gapup-mcp

seo_keyword_research

Read-only

SEO keyword research from a seed keyword or topic. Uses Google Suggest (public, keyless) to discover related queries at 2 expansion levels, then clusters them by intent: informational / commercial / transactional / navigational — via heuristic pattern matching. Search volume is bucketed (very_high / high / medium / low / very_low) and clearly labelled as ESTIMATED — no fabricated precise numbers. Returns all keywords, intent clusters, quality scores (0-100), and top 10 opportunities. Supports country (gl) and language (hl) targeting. 100% keyless. Cache TTL 6h. ICP: SEO managers, content strategists, SaaS founders, agency teams.

Input Schema

TableJSON Schema

Name	Required	Description
`async`	No	If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
`country`	No	ISO 3166-1 alpha-2 country code for Google Suggest (e.g. 'US', 'FR', 'DE'). Defaults to 'US'.
`language`	No	BCP-47 language code for suggestions (e.g. 'en', 'fr', 'de', 'es'). Defaults to 'en'.
`seed_keyword`	Yes	The seed keyword or topic to research (e.g. 'invoice software', 'project management tool')

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`country`	Yes
`clusters`	Yes
`language`	Yes
`warnings`	Yes
`all_keywords`	Yes
`seed_keyword`	Yes
`quality_score`	Yes
`total_keywords`	Yes
`top_opportunities`	Yes

TDQS

A4.7/5.0

Behavior5/5

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

The description goes beyond annotations by detailing the method (Google Suggest, heuristic clustering), volume bucketing (labelled ESTIMATED), cache TTL, and output specifics (intent clusters, quality scores). There is no contradiction with annotations (readOnlyHint=true, openWorldHint=true).

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 well-structured and concise despite its length. It front-loads the purpose, then method, output, constraints (keyless, caching), and ICP. Every sentence adds value with no repetition or fluff.

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

Completeness5/5

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

Given the presence of annotations and output schema, the description is complete. It covers input parameters, underlying method, output specifics, caching behavior, and target audience. Nothing critical is missing.

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?

Schema coverage is 100%, so baseline is 3. The description adds examples for 'seed_keyword' and explains the purpose of country/language parameters in the context of Google Suggest. It adds marginal value beyond the schema by contextualizing the parameters.

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 does 'SEO keyword research from a seed keyword or topic' and explains the method (Google Suggest, intent clustering). The name and description together make the purpose immediately clear, and it distinguishes itself from sibling tools like 'seo_cro_audit' by being a research tool.

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

Usage Guidelines4/5

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

The description specifies the tool uses Google Suggest (public, keyless), supports country and language targeting, and targets ICP (SEO managers, etc.). It mentions cache TTL and that it's 100% keyless. However, it does not explicitly state when NOT to use it or provide alternatives for different keyword research needs.

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

All gapup-mcp tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

Resources

Need Help?