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campaignstack_edit_and_approve_review

Edit the AI-generated content and approve the review entry. The original content is preserved for audit trail. Draft parked at a review node: the lead advances via the 'approved' edge. Escalated entry parked at the acting node: the withheld action is resumed with your edited text (re-dispatched or released through the delivery exit).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entryIdYes
workspaceIdYes
updatedContentYes

Schema Changelog

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

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Beyond the annotations, which only indicate the operation is not read-only and not idempotent, the description adds meaningful behavioral detail: the original content is preserved for audit trail, the lead advances via the 'approved' edge, and escalated actions are resumed with the edited text. This gives the agent a clearer picture of side effects and workflow impact.

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 compact and front-loaded, with the core action in the first sentence and supporting workflow details in the next two sentences. Every sentence provides useful information, and there is no filler or repeated schema content.

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

Completeness3/5

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

The description explains the two workflow scenarios and their outcomes well, which is helpful for tool selection. However, it lacks parameter-level guidance and does not describe the expected shape of updatedContent or what the caller should provide beyond 'edited text,' leaving some invocation ambiguity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for the lack of parameter documentation. It only hints at updatedContent through 'your edited text' and never explains the meaning or expected structure of entryId, workspaceId, or updatedContent. The agent is left to infer parameter roles from names alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: edit AI-generated content and approve a review entry. It implicitly differentiates itself from related siblings like approve_review by adding the editing step, but it does not explicitly name any sibling or contrast itself with them.

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 provides clear contextual usage by describing two distinct workflow states: a draft parked at a review node and an escalated entry parked at the acting node. It does not explicitly say when not to use the tool or name alternative tools such as approve_review or reject_review, but the context is clearly scoped.

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