free-seo-mcp

expand_alphabet

Read-onlyIdempotent

Expand a keyword seed into alphabet-suffixed variations, ranked by popularity with occurrence counts. Get broader keyword research from autocomplete sources.

Instructions

Expand a seed in breadth: the seed itself plus "seed a", "seed b"... per alphabet.

Returns unique phrases ranked by a popularity score (appearances weighted by list position) with occurrence counts. sources defaults to ["google"]. Reports progress.

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": {
      "keyword": "<keyword>"
    },
    "name": "expand_alphabet"
  }
}

Input Schema

Name	Required	Default
`gl`	No	us
`hl`	No	en
`keyword`	Yes
`sources`	No

Output Schema

Name	Required	Description
`gl`	Yes
`hl`	Yes
`errors`	No	Failed source/query pairs
`keyword`	Yes
`phrases`	Yes
`queries`	Yes	Expansion queries sent per source
`sources`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 25, 2026

TDQS

B3.2/5.0

Behavior4/5

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

Annotations already cover readOnlyHint, idempotentHint, and openWorldHint. The description adds useful behavioral details beyond those: it returns unique phrases ranked by a popularity score, includes occurrence counts, defaults sources to ['google'], and reports progress. No contradiction with annotations exists.

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?

The description is compact and front-loaded with the core behavior, followed by return characteristics and defaults. It earns its sentences, though the final two clauses could be better integrated and slightly more structured.

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?

The presence of an output schema reduces the need to describe return values, and annotations cover safety and idempotency. However, the description still leaves gaps around how the alphabet is determined, what gl/hl control, and how to choose this tool among similar expand/suggest siblings.

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?

Schema description coverage is 0%, so the description carries the burden of explaining parameters. It only clarifies that sources defaults to ['google']. The meaning of keyword, gl, and hl is left implicit or unexplained, and 'per alphabet' is not tied to any parameter.

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 states a specific action ('Expand a seed in breadth') and defines the resource (the seed plus alphabet suffixes). It is clear what the tool does and roughly how it differs from general suggestion tools, though it does not explicitly distinguish itself from expand_questions or expand_multilingual.

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 explicit guidance is given for when to use this tool versus its siblings. The phrase 'expand a seed in breadth' implies a use case, but there are no stated exclusions, alternatives, or conditions that would help an agent choose this over expand_questions or expand_multilingual.

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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