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Glama

Growthr SEO + GEO MCP

Order a site's fixes by effort

growthr_fix_order
Read-onlyIdempotent

Run the scan and sort every failed check into buckets: blockers (fix before anything else, they hide everything downstream), this afternoon (metadata, llms.txt, trust pages, on-page), needs a sprint (schema, markdown negotiation, speed), and the off-site work no scan can measure (reviews, directories, third-party mentions), which is what decides whether AI engines recommend a business.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesPublic domain, e.g. example.com

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
otherNo
scoreYes
domainYes
sprintYes
blockersYesCheck ids to fix before anything else
afternoonYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

The description says 'Run the scan,' implying a read-only action, and annotations confirm read-only and non-destructive behavior, but it does not call out side effects or limits beyond what annotations already state.

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

Conciseness3/5

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

The description is one long sentence with stylized bucket names and explanatory asides; it is informative but somewhat wordy, and the front-loaded purpose is somewhat buried by the extended categorization.

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?

The description explains the output buckets and their significance, and with the output schema present it gives enough context for an agent to know what to expect from a call.

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?

Only one parameter, domain, is documented in the schema with a clear example. The description adds no further detail about the parameter, so it relies on the schema.

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 specifies the tool's purpose: it runs a scan and sorts failed checks into prioritized buckets, with category examples. This distinguishes it from sibling scan/visibility tools by emphasizing prioritization.

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?

It describes what the tool does but does not explicitly state when to choose it over the sibling scan or visibility tools; no direct when-to-use guidance is provided.

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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TDQS

A4.1/5.0
Disambiguation5/5

Each tool targets a clearly distinct deliverable: technical audit, AI visibility check, llms.txt draft, and fix prioritization. The outputs and workflows described are separate enough that an agent should not confuse one tool for another.

Naming Consistency3/5

All tools share the growthr_ prefix and snake_case, which provides some consistency, but the post-prefix naming is mixed: 'scan' and 'fix_order' are verb-led while 'ai_visibility' and 'llms_txt' are noun phrases. There is no consistent verb_noun pattern across the set.

Tool Count5/5

Four tools is a well-scoped size for an SEO/GEO audit server, and each tool has a distinct, non-redundant role. This is within the ideal range and does not feel either sparse or bloated.

Completeness4/5

The tool set covers the core lifecycle of auditing, measuring AI visibility, generating llms.txt, and prioritizing fixes. Minor gaps exist—such as no explicit historical tracking or content-optimization tool—but the main workflow is usable without dead ends.

Resources