Get machine-readable pricing for all Trillboards products. Returns graduated usage-based pricing, free tier thresholds, and committed-use discount tiers. No authentication required — use this to evaluate costs before integrating.
| Name | Required | Description | Default |
|---|---|---|---|
| product | No | Optional: filter to a specific product (data_api, proof_of_play, attribution, data_marketplace, partner_platform, programmatic, fein_edge_ai) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the no-auth requirement and describes the return format and content (graduated pricing, free tier, discount tiers). This is meaningful behavioral context. It doesn't explicitly state that the operation is read-only, but the 'get' verb implies it, and no side effects are suggested.
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 the core purpose, and every sentence adds value. The second sentence packs return details, auth requirement, and use case without fluff.
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?
For a simple tool with one optional parameter and no output schema, the description covers purpose, return contents, auth, and intended use. It could explicitly state the behavior when 'product' is omitted (e.g., returns all products), but the phrase 'all Trillboards products' implies this. Overall, the description is sufficient for an agent to select and invoke the tool.
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?
Schema description coverage is 100%: the single optional 'product' parameter is fully described with an enum of values. The tool description doesn't add any extra parameter semantics beyond the schema, so the baseline of 3 is appropriate.
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 the tool's purpose with a specific verb and resource: 'Get machine-readable pricing for all Trillboards products.' It also enumerates return contents (graduated usage-based pricing, free tier thresholds, committed-use discount tiers), which differentiates it from sibling tools like get_products or get_billing_status.
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?
The description provides a clear use case: 'use this to evaluate costs before integrating.' It also mentions the authentication requirement (none). However, it doesn't explicitly name alternative tools or state when not to use it, so it falls 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.
Add one secure layer between your agents and this server.
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.
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.
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.
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.