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Trillboards DOOH Advertising

by io.github.snehdhruv

get_webhook_deliveries

Get delivery history for a webhook.

WHEN TO USE:

  • Debugging failed webhook deliveries

  • Auditing webhook activity

  • Checking delivery success rates

RETURNS:

  • deliveries: Array of delivery records with:

    • delivery_id: Unique delivery ID

    • event: Event type

    • status: success/failed

    • response_code: HTTP response code

    • response_time_ms: Response time

    • attempted_at: Attempt timestamp

    • error: Error message (if failed)

  • total: Total delivery count

  • success_rate: Percentage of successful deliveries

EXAMPLE: User: "Show me failed deliveries for this webhook" get_webhook_deliveries({ webhook_id: "wh_mmmpdbvj_8b7c5a59296d", status: "failed", limit: 20 })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of deliveries to return (default: 50, max: 100)
statusNoFilter by delivery status
webhook_idYesWebhook ID to get deliveries for (wh_xxx format or legacy ObjectId)

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses the return structure in detail (deliveries array with fields, total, success_rate) and provides an example, which goes beyond schema to explain behavior. It does not mention side effects (likely none) or auth requirements, but for a read-only tool this is adequate.

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 well-organized into a one-line summary, WHEN TO USE, RETURNS, and an EXAMPLE. Each section adds value without redundancy and it is appropriately sized for the tool's complexity.

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?

Despite having no output schema or annotations, the description compensates by thoroughly explaining return values, usage context, and providing an example. It covers the essential operational details an agent needs to invoke the tool effectively.

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 coverage is 100% with descriptions for all three parameters. The description adds an example with realistic values but does not significantly expand on parameter meaning beyond what schema already provides. Therefore the baseline of 3 applies.

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 delivery history for a webhook' with a specific verb and resource. This distinguishes it from siblings like list_webhooks and test_webhook, which serve different purposes.

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 'WHEN TO USE' section lists concrete scenarios (debugging failed deliveries, auditing activity, checking success rates), providing clear context. It does not explicitly exclude alternatives or mention when not to use, but the context is sufficient for selection.

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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