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Trillboards DOOH Advertising

describe_endpoint

Describe a single API operation including its parameters, response shape, and error codes.

WHEN TO USE:

  • Inspecting an endpoint's full contract before calling it.

  • Discovering which error codes an endpoint can return and how to recover.

RETURNS:

  • operation: Full discovery record for the endpoint.

  • parameters: Raw OpenAPI parameter definitions.

  • request_body: Body schema (when applicable).

  • responses: Map of status code → description/schema.

  • linked_error_codes: Error catalog entries the endpoint can emit.

EXAMPLE: Agent: "How do I call the screen audience endpoint?" describe_endpoint({ path: "/v1/data/screens/{screenId}/audience", method: "GET" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYesThe operation path (OpenAPI template form, e.g. "/v1/data/screens/{screenId}/audience").
methodYesHTTP method (case-insensitive).

TDQS

A4.1/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It reveals the return structure and the example implies a read-only operation, but it does not explicitly state that the tool has no side effects, nor does it mention any behavior around error handling for the tool call itself (e.g., invalid path). The RETURNS section provides useful output context, but deeper behavioral disclosure is missing.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with WHEN TO USE, RETURNS, and EXAMPLE sections. It is longer than the minimum, but each section contributes meaningful information. The example is particularly helpful without being redundant. It is not excessive, so a 4 is appropriate.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple (2 params), has no output schema, and no annotations. The description compensates well by listing the return fields, providing usage guidance, and including an illustrative example. It covers the essential aspects needed for an agent to select and invoke the tool correctly. A small gap is the lack of explicit statement about response status codes for the tool itself, but overall it is fairly complete.

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?

Schema coverage is 100% and both parameters are described in the schema. The description adds value with a concrete example showing a real path with template syntax and method 'GET', which clarifies how to format the path. This goes beyond the schema's dry descriptions, earning a 4 rather than the baseline 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 first sentence clearly states 'Describe a single API operation including its parameters, response shape, and error codes.' This uses a specific verb ('describe') and resource ('API operation'), and the singular scope distinguishes it from sibling tools like list_endpoints. No ambiguity.

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 explicitly lists two use cases: inspecting an endpoint's full contract before calling it, and discovering error codes and recovery. This gives clear context for when to use the tool. However, it does not explicitly mention alternatives or when not to use it, so it stops short of a 5.

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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