Everything one company does on the conference circuit: events sponsored (with tier) and employees who speak. Point it at a competitor or a target account.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | company slug (e.g. datadog) or domain (datadoghq.com) |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It usefully reveals output content (sponsored events with tier, speaking employees), but does not state whether data is read-only, whether it includes current/historical conferences, or if any authentication is required. The tool name 'get' implies read-only, but the description does not explicitly confirm it or discuss edge cases.
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?
Two short sentences with zero wasted words. The most important output details are front-loaded in the first sentence, and the use case is delivered in the second.
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 read tool with no output schema, the description is largely complete: it explains what the result contains and when to use it. It could mention handling of unknown slugs or whether results are time-scoped, but given the low complexity, this is not a significant gap.
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 schema covers the single parameter 'slug' with examples for both slug and domain formats (100% coverage), so the description adds no additional parameter meaning. Baseline 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?
Description states a specific resource ('a company') and precisely enumerates what is returned: events sponsored with tier and employees who speak. This distinguishes get_company from sibling search tools and get_event by making clear it aggregates all activity for a single company rather than listing individual events or speakers.
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 second sentence gives an explicit use case: 'Point it at a competitor or a target account.' This tells an agent when the tool is appropriate and implies it is for company-level lookups rather than general search. It does not name alternative sibling tools or state exclusions, 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.
