Skip to main content
Glama
Sign In
kd-scout

kd-scout

by moiosintel
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

opportunity_score

Identify keyword potential by combining difficulty and monthly volume into a single 0-100 score. Use it to prioritize keywords with high opportunity and lower competition.

Instructions

Blend difficulty and monthly volume into a single 0-100 opportunity score (70% inverse difficulty, 30% log volume).

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": {
      "difficulty": "<difficulty>",
      "monthly_volume": "<monthly_volume>"
    },
    "name": "opportunity_score"
  }
}

Input Schema

NameRequiredDescriptionDefault
difficultyYes
monthly_volumeYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description fully carries the behavioral transparency burden. It discloses the scoring algorithm, the 0-100 output range, the weighting, and that difficulty is inversely used and volume is log-transformed. This is strong transparency, though input constraints like expected numeric ranges are not stated.

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?

A single efficient sentence delivers the purpose, formula, weights, and output range with no filler. The key information is front-loaded and easily parsed.

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

Completeness4/5

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

For a two-parameter numeric computation, the description is nearly complete, even specifying the output scale. The missing piece is explicit input constraints or edge-case guidance, but the formula is stated clearly enough for correct selection and invocation.

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

Parameters4/5

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

The input schema gives only parameter names and number types, with 0% schema description coverage. The tool description compensates by explaining the meaning of both parameters in functional terms: difficulty enters inversely and monthly_volume is log-transformed.

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

Purpose5/5

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

The description clearly states a specific action: blending difficulty and monthly volume into a single 0-100 opportunity score. It also specifies the exact weighting formula, which differentiates it from siblings like keyword_difficulty and content_brief.

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

Usage Guidelines3/5

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

The tool's own description implies the use case: you want an opportunity score combining difficulty and volume. However, it does not explicitly contrast this with the sibling tools or say when to prefer it over keyword_difficulty.

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

Deploy Server

Other Tools