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MisarSEO MCP Server

get_gbp_optimization

Completeness/quality score and AI-grounded optimization recommendations for the project's linked Google Business Profile location. The score (0-100) is a DETERMINISTIC heuristic checklist over real measured facts — not AI-generated. Only recommendations comes from the model. CHARGES ONE AI-GATEWAY OPERATION against the organization's shared monthly ceiling.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectIdYesRequired. The MisarSEO project ID to scope this call to. Get one from list_projects.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

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

Goes well beyond the annotations by disclosing that only `recommendations` is AI-generated while `score` is a deterministic heuristic over measured facts, and that the call charges ONE AI-gateway operation against a shared monthly ceiling. This cost/budget disclosure is exactly the kind of behavioral context annotations (readOnly=false, openWorld=true) cannot convey.

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?

Three sentences, front-loaded with the purpose and return values, then the deterministic-vs-AI distinction, then the cost warning. No filler; every sentence carries information an agent needs to interpret the result or budget the call.

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?

With no output schema present, the description correctly compensates by explaining the two return components (score and recommendations). Combined with the fully documented parameter and the safety annotations, an agent has everything required to call and interpret this tool.

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 coverage is 100% and the single projectId parameter is already fully documented in the schema (including how to obtain it via list_projects). The description adds nothing further about the parameter, so the baseline of 3 applies.

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?

States a specific verb+resource (get GBP optimization) and precisely what the tool returns: a 0-100 completeness/quality score plus AI-grounded recommendations. It clearly distinguishes itself from siblings like get_gbp_profile, get_gbp_performance and get_gbp_reviews by characterizing its output rather than just its target.

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?

Usage is implied — you'd call this to obtain a GBP quality score and optimization advice — but the description names no alternatives and gives no explicit when-to-use/when-not conditions. The cost warning implies 'use deliberately,' yet no sibling is offered as a cheaper substitute.

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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