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campaignstack_approve_review

Approve a pending review entry. For a draft parked at a review node, the lead advances via the 'approved' edge. For an escalated entry parked at the acting node itself (critic-flagged auto-send, agent escalation), approval RESUMES the withheld action: the runner action is re-dispatched with the approved text, or the draft is released through the node's delivery exit. Use campaignstack_list_pending_reviews to find entry IDs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entryIdYes
workspaceIdYes

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Description discloses that approval triggers edge traversal for drafts and resumes withheld actions for escalated entries, including re-dispatching runner actions or releasing the draft. This adds meaningful behavioral context beyond the raw annotations, which only state readOnly=false and idempotent=false.

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?

Four sentences all contribute: the first states the purpose, the next two detail the two modes, and the last gives a concrete discovery pointer. No filler or repetition of schema fields.

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?

Covers the two complex execution paths fully, so an agent can predict side effects before calling. The lack of output-schema details is acceptable here since no output schema is provided and the action semantics are thoroughly specified.

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

Parameters4/5

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

With 0% schema description coverage, the description compensates by telling the agent to use campaignstack_list_pending_reviews to find entryId, giving the key parameter actionable meaning. workspaceId remains generic, but its name is self-explanatory and common across workspace-scoped tools.

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 the verb 'Approve' and the resource 'pending review entry' with enough specificity to separate it from sibling approval tools for ad creatives and content posts. It also explains what approval means in two distinct workflow contexts, which goes beyond a generic one-liner.

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?

Explicitly identifies the two situations where the tool applies (draft at a review node, escalated entry at the acting node) and directs the agent to campaignstack_list_pending_reviews for entry IDs. It does not explicitly mention alternatives such as reject_review or retry_review, so exclusions are left to the agent, but the contexts are clear.

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

A3.7/5.0
Disambiguation3/5

The set is enormous and generally well-differentiated through detailed cross-referenced descriptions, but several clusters blur together: archive/delete/remove have inconsistent permanence semantics (delete_campaign vs remove_signal_watch vs archive_campaign), create_connection_watch_agent explicitly overlaps with set_account_watcher, and the parallel draft-checkup and playbook-proposal flows (run_draft_checkup/get_draft_checkup/accept_draft_checkup vs propose_playbook_change/get_playbook_proposal/decide_playbook_proposal) present near-identical decision pipelines.

Naming Consistency4/5

Nearly every tool follows the campaignstack_<verb>_<noun> convention with disciplined get/list pairing and consistent verb choices (create/update/delete/pause/resume). Minor deviations like campaignstack_priority_enrich (adverb+verb) and campaignstack_whoami break the strict verb_noun pattern but are isolated and do not hinder navigation.

Tool Count1/5

223 tools is an extreme surface for any MCP server. Even though each tool maps to a distinct API operation and the underlying platform is broad, the scale far exceeds the 50+ threshold for an extreme mismatch and will overwhelm agents with selection overhead.

Completeness5/5

The surface is exhaustive for the LinkedIn outreach domain: full campaign/workflow/lead-list lifecycles, ICP and persona management, content scheduling and approvals, inbox and messaging, enrichment and integrations, signal watches and exclusions, review queues, playbook versioning, workspace admin, billing, and notifications. Minor gaps like a missing delete_lead or delete_company are explained by shared-data semantics, so no critical dead ends remain.

Resources