by mcpfinder
Provides AI agents with a discovery layer to locate, evaluate, and install MCP servers from the Official Registry, Glama, and Smithery, delivering trust signals and ready‑to‑use configuration snippets.
Mcpfinder offers a searchable catalog of more than 25,000 MCP servers, aggregating data from the Official MCP Registry, Glama, and Smithery. It evaluates each entry for trust, popularity, and freshness, then generates client‑specific installation snippets so AI assistants can instantly extend their capabilities.
{
"mcpServers": {
"mcpfinder": {
"command": "npx",
"args": ["-y", "@mcpfinder/server"]
}
}
}
search_mcp_servers(query) – locate servers by keyword or use‑case.get_server_details(name) – view metadata, trust signals, and required secrets.get_install_config(name, platform) – obtain a JSON snippet tailored to Claude Desktop, Cursor, Claude Code, Cline, or Windsurf.browse_categories(category?) – explore servers by domain.search_mcp_servers("postgres"), pick the best server, and embed the config.browse_categories to present options in UI dialogs.Q: Which clients are supported? A: Claude Desktop, Claude Code, Cursor, Cline / Roo Code, and Windsurf.
Q: Do I need to run a hosted HTTP server?
A: Currently the canonical interface is the stdio server launched with npx -y @mcpfinder/server. An HTTP endpoint is planned for the future.
Q: How are trust signals calculated?
A: By combining source count, verification flags, community usage (useCount), presence in the official registry, and update recency.
Q: Can I use Mcpfinder offline? A: Yes. The first run can bootstrap from a pre‑built SQLite snapshot, allowing fully offline searches afterward.
Q: How do I add my own MCP server to the catalog? A: Publish it to one of the supported registries (Official MCP Registry, Glama, or Smithery). Mcpfinder will ingest it on the next snapshot build.
The MCP server that helps AI agents discover, evaluate, and install other MCP servers.
MCPfinder is an AI-first discovery layer over the Official MCP Registry, Glama, and Smithery. Install it once, and your assistant can search for missing capabilities, inspect trust signals, review required secrets, and generate client-specific MCP config snippets.
stdio via npx -y @mcpfinder/server@mcpfinder/serverdev.mcpfinder/server{
"mcpServers": {
"mcpfinder": {
"command": "npx",
"args": ["-y", "@mcpfinder/server"]
}
}
}
{
"mcpServers": {
"mcpfinder": {
"command": "npx",
"args": ["-y", "@mcpfinder/server"]
}
}
}
{
"mcpServers": {
"mcpfinder": {
"command": "npx",
"args": ["-y", "@mcpfinder/server"]
}
}
}
Supported install targets today:
If your agent supports the Agent Skills format (Claude Code, GitHub Copilot in VS Code, OpenAI Codex, and others), you can drop a one-line install and let the agent handle the config merge itself.
Claude Code (global):
mkdir -p ~/.claude/skills/install-mcpfinder && \
curl -sSf -o ~/.claude/skills/install-mcpfinder/SKILL.md \
https://mcpfinder.dev/skill/install-mcpfinder/SKILL.md
VS Code (project-scoped):
mkdir -p .agents/skills/install-mcpfinder && \
curl -sSf -o .agents/skills/install-mcpfinder/SKILL.md \
https://mcpfinder.dev/skill/install-mcpfinder/SKILL.md
Then tell your agent any of: "install MCPfinder", "connect my AI to Postgres", "I need a tool for [anything]" — the skill activates, detects your client, merges the config without clobbering, and tells you what to restart.
Use MCPfinder when the user needs a capability you do not already have.
search_mcp_servers.get_server_details.get_install_config.browse_categories (omit category to list; pass category for top servers).Preferred workflow:
search_mcp_servers(query="postgres")get_server_details(name="...best candidate...")get_install_config(name="...best candidate...", platform="claude-desktop")| Tool | Purpose | When to call |
|---|---|---|
search_mcp_servers |
Search by keyword, technology, or use case | First step when a capability is missing |
get_server_details |
Inspect metadata, trust signals, tools, warnings, env vars | Before recommending or installing |
get_install_config |
Generate a JSON config snippet for a target client | After selecting a server |
browse_categories |
Single-call category browser (omit category to list; pass category for top servers) |
Domain-driven discovery |
MCPfinder is intentionally optimized for agent consumption.
Search ranking uses:
useCount)Each result is also annotated with:
confidenceScorerecommendationReasonwarningFlagsupdatedAtsourceCountMCPfinder aggregates:
Counts vary over time and differ depending on whether you count raw upstream records or merged/deduplicated entries. Snapshot metadata is the source of truth for the currently published local bootstrap dataset.
First run can bootstrap from a prebuilt SQLite snapshot instead of doing a slow live sync.
/api/v1/snapshot/manifest.json/api/v1/snapshot/data.sqlite.gz.github/workflows/snapshot.ymlUser request:
I need my assistant to read data from PostgreSQL.
Agent workflow:
search_mcp_servers(query="postgres")
get_server_details(name="io.example/postgres")
get_install_config(name="io.example/postgres", platform="cursor")
Agent response:
I found a PostgreSQL MCP server with official registry presence and recent metadata.
It requires DATABASE_URL and runs via npx.
Add this JSON to ~/.cursor/mcp.json, then reload Cursor.
mcpfinder/
├── packages/
│ ├── core/ # sync, SQLite search, trust signals, install-config generation
│ └── mcp-server/ # stdio MCP server
├── landing/ # static website and AI-facing public files
├── api-worker/ # snapshot/support worker for published bootstrap artifacts
└── scripts/ # snapshot builder and other support scripts
pnpm install
pnpm --filter @mcpfinder/core build
pnpm --filter @mcpfinder/server build
node packages/mcp-server/dist/index.js
stdio server is the canonical interface. Install via npx -y @mcpfinder/server.mcpfinder.dev/mcp. The api-worker package is reserved for snapshot support and will only be promoted to a canonical HTTP transport once it exposes the same tool contract as the stdio server.These items are planned but not yet implemented. Informed largely by feedback from AI agents consuming the tool surface.
toolsExposed[*].description (where upstream exposes
it) into a lightweight embedding column, expose a semanticQuery parameter
alongside the existing keyword query, and rank hybrid.mcpfinder.dev/mcp. Today only stdio is
canonical. Serverless AI agents (Workers, Lambda, browser) can't spawn a
subprocess; giving them an HTTP transport with the same 4-tool contract
removes an entire class of blocker. Plan: port the MCP SDK streamable-http
transport into api-worker/, re-use the same snapshot-backed database via
R2 + Durable Objects, gate with a lightweight rate limit.capabilityCount is
currently 0 for most Official/Smithery rows because those upstreams don't
publish tool manifests in list responses. Plan: during the snapshot build,
probe the downstream server's README or, for npm packages, parse the tarball's
package.json for an mcp.tools hint; surface per-row confidence in the
extracted list.docs/publish-playbook.md) is manual and consumes a fresh OTP per package.
Plan: move to GitHub Actions with NPM automation tokens and a committed
mcp-publisher login step triggered on v* tags.dev.mcpfinder/serverBuilt by Coder AI under AGPL-3.0-or-later.
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{
"mcpServers": {
"mcpfinder": {
"command": "npx",
"args": [
"-y",
"@mcpfinder/server"
],
"env": {}
}
}
}claude mcp add mcpfinder npx -y @mcpfinder/server