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kd-scout

kd-scout

by moiosintel
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

keyword_difficulty

Estimate keyword difficulty (0-100) from SERP signals like word count, brand presence, and commercial intent to rank content opportunities.

Instructions

Estimate keyword difficulty (0-100) from SERP signal inputs: number of words, presence of brand terms, and commercial intent markers.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "keywords": "<keywords>"
    },
    "name": "keyword_difficulty"
  }
}

Input Schema

NameRequiredDescriptionDefault
keywordsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description explains the scoring range and the concrete signals used (number of words, brand terms, commercial intent markers). However, with no annotations provided, it does not fully carry the behavioral transparency burden: it omits output shape for multiple keywords and what low versus high scores imply.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

One sentence that is properly front-loaded with the core action, and every clause adds context. No filler, repetition, or unnecessary qualification.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Adequate for a simple single-parameter tool: it gives a clear output range and the key signal inputs. It omits what a returned score represents in each direction and how batches of keywords are returned, leaving the agent with some ambiguity for a batch array input.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema describes only a `keywords` array with 0% field description coverage, so the description must clarify the relationship between the input and the signals. It names signals like brand terms and commercial intent markers but never says these are derived from the `keywords` parameter, potentially implying buyable signals the schema does not support.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a clear verb ('Estimate') and resource ('keyword difficulty') with an explicit 0-100 output range. The signal list helps clarify what kind of assessment it makes, though it does not explicitly name sibling tools to differentiate them within the description itself.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given about when to use keyword_difficulty versus opportunity_score or content_brief. There are no conditions, exclusions, or alternative routing cues present in the description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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