kd-scout
kd-scout
Zero-dependency MCP server for keyword research arithmetic: difficulty estimation, opportunity scoring, and structured content briefs. Runs anywhere Python 3.10+ runs - no packages, no API keys required for the deterministic mode.
Tools
Tool | Input | Output |
|
| per-keyword difficulty 0-100 |
|
| blended 0-100 score |
|
| H2 outline, word-count target, FAQs, schema type |
Related MCP server: mcp-gsc-advanced
Install
pip install kd-scout
kd-scout # stdio MCP serverOr point any MCP client at the module directly:
{"command": "kd-scout"}Production use
The scoring model is derived from the editorial pipeline behind Groundwork's money & tools research, where it screens hundreds of candidate keywords weekly.
License
MIT
Available Tools
3 toolscontent_briefA
Generate a structured article brief: H2 outline, word-count target, FAQ suggestions, and schema type recommendation.
Inputs: topic (required).
keyword_difficultyB
Estimate keyword difficulty (0-100) from SERP signal inputs: number of words, presence of brand terms, and commercial intent markers.
Inputs: keywords (required).
opportunity_scoreA
Blend difficulty and monthly volume into a single 0-100 opportunity score (70% inverse difficulty, 30% log volume).
Inputs: difficulty (required), monthly_volume (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose in the keyword research workflow: difficulty estimation, opportunity scoring, and content brief generation. There is meaningful sequential dependency, but no functional overlap that would confuse an agent.
All tool names follow the same lowercase snake_case pattern and describe their output: keyword_difficulty, opportunity_score, content_brief. The naming convention is predictable and easy to extend.
Three tools is perfectly scoped for this server's narrow purpose. Each tool fills a distinct step in the pipeline without redundancy or unnecessary bloat.
The set covers the core lifecycle from raw signals through difficulty and opportunity scoring to a content brief, leaving no obvious workflow dead end. It lacks built-in SERP or volume retrieval, but those appear to be external inputs rather than gaps in the server's stated scope.
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