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campaignstack_get_ad_analytics

Read-onlyIdempotent

Get raw daily ad metric rows (spend, impressions, clicks, conversions, leads, cost per lead) for one aggregation level: account, campaignGroup, campaign, or creative. Dates are YYYY-MM-DD, inclusive. For a summarized view use campaignstack_get_ad_dashboard.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endDateYesYYYY-MM-DD (inclusive)
platformNoAd platform. Only 'linkedin' is supported today.linkedin
startDateYesYYYY-MM-DD (inclusive)
entityTypeYesAggregation level of the daily metric rows
workspaceIdNoDefaults to the API key's workspace

Schema Changelog

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

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already establish readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds meaningful behavioral context beyond those annotations: it returns raw daily rows, uses inclusive YYYY-MM-DD dates, and operates at exactly one aggregation level per call. This helps set expectations without contradicting the 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?

Three sentences, each earning its place: the first defines the resource and metrics, the second specifies date format, and the third routes to the alternative tool. The most important scoping detail ('raw daily' and 'one aggregation level') is front-loaded. No filler or redundant restatement of the schema.

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?

Given the tool's moderate complexity, full schema coverage, and safe annotations, the description covers the essential decision points: what is returned, at what granularity, over what date range, and which sibling to choose for a different need. The absence of an output schema is partially compensated by explicitly listing the returned metric fields. No critical information is missing.

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%, so the schema already documents every parameter, including the date format, entityType enum, platform default, and workspaceId fallback. The description adds little parameter-level meaning beyond what the schema provides; it only reinforces 'aggregation level' and the inclusive date format. 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?

The description uses a specific verb ('Get') and resource ('raw daily ad metric rows'), enumerates the exact metrics returned, and lists all four aggregation levels. It also explicitly contrasts itself with campaignstack_get_ad_dashboard ('summarized view'), making its purpose unmistakable.

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?

The description clearly states when to use this tool: when raw daily metric rows are needed. It names the alternative tool (campaignstack_get_ad_dashboard) and the condition ('summarized view') that should select that sibling instead. This gives an agent actionable routing guidance.

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