get_unified_ad_reportGet a cross-platform advertising performance report.
Aggregates metrics across all connected ad platforms into a single view. Useful for comparing performance across Meta, Google, LinkedIn, TikTok, etc.
| Name | Required | Description | Default |
|---|---|---|---|
| endDate | Yes | End date | |
| groupBy | No | Group results by dimension | |
| startDate | Yes | Start date |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only/destructive hints. The description adds the aggregation behavior and multi-platform scope, but does not disclose return structure, metric definitions, or potential latency. Given annotations, this is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with purpose, and contains no filler. Every sentence contributes.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description would benefit from listing what metrics are included (e.g., impressions, clicks, spend) and clarifying grouping behavior. Without an output schema, the agent may not know what to expect. However, for a read-only report with clear scope, it's passable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All three parameters have schema descriptions covering 100%, so the baseline is 3. The description does not add any parameter-specific guidance beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb ('Get') and resource ('cross-platform advertising performance report'), and the aggregation detail distinguishes it from single-platform siblings like get_ad_performance.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides usage context ('useful for comparing performance across Meta, Google, LinkedIn, TikTok'), which implies a cross-platform comparison use case, but stops short of explicitly naming excluded scenarios or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
With 148 tools, there is significant overlap. For example, generate_content, publish_ai, generate_post_bundle, and request_project_content all generate content; get_analytics, get_unified_analytics, get_post_analytics, get_ad_performance, and get_unified_ad_report all fetch performance metrics; and list_inbox vs list_conversations blur comment and conversation management. Descriptions help, but boundaries between tools are often unclear.
Most tools follow a verb_noun pattern (e.g., list_teams, create_goal, delete_post), but there are notable deviations: create_library_item vs save_to_library, publish_content vs publish_ai, schedule_content vs schedule_content_advanced, and connect_platform vs connect_connector. Mixed prefixes like 'autopilot_', 'check_', and 'get_' are fine, but overlapping verbs and a hyphen in 'connect_linkedin-page' reduce consistency.
148 tools is extreme for any server. Even for a broad social media management platform, this is far beyond what an agent can effectively navigate. The count is unwieldy and suggests the surface should be split into multiple focused servers (publishing, analytics, connectors, workflows, etc.).
The core social publishing workflow is well covered (create, schedule, publish, edit, delete, retry), and there are extensive features for analytics, workflows, connectors, and AI agents. However, some resources have CRUD gaps: no update/delete for brand voices, no delete_project, no update/delete for Product Hunt goals, and no explicit get_workflow. These are workable but notable omissions.