional and can be omitted.
If you installed via pip in a virtual environment, ensure the command points to the correct executable path.
Troubleshooting
Empty search query error
If you see an error such as valueerror: query must not be empty, ensure the query parameter contains non-whitespace characters. Leading or trailing whitespace is automatically trimmed.
Invalid URL scheme error
If you see an error such as valueerror: url scheme must be http or https, ensure the url parameter uses either http:// or https:// as the scheme.
Unsupported URL
If you see an error such as valueerror: url must include a host, the provided URL does not contain a valid hostname. Provide a full URL including domain (e.g. https://example.com).
Development
Local Installation
Clone and install this server in editable mode:
With pip:
git clone <repository-url>
cd tinyfish-search-fetch-mcp
pip install -e ".[dev]"
With uv:
git clone <repository-url>
cd tinyfish-search-fetch-mcp
uv pip install -e ".[dev]"
Running Tests
This project uses pytest. To run the test suite:
python -m pytest
Live tests exercise the real TinyFish Search/Fetch APIs. They require both network access and TINYFISH_API_KEY.
- If
TINYFISH_API_KEYis not set, live tests are skipped. - If
CODEX_SANDBOX_NETWORK_DISABLED=1, live tests are skipped because outbound network access is unavailable. - With network access and an API key, the live tests call all three MCP tools and cover parameter variants such as
location,language,max_results,format,links, andimage_links.
Coding Standards
Use ruff for linting:
python -m ruff check src tests
License
This project is licensed under the MIT License - see the LICENSE file for details.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.