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MCP DataForSEO Server

MCP DataForSEO Server

by kevinmaqueda
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

get_keyword_difficulty

Assess keyword difficulty scores to gauge ranking competition. Get search volume and competition level for targeted keywords.

Instructions

Get keyword difficulty scores (0-100) for keywords. Higher scores mean harder to rank. Also returns search volume and competition level.

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": "get_keyword_difficulty"
  }
}

Input Schema

NameRequiredDescriptionDefault
keywordsYesList of keywords to analyze (max 1000)
country_codeNoCountry code. Available: ES, MX, AR, CO, US, GB, DE, FR, IT, PT, NL, BE, AT, CH, IE, PL, CZ, SE, DK, NO, FI, GR, HU, RO, BG, HR, SK, SI, LT, LV, EE, BR, CL, PEES

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations provided, the description must carry the behavioral disclosure burden. It adds useful context about score interpretation (higher = harder to rank) and mentions additional returned data (search volume, competition level). However, it lacks explicit safety guarantees (e.g., read-only nature) or details about request limits or handling beyond what is in the schema.

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?

The description is concise and well-structured: two sentences that front-load the primary purpose and immediately explain the scale meaning. There is no redundant information or unnecessary details.

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?

Given no output schema, the description appropriately mentions key return values (search volume, competition level). It is sufficiently complete for a simple lookup tool, though it could hint at response format or error behavior. The schema fills the remaining parameter context, making this a well-rounded description overall.

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?

The input schema already covers both parameters thoroughly, with descriptions for keywords and country_code (including the default and list of available codes). The description adds no extra parameter-specific meaning, so the baseline of 3 applies due to high schema coverage.

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 the tool's function: getting keyword difficulty scores with a specific range (0-100). It uses a specific verb ('Get') and resource ('keyword difficulty scores'), and it distinguishes itself from sibling tools like get_keyword_trends or get_keyword_suggestions by focusing on difficulty.

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 description implies when to use the tool (when you need keyword difficulty data) but does not explicitly mention alternatives or exclusion scenarios. There is no guidance on when to prefer this over other keyword-related tools, leaving usage context to inference.

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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