Revenue attribution event programs. Return pipeline-impact data for an event id from the live dashboard if recorded, otherwise return the attribution model and how it is tracked, with the dashboard link. No fabricated figures.
| Name | Required | Description | Default |
|---|---|---|---|
| event_id | Yes | Event id |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description adds meaningful behavioral context: it discloses reliance on a live dashboard and explicitly promises 'No fabricated figures.' However, it does not mention permissions, rate limits, or error handling, leaving gaps for a fully transparent behavioral profile.
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 extremely concise, with three short sentences covering purpose, fallback behavior, and integrity. Every sentence earns its place, and the key details are front-loaded.
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 one-parameter tool with no output schema, the description adequately covers the main return cases (recorded vs. not recorded) and includes a dashboard link. It lacks error-handling details or edge-case behavior, but it is sufficiently complete for a straightforward read operation.
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?
The input schema already documents event_id as 'Event id' with 100% coverage. The description adds no additional semantic meaning beyond referencing the id, so the baseline score of 3 applies.
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 returns pipeline-impact data for a specific event id, with a fallback to the attribution model and tracking details. This specific verb+resource clearly distinguishes it from sibling tools like get_product or get_categories, which serve different domains.
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 implies usage when revenue attribution for an event is needed, but it does not explicitly state when to prefer this tool over siblings or mention exclusions. The 'if recorded... otherwise' clause is about data availability, not tool selection.
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.
get_quote and get_live_quote both return wholesale quotes for product SKUs and quantities, differing only in context (enterprise events vs. general). get_product_catalog and search_products both search the catalog with filters, making the boundary between them unclear. These overlaps can lead to agent misselection.
All tools use snake_case with a verb_noun pattern (get_, generate_, search_, track_). There are minor deviations where similar actions use different verbs (e.g., get_product_catalog vs. search_products, get_quote vs. get_live_quote), but the overall pattern is consistent and predictable.
With 10 tools, the server is well-scoped for its purpose. Each tool covers a distinct aspect of the procurement and event management domain, and the count falls within the ideal 3-15 range, earning its place without feeling bloated.
The server covers product discovery, catalog browsing, quotes, event program generation, and shipment tracking. However, there is no tool for placing an order or managing event records, and the link between quotes and orders is unclear, leaving notable gaps in the end-to-end procurement workflow.