An official Model Context Protocol (MCP) server for TheNicheGap.
This server allows AI assistants (like Claude Desktop) to connect directly to TheNicheGap's real-time SEO analysis engine. Instead of relying on LLM hallucinations, your AI assistant can fetch real Google top-10 SERP data, analyze content gaps, and write highly differentiated SEO articles.
Real SERP Data: Fetches actual top-10 rankings for any given keyword.
Content Gap Analysis: Identifies what the top-10 unanimously cover (Must-Haves) and what they miss (Gaps).
Automated Outline: Returns an SEO-optimized H1/H2/H3 structure with CTR-optimized title suggestions.
Related MCP server: SEO MCP Server
To use this MCP server, you need a TheNicheGap API Key.
Log in to your TheNicheGap Dashboard.
Generate an API Key in the Developer section.
Open your Claude Desktop configuration file:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Add the TheNicheGap MCP Server to the mcpServers section:
{
"mcpServers": {
"thenichegap": {
"command": "npx",
"args": [
"-y",
"@thenichegap/mcp-server"
],
"env": {
"THENICHEGAP_API_KEY": "your_api_key_here"
}
}
}
}Restart Claude Desktop. You will now see a tool icon (hammer) in the Claude interface indicating that TheNicheGap tools are available.
Once installed, you can simply ask Claude to use TheNicheGap:
"Analyze the keyword 'best standing desk' using TheNicheGap. What are the content gaps?"
"Call TheNicheGap for 'how to lose weight'. Based on the Must-Haves and Gaps it returns, write a complete 2000-word blog post for me."
Clone this repository.
Run npm install
Build the server: npm run build
Test locally using the MCP Inspector:
THENICHEGAP_API_KEY=your_key_here npx @modelcontextprotocol/inspector node build/index.jsMIT
Analyze an SEO keyword to find search intent, industry must-haves, and content gaps using real SERP data from TheNicheGap. Use this to help craft highly differentiated articles.
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | The language locale code for the analysis (e.g., 'en', 'zh'). Defaults to 'en'. | |
| keyword | Yes | The target SEO keyword to analyze (e.g., 'best standing desk'). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full behavioral burden. It mentions using 'real SERP data from TheNicheGap,' which hints at an external data source, but does not disclose read-only status, authentication needs, rate limits, latency, or return behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tightly written sentences with no waste. The purpose and intended use are front-loaded and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-parameter analysis tool with no output schema and no annotations, the description covers purpose, outputs, and a usage context. However, it omits return structure and operational details that would help an agent invoke it confidently.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so both parameters are already documented. The description does not add any parameter meaning beyond the schema, making the baseline 3 appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb (analyze) and resource (SEO keyword), and names the outputs: search intent, industry must-haves, content gaps. It also identifies the data source. No siblings exist to differentiate from, so it falls short of a 5 only in that dimension.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Gives one clear use case: 'Use this to help craft highly differentiated articles.' It does not state when not to use it, prerequisites, or alternatives, so usage is implied rather than fully guided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0analyze_niche_gapScored across 1 tool
There is only one tool, so there is no overlap or misselection risk between tools. Its purpose is explicit and distinct.
The single tool follows a clear snake_case verb_noun naming pattern. With no other tools, there are no inconsistent conventions present.
One tool is borderline thin for a server surface, though it may be acceptable for a tightly scoped niche-analysis endpoint. The scale treats 1-2 tools as borderline.
The tool covers the core described operation of analyzing an SEO keyword for intent and content gaps. Minor gaps remain, such as batch analysis or adjacent keyword-discovery operations, but the primary use case is covered.
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