directree
VerifiedFeaturedRemoteAbout directree
directree is an MCP server for searching and comparing software listings from AI assistants. Its structured catalog labels facts by provenance, distinguishing observed, AI-inferred, and founder-edited information.
Key features
- 01Search listings by name or description
- 02Filter by category or free plan
- 03Retrieve structured facts for a listing
- 04Compare two to four products
- 05List categories and tool counts
Use cases
- 01Find software matching a category
- 02Check whether a product has a free plan
- 03Compare several products side by side
MCP support
Provides search, listing lookup, comparison, and category tools to MCP clients through a hosted HTTP endpoint or a local stdio server. No API key is required.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://www.directree.io/api/mcpclaude mcp add --transport http directree https://www.directree.io/api/mcp{"mcpServers":{"directree":{"type":"http","url":"https://www.directree.io/api/mcp"}}}Open in Cursor{"servers":{"directree":{"type":"http","url":"https://www.directree.io/api/mcp"}}}Open in VS Code{"mcpServers":{"directree":{"type":"http","url":"https://www.directree.io/api/mcp"}}}{"mcpServers":{"directree":{"serverUrl":"https://www.directree.io/api/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
4| Parameter | Type | Required | Description |
|---|---|---|---|
| limit | number | No | Max results (default 10, max 20) |
| query | string | No | Search term (name, tagline, description) |
| category | string | No | Category slug to filter by |
| has_free_plan | boolean | No | Filter to tools with a free plan |
Details
From the project READMEdirectree MCP Server
Query directree — the honest software directory — from any AI assistant that supports the Model Context Protocol.
Search 5,000+ software tools, get structured facts with clear provenance labels (observed / AI-inferred / founder-edited), and compare products side by side.
Tools
| Tool | Description |
|---|---|
search_tools | Search by name, tagline, or description. Filter by category or free plan. |
get_tool | Get full structured facts for one tool by slug. |
compare_tools | Compare 2-4 tools side by side. |
list_categories | List all software categories with tool counts. |
Usage
Option A: Remote HTTP endpoint (no install)
Any MCP-compatible client can connect to the hosted endpoint directly:
https://www.directree.io/api/mcp
Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):
{
"mcpServers": {
"directree": {
"command": "npx",
"args": ["-y", "mcp-remote", "https://www.directree.io/api/mcp"]
}
}
}
Restart Claude Desktop after saving. The directree tools will appear in the tools panel.
Option B: Run locally (stdio)
# Clone and install
git clone https://github.com/coburn2716/directree-mcp.git
cd directree-mcp
npm install
# Run
node mcp-server.mjs
No API key required. Reads directly from the directree public catalog.
Claude Desktop config for local:
{
"mcpServers": {
"directree": {
"command": "node",
"args": ["/path/to/directree-mcp/mcp-server.mjs"]
}
}
}
REST API
Prefer plain REST? Use the agent-ready endpoints directly:
# Search tools
curl "https://www.directree.io/api/agent/tools?q=project+management&has_free_plan=true"
# Get one tool
curl "https://www.directree.io/api/agent/tools/notion"
# List categories
curl "https://www.directree.io/api/agent/categories"
Full docs: directree.io/mcp
Why directree?
Every fact in the directory is labelled with its source:
- Observed - crawled directly from the tool's own website
- AI-inferred - generated by a model, clearly marked as such
- Founder-edited - verified and corrected by the tool's owner
No fake scores. No pay-to-rank. Clean, structured data agents can actually trust.
License
MIT
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.