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Trillboards DOOH Advertising

by io.github.snehdhruv

get_campaign_performance

Get detailed performance metrics for a campaign.

WHEN TO USE:

  • Monitoring active campaign performance

  • Reviewing completed campaign results

  • Getting per-screen impression breakdowns

RETURNS:

  • campaign_id, name, status, budget, dates

  • performance: impressions, spend_estimate_usd, avg_cpm, unique_screens, avg_latency_ms

  • screen_breakdown: per-screen impressions and CPM

EXAMPLE: User: "How is my NYC retail campaign performing?" get_campaign_performance({ campaign_id: "550e8400-e29b-41d4-a716-446655440000" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
campaign_idYesCampaign UUID returned from create_campaign

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It details the return structure (campaign_id, performance metrics, screen_breakdown) and includes an example, which clarifies the data returned. However, it does not explicitly state whether the operation is read-only or discuss side effects, though 'Get' implies read-only.

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 highly structured with clear sections (WHEN TO USE, RETURNS, EXAMPLE). Every sentence serves a purpose, and the content is front-loaded with the primary purpose statement.

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?

For a single-parameter tool with no output schema, the description compensates by listing all return fields and providing a realistic example. It covers the main use cases and gives enough context for an agent to invoke the tool correctly. No gaps identified.

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?

The input schema already documents campaign_id as a required Campaign UUID with 100% coverage. The description reinforces this with an example parameter but adds no additional semantic depth beyond what the schema provides, matching the baseline of 3.

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 'Get detailed performance metrics for a campaign,' specifying a specific verb and resource. It distinguishes from sibling tools by focusing on campaign performance metrics with per-screen breakdowns, which is unique among the many get_* tools.

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 WHEN TO USE section explicitly lists three use cases: monitoring active campaigns, reviewing completed campaigns, and getting per-screen impression breakdowns. This provides clear guidance on when to select this tool over alternatives.

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

B3.3/5.0
Disambiguation2/5

There are exact duplicates (get_task_status/tasks_get, list_tasks/tasks_list) and several overlapping analytics, attribution, and semantic search clusters (get_attention_metrics vs get_creative_attention vs get_social_attention; find_similar_moments vs semantic_search_observations; get_campaign_attribution vs get_multi_touch_attribution vs get_roas). Detailed descriptions help, but with 83 tools an agent will frequently struggle to pick the right one.

Naming Consistency3/5

Most tools follow a snake_case verb_noun pattern (list_devices, create_campaign, delete_webhook), but there are notable inconsistencies: list_* and get_* are used interchangeably for list operations, attention tools mix conventions (get_attention_metrics vs get_creative_attention vs get_social_attention), and the legacy tasks_get/tasks_list names break the established get_task_status/list_tasks pattern.

Tool Count1/5

83 tools is an extreme count for a single MCP server, spanning device management, sensing, campaigns, media buys, attribution, webhooks, billing, API discovery, and AdCP protocol concerns. This is a broad API surface dump rather than a focused tool set, and it would be far better split into several coherent servers.

Completeness2/5

Despite the enormous surface, core campaign lifecycle is incomplete: create_campaign explicitly tells the agent to use update_campaign to activate a campaign, but no update_campaign tool exists, and there are no list/delete campaign tools. Significant capabilities exist for analytics, attribution, and webhooks, but the primary advertising workflow has a dead end.

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