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assist_classify_keywords

Destructive

OFFLOAD keyword classification to XeoRank's server-side model instead of reasoning over keyword lists yourself — saves your own tokens. Classifies intent (informational/commercial/transactional/navigational), funnel_stage (tofu/mofu/bofu), and cluster_name for tracked keywords, and stores the results. Omit keywords to classify every unclassified tracked keyword on the site. Costs Assist credits (~1/keyword); prefer this over classify_tracked_keywords when a balance is available.

Input Schema

NameRequiredDescriptionDefault
storeNoPersist onto tracked keywords (default true)
site_idYesSite UUID (from list_sites)
industryNoOptional industry/context hint to sharpen classification
keywordsNoOptional JSON array of keyword strings; omit to classify all unclassified tracked keywords

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare destructiveHint=true, idempotentHint=false, and openWorldHint=true, so the safety profile is covered structurally. The description adds valuable beyond-schema context — cost (~1 Assist credit/keyword) and that results are stored — but does not clarify what the destructiveHint actually destroys (e.g. whether existing intent/funnel values are overwritten when store=true).

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?

Front-loaded with the core action and the token-saving rationale, and each sentence carries signal (what it classifies, the omit-default, the cost, the sibling preference). Slightly padded by the 'saves your own tokens' framing, but overall tight.

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?

For a mutation tool with no output schema but full annotation coverage, the description covers purpose, storage behavior, cost, and the sibling alternative, which is nearly everything an agent needs. It stops short of explaining the destructive/overwrite semantics flagged by the annotations.

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%, so the schema already documents all four parameters including the 'omit to classify all unclassified' behavior of keywords. The description largely restates that, adding no syntax or format detail beyond the schema, so the baseline 3 is appropriate.

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 ('Classifies intent, funnel_stage, and cluster_name for tracked keywords') and immediately distinguishes itself from the sibling by explaining it is a server-side offload rather than local reasoning. An agent can tell it apart from classify_tracked_keywords without opening either schema.

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

Usage Guidelines5/5

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

Gives an explicit routing rule: 'prefer this over classify_tracked_keywords when a balance is available,' plus the operational condition 'Omit keywords to classify every unclassified tracked keyword.' It names the alternative and the selecting condition, leaving little 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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