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Vector Search — In-Memory TF-IDF Semantic Store icon

Vector Search MCP Setup & Guide

MCP Serversmithery

io.smithery.axel-belfort.vector-search

In-memory vector search API for AI agents. Store documents and query by semantic meaning using TF-IDF vectorization with cosine similarity. Lightweight alternative to Pinecone/Weaviate for small datasets. Tools: data_vector_search. Use this for building simple RAG systems, document matching, or semantic search over small collections (< 10K docs). IMPORTANT: For web-wide search, use web_search_query instead. Returns: {results[], scores[], matchCount}. No API key required — x402 micropayment $0.005/call on Base L2.

View on smithery ↗

Copy the install config on this page first, then verify docs and permissions upstream.

Popularity

Uses3.6k

Overview

In-memory vector search API for AI agents. Store documents and query by semantic meaning using TF-IDF vectorization with cosine similarity. Lightweight alternative to Pinecone/Weaviate for small datasets. Tools: data_vector_search. Use this for building simple RAG systems, document matching, or semantic search over small collections (< 10K docs). IMPORTANT: For web-wide search, use web_search_query instead. Returns: {results[], scores[], matchCount}. No API key required — x402 micropayment $0.005/call on Base L2. Vector Search — In-Memory TF-IDF Semantic Store is a MCP Server listed from smithery. Transports: streamable-http. This page includes an overview, setup tutorial, install commands, and use cases for Trae, Tongyi Lingma, Cursor, Claude Code, and VS Code.

remote

AgentHub Verified AvailabilityTested & Ready

Automated pipeline validated install commands, protocol & client compatibility

Verified At2026-07-18
Install Snippets TestedCLI & JSON config syntax verified
Protocol Handshake ReadyComplies with JSON-RPC 2.0 specifications
Origin Registry ActiveSourced from smithery
Verified ClientsClaude Code、Claude Desktop、Cursor 等
Security Tier: A+ 级 · 零权限本地沙盒 (A+)·Scanned for high-risk vulnerabilities. Recommended to run with project-scoped permissions.

Copy by platform

Choose your platform

  1. Open or create .cursor/mcp.json in your project root
  2. Click Copy config and paste; if you already have MCPs, merge only this entry under mcpServers
  3. Save, then Cmd+Shift+P (Ctrl+Shift+P on Windows) → Reload Window
Click to copy snippet
{
  "mcpServers": {
    "io-smithery-axel-belfort-vector-search": {
      "url": "https://smithery.ai/servers/axel-belfort/vector-search"
    }
  }
}

Setup tutorial

  1. Open the Vector Search — In-Memory TF-IDF Semantic Store page and confirm this MCP Server (source: smithery).
  2. Copy the Cursor, Claude Code, or VS Code snippet.
  3. Merge it into mcpServers and replace env placeholders with real secrets.
  4. Reload the window, then call the MCP tools from your agent chat.

Install commands

Install commands and setup steps are in the HTML so search engines and no-JS browsers can read them without running client JavaScript.

Claude Code (remote)

  1. Install Claude Code CLI (claude must work in your terminal)
  2. Click Copy config below, paste into terminal, and run
  3. Restart Claude Code after it succeeds
claude mcp add --transport http io-smithery-axel-belfort-vector-search https://smithery.ai/servers/axel-belfort/vector-search

Cursor — .cursor/mcp.json (remote)

  1. Open or create .cursor/mcp.json in your project root
  2. Click Copy config and paste; if you already have MCPs, merge only this entry under mcpServers
  3. Save, then Cmd+Shift+P (Ctrl+Shift+P on Windows) → Reload Window
{
  "mcpServers": {
    "io-smithery-axel-belfort-vector-search": {
      "url": "https://smithery.ai/servers/axel-belfort/vector-search"
    }
  }
}

VS Code — .vscode/mcp.json (remote)

  1. Install GitHub Copilot in VS Code with MCP support
  2. Open or create .vscode/mcp.json in your project root
  3. Click Copy config and paste; merge only this mcpServers entry if others exist
  4. Save and Developer: Reload Window
{
  "mcpServers": {
    "io-smithery-axel-belfort-vector-search": {
      "url": "https://smithery.ai/servers/axel-belfort/vector-search"
    }
  }
}

Claude Desktop — claude_desktop_config.json (remote)

  1. Open Claude Desktop config: macOS ~/Library/Application Support/Claude/claude_desktop_config.json; Windows %APPDATA%\Claude\claude_desktop_config.json
  2. Click Copy config and merge under mcpServers
  3. Fully quit and restart Claude Desktop
{
  "mcpServers": {
    "io-smithery-axel-belfort-vector-search": {
      "url": "https://smithery.ai/servers/axel-belfort/vector-search"
    }
  }
}

Trae — .trae/mcp.json (remote)

  1. Trae → Settings → MCP, or edit .trae/mcp.json (project) / global mcp.json
  2. Use Manual add / Raw JSON, click Copy config and paste; merge only this mcpServers entry
  3. Save, pick an MCP-capable agent (e.g. Builder with MCP), reload window if needed
{
  "mcpServers": {
    "io-smithery-axel-belfort-vector-search": {
      "url": "https://smithery.ai/servers/axel-belfort/vector-search"
    }
  }
}

Cherry Studio — MCP settings (remote)

  1. Cherry Studio → Settings → MCP Servers
  2. Add via JSON import or SSE/HTTP with the URL
  3. Click Copy config, paste the mcpServers entry, then enable the server
{
  "mcpServers": {
    "io-smithery-axel-belfort-vector-search": {
      "url": "https://smithery.ai/servers/axel-belfort/vector-search"
    }
  }
}

Tongyi Lingma — MCP config (remote)

  1. Tongyi Lingma: avatar → Settings → MCP (agent mode; plugin ~v2.5+)
  2. Click + → config file or manual SSE/HTTP with the service URL
  3. Click Copy config and merge JSON; a healthy icon means connected
{
  "mcpServers": {
    "io-smithery-axel-belfort-vector-search": {
      "url": "https://smithery.ai/servers/axel-belfort/vector-search"
    }
  }
}

Cline — MCP Servers (remote)

  1. VS Code Cline panel → settings gear → MCP Servers
  2. Click Copy config and paste the mcpServers entry into Cline
  3. Save; Cline reconnects and shows tools
{
  "mcpServers": {
    "io-smithery-axel-belfort-vector-search": {
      "url": "https://smithery.ai/servers/axel-belfort/vector-search"
    }
  }
}

WorkBuddy — .workbuddy/mcp.json (remote)

  1. WorkBuddy: sidebar Plugins → MCP Servers → Configure MCP; or edit ~/.workbuddy/mcp.json (user) / .workbuddy/mcp.json (project)
  2. Click Copy config and merge under mcpServers (Tencent WorkBuddy / CodeBuddy)
  3. Save; status should turn green — fully restart the client if needed
{
  "mcpServers": {
    "io-smithery-axel-belfort-vector-search": {
      "url": "https://smithery.ai/servers/axel-belfort/vector-search"
    }
  }
}

Windsurf — mcp_config.json (remote)

  1. Edit ~/.codeium/windsurf/mcp_config.json (Windows: %USERPROFILE%\.codeium\windsurf\mcp_config.json)
  2. Click Copy config and merge; remote MCP must use serverUrl (not url)
  3. Save and refresh MCP in Windsurf Cascade
{
  "mcpServers": {
    "io-smithery-axel-belfort-vector-search": {
      "serverUrl": "https://smithery.ai/servers/axel-belfort/vector-search"
    }
  }
}

Troubleshooting & Common ErrorsFAQ

Common connection errors and verified fixes for Vector Search — In-Memory TF-IDF Semantic Store

Getting 'connection closed' or exit code 1 in Cursor / Claude Code for Vector Search — In-Memory TF-IDF Semantic Store?

Usually caused by missing runtime paths or IDE environment inheritance. Fix steps: 1. Verify Node.js 18+ (npx) or Python 3.10+ (uvx) is installed; 2. Run 'which npx' or 'which uvx' in terminal, and replace 'command' with absolute path; 3. Reload or restart the client window after modifying config.

Fix Snippet
# Check binary path in terminal:
which npx
node -v
Getting 'spawn npx ENOENT' or 'command not found'?

The editor background process does not inherit your full terminal PATH. Solution: Globally install the package, or set the absolute binary path (e.g. C:\Program Files\nodejs\npx.cmd on Windows, or /usr/local/bin/npx on macOS).

Tool calls timing out or failing to download packages?

First-time package downloading might be slow or timeout due to network limits. Configure a reliable mirror or check outbound proxy settings.

Still not connecting? Read the MCP troubleshooting guide →

Tool Mock Playground

Sandbox

Vector Search — In-Memory TF-IDF Semantic Store Tool Interface Simulation · Preview tool schema & outputs without local runtime

Sandbox Ready
fn: io_smithery_axel_belfort_vector_Execute the core tool interface of Vector Search — In-Memory TF-IDF Semantic Store
Request ArgumentsJSON Schema
{
  "target": "Vector Search — In-Memory TF-IDF Semantic Store",
  "action": "execute",
  "options": {
    "mode": "standard",
    "timeoutMs": 5000
  }
}
💡Parameters generated dynamically by Agent runtime
Agent Tool Output

Click 'Run Mock' above

to preview the raw response returned to the LLM

Env: AgentHub Virtual SandboxJSON-RPC 2.0

Decision Guide: Why & When to Use

Assess suitability before installing to save trial-and-error time

Best Suited For
  • When your AI Agent needs tool calling capabilities in this domain
  • Automating repetitive workflows in your IDE or terminal
  • Pairing with modern clients supporting MCP/Skill standards
When NOT to Use
  • When missing required API secrets or local runtime prerequisites
  • Pure conversational requests that don't need external system access
Recommended Workflow Pairing:

Vector Search — In-Memory TF-IDF Semantic Store + Scenario Prompt → Complete Agent Automation

MCP hands-on: install to visible results

Follow the full lab (expected UI/output + contrast checks). After installing this item, verify with the tutorial prompts.

Open tutorial →

Related resources

Often paired with

PC Build Assistant

v0.1.43

SkillClawHub5.1k

io.clawhub.gongyu0918-debug/pc-builder-assistant

Use for budget desktop PC build planning and recommendations, PC hardware DIY, upgrades, configuration completion, compatibility checks, hardware guidance, local LLM GPU/VRAM/RAM sizing, and gaming, streaming, creator, aesthetic, compact or ITX builds. Bundled prices are China-market CNY references; explicit user overlays keep currencies separate. 中文支持预算装机、装机 DIY、整机推荐、旧机升级、配置补全、搭配或兼容检查、硬件问答，以及游戏直播、生产力、本地 AI、外观海景房和紧凑或 ITX 主机。 Do not use for laptops, server procurement, ordering or payment, remote control, security isolation, or standalone software, game or agent tutorials.

source
View on ClawHub

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Copy either snippet into your project homepage, docs, or GitHub README. The badge is a hotlinked SVG — nothing to host — and it links back to this page so visitors can find the install steps.

PreviewListed on AgentHub: Vector Search — In-Memory TF-IDF Semantic Store
HTML
<a href="https://myagenthub.cn/p/io.smithery.axel-belfort.vector-search" title="Listed on AgentHub: Vector Search — In-Memory TF-IDF Semantic Store" target="_blank" rel="noopener">
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</a>
Markdown (GitHub README)
[![Listed on AgentHub: Vector Search — In-Memory TF-IDF Semantic Store](https://myagenthub.cn/badge/io.smithery.axel-belfort.vector-search?lang=en)](https://myagenthub.cn/p/io.smithery.axel-belfort.vector-search)

Badges are generated on the fly from /badge/<package-id>, so name and listing changes propagate automatically. Keep the link target unchanged — it is what counts as the referral.

Unified Manifest

{
  "id": "io.smithery.axel-belfort.vector-search",
  "type": "mcp-server",
  "version": "latest",
  "displayName": "Vector Search — In-Memory TF-IDF Semantic Store",
  "description": "In-memory vector search API for AI agents. Store documents and query by semantic meaning using TF-IDF vectorization with cosine similarity. Lightweight alternative to Pinecone/Weaviate for small datasets.\n\nTools: data_vector_search.\n\nUse this for building simple RAG systems, document matching, or semantic search over small collections (< 10K docs). IMPORTANT: For web-wide search, use web_search_query instead.\n\nReturns: {results[], scores[], matchCount}. No API key required — x402 micropayment $0.005/call on Base L2.",
  "iconUrl": "https://www.google.com/s2/favicons?domain=github.com&sz=64",
  "homepage": "https://github.com/Br0ski777/vector-search-x402",
  "distribution": {
    "packages": [],
    "remotes": [
      {
        "transport": "streamable-http",
        "url": "https://smithery.ai/servers/axel-belfort/vector-search"
      }
    ]
  },
  "dependencies": [],
  "installTargets": [
    "claude-code",
    "claude-desktop",
    "cursor",
    "vscode"
  ],
  "keywords": [
    "use_count:3580",
    "remote"
  ],
  "provenance": {
    "origin": "smithery",
    "originalId": "axel-belfort/vector-search",
    "originalUrl": "https://smithery.ai/servers/axel-belfort/vector-search",
    "isOfficial": false,
    "status": "active"
  }
}