What is the key difference between Analook — Competitor Intelligence and TensorFeed?

Analook — Competitor Intelligence belongs to the Finance & Fintech category with cloud HTTP/SSE execution, while TensorFeed belongs to the Finance & Fintech category with local stdio execution.

How do I install Analook — Competitor Intelligence or TensorFeed in Claude Desktop or Cursor?

Copy the mcpServers JSON configuration snippet provided on this page into your claude_desktop_config.json or ~/.cursor/mcp.json file and restart your AI client.

Are Analook — Competitor Intelligence and TensorFeed free or paid?

Analook — Competitor Intelligence operates under a Free / Open Source model (No auth required). TensorFeed operates under a Free / Open Source model (No auth required).

Which MCP server has more tools and community activity?

Analook — Competitor Intelligence lists 0 tools with 0 views and 105 GitHub stars. TensorFeed lists 0 tools with 0 views and 2 GitHub stars.

Can I use both Analook — Competitor Intelligence and TensorFeed together in the same MCP host?

Yes! MCP hosts like Claude Desktop, Cursor, Windsurf, Cline, and VS Code support configuring multiple servers under the mcpServers object simultaneously.

How do I install Analook — Competitor Intelligence or TensorFeed in Claude Desktop, Cursor, or Windsurf?

Both servers follow the standard Model Context Protocol configuration format. Simply copy the JSON block from the configuration section above and paste it into your AI client's mcpServers configuration object (for instance in claude_desktop_config.json or ~/.cursor/mcp.json ), then completely restart or refresh the application.

Are Analook — Competitor Intelligence and TensorFeed free to use, or do they require API keys?

Analook — Competitor Intelligence is listed under a Free / Open Source model with No auth required . TensorFeed operates under a Free / Open Source model with No auth required . If environment variables are required (such as API keys), be sure to define them under the env key in your MCP client's configuration file.

Can I run both Analook — Competitor Intelligence and TensorFeed at the same time in my AI client?

Yes! MCP clients support multi-server orchestration. You can include both analook-competitor-intelligence and tensorfeed as distinct keys inside the single mcpServers object in your configuration file. Your AI assistant will automatically select and call the appropriate tool from either server during chat sessions.

Which MCP server has higher directory engagement and quality ratings?

On AllMCPs, Analook — Competitor Intelligence has a Quality Score of 45 /100 ( Fair ) with 0 views, 0 installs, and 105 GitHub stars. TensorFeed holds a Quality Score of 41 /100 ( Fair ) with 0 views, 0 installs, and 2 GitHub stars.

Side-by-Side Model Context Protocol Comparison

Analook — Competitor Intelligence vs TensorFeed

In-depth architectural comparison of the Analook — Competitor Intelligence and TensorFeed MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.

At a Glance & Executive Verdict

Analook — Competitor Intelligence

Finance & Fintech · Remote HTTP/SSE

Quality: 45/100 (Fair) | Auth: No auth required

TensorFeed

Finance & Fintech · Local stdio

Quality: 41/100 (Fair) | Auth: No auth required

Verdict Summary: Choose Analook — Competitor Intelligence if you need specialized Finance & Fintech tools running via a hosted cloud SSE transport. Choose TensorFeed if your workspace requires Finance & Fintech integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.

Which MCP Server Should You Choose?

Choose Analook — Competitor Intelligence when:

You need dedicated capabilities in the Finance & Fintech domain.
You prefer remote streaming HTTP/SSE transport architecture.
Your security boundary fits: No auth required (Free / Open Source).

Explore Analook — Competitor Intelligence Details

Choose TensorFeed when:

You need dedicated capabilities in the Finance & Fintech domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).

Explore TensorFeed Details

Feature & Specification Comparison

Specification	A Analook — Competitor Intelligence Finance & Fintech	TensorFeed Finance & Fintech
Summary	Competitor intelligence for AI agents — SEO, traffic, social, Product Hunt, pricing, AI insights.	AI news, model pricing, service status, and machine-payable premium tools. AFTA-certified.
Category & Scope	Finance & Fintech	Finance & Fintech
Quality signal

Tools & Capabilities Breakdown

Analook — Competitor Intelligence Tools (0)

No explicit tool names declared in metadata yet. Check project README on main listing page.

TensorFeed Tools (0)

No explicit tool names declared in metadata yet. Check project README on main listing page.

Ready-to-Paste Client Configurations

Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).

Analook — Competitor Intelligence Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "analook-competitor-intelligence": {
      "url": "https://www.analook.com/assets/favicon-512x512.png"
    }
  }
}

TensorFeed Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "tensorfeed": {
      "command": "npx",
      "args": [
        "-y",
        "@tensorfeed/mcp-server"
      ]
    }
  }
}

Frequently Asked Questions

Analook — Competitor Intelligence is categorized under Finance & Fintech and uses a remote streaming HTTP/SSE transport. In contrast, TensorFeed belongs to Finance & Fintech using local stdio subprocess. Select Analook — Competitor Intelligence when you need capabilities focused on finance & fintech and TensorFeed when you require tools for finance & fintech.

More alternatives to Analook — Competitor Intelligence More alternatives to TensorFeed Finance & Fintech category hub