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campaignstack_seo_list_prompts

Read-onlyIdempotent

List the workspace's AI-visibility probe prompts (buyer questions asked to AI assistants each audit), with source (auto = generated from ICP data, user = hand-written), active state, and brand detection terms. Also returns the cost state: weekly-run settings, overagePromptCount, weeklyOverageCredits (per week when the overage opt-in is on), and perAuditProbeCredits for a manual audit at the current set.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workspaceIdNoWorkspace ID (required for user keys; workspace keys are bound)

Schema Changelog

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

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already cover readOnly, idempotent, and non-destructive behavior. The description adds useful behavioral context by explaining what data is returned, what 'source' means, and clarifying cost-state semantics such as weeklyOverageCredits and perAuditProbeCredits. No contradiction with annotations.

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 two sentences with no filler. It front-loads the primary listing purpose and then adds the secondary cost-state returns, with parentheticals that clarify technical terms without bloating the text.

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?

Given there is no output schema and only one optional parameter, the description provides enough detail about return contents (prompts, source, active state, brand terms, cost settings) for an agent to select and invoke the tool correctly. It doesn't discuss pagination or response structure, but for this simple read-only list tool, the provided context is strong.

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 description coverage is 100%, and the single parameter (workspaceId) is already well-documented in the schema. The tool description adds no new parameter-specific meaning, so the baseline of 3 is appropriate since the schema carries the parameter documentation burden.

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 lists the workspace's AI-visibility probe prompts, with specific fields (source, active state, brand detection terms) and cost state details. This is a specific verb+resource combination that distinguishes it from the many other list tools and from seo_update_prompts.

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 makes its use case clear: when you need to view AI-visibility probe prompts and associated cost/run settings. It doesn't explicitly name alternatives or exclusions, but the read/list nature is evident from the phrasing and contrasts implicitly with update/run siblings, providing adequate context.

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

A3.6/5.0
Disambiguation3/5

Many tools share the same verb prefix (create_, list_, update_, get_) across closely related resources, so pairs like add_lead_to_external_list vs add_lead_to_sequence, create_signal_agent vs create_signal_watch, and approve_review vs approve_content_post can be confused. The descriptions are unusually detailed and cross-referenced, which mitigates but does not eliminate the ambiguity inherent in a 282-tool surface.

Naming Consistency4/5

Virtually every tool follows the campaignstack_verb_noun snake_case pattern, which is highly predictable. Minor deviations exist: destructive operations mix remove_ and delete_ (remove_lead_list vs delete_campaign), AI generation uses both craft_ and generate_, and the seo_/search_console_ subdomains introduce a second prefix convention.

Tool Count1/5

282 tools is an extreme mismatch by any reasonable standard, exceeding the 50+ threshold by more than 5x. Even for a full B2B outreach platform, this surface is far too large and would be better consolidated into higher-level operations or grouped sub-servers.

Completeness4/5

The tool surface is impressively comprehensive, covering campaigns, workflows, leads, content, ads, SEO, integrations, billing, and more with CRUD-level depth. Minor gaps remain: no single-ICP getter, no direct pause/delete for search watches, and no explicit delete for ad campaigns (only archive via update).

Resources