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semrush-ai-mcp

Keyword Difficulty

semrush_keyword_difficulty

Score up to 100 keywords at once by Google top-10 ranking difficulty (0-100) to prioritize SEO targets and assess competition across regional databases.

Instructions

Keyword Difficulty index (0-100): how hard it is to rank in Google's top 10. Accepts up to 100 keywords at once for batch scoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
phrasesYesList of keywords to score (max 100)
databaseNoRegional database code, e.g. us, uk, de, fr, es, it, br, au, ca, in (default: us)us

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses the output scale and batch limit, but says nothing about permissions, rate limits, error behavior, or how database selection affects results. For a tool with zero annotation coverage, key behavioral traits are missing.

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

Conciseness4/5

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

Two tight sentences with zero filler, and the core definition is front-loaded ahead of the batch detail. Efficient and readable, though it ends without routing context.

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?

For a simple two-parameter tool with no output schema, the description adequately explains the return value's meaning (0-100 scale). However, it omits how the database choice influences scores and offers no selection guidance among the many keyword siblings, leaving gaps an agent would want closed.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents both parameters fully. The description's 'up to 100 keywords at once' mirrors the phrases maxItems constraint but adds no new semantics, and the database parameter is left to the schema. Baseline 3 applies when structured data does the heavy lifting.

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?

The description gives a specific resource (Keyword Difficulty index) with a defined output scale (0-100) and states its meaning ('how hard it is to rank in Google's top 10'), plus the batch-scoring capability. This is a clear verb/resource statement, but it never positions itself against keyword siblings like semrush_keyword_overview or semrush_keyword_serp, so an agent must infer which keyword tool to pick.

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?

There is no explicit when-to-use guidance, no mention of alternatives, and no exclusions. The description implies usage (scoring keywords to gauge ranking difficulty) but does not tell the agent when to choose this over semrush_keyword_overview or related keyword tools.

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