Retrieve AI company intelligence by combining incidents, job postings, and funding data for a given entity. Use it to analyze a company's risk and growth signals in one call.
Cross-signal intelligence: incidents + jobs + funding per AI company.
| Name | Required | Description | Default |
|---|---|---|---|
| entity | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, and it only implies that the response bundles incidents, jobs, and funding data. It says nothing about permissions, caching/staleness, coverage limits, or what an empty entity argument does, so the behavioral profile is largely undisclosed.
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?
A single short sentence with no filler, and the aggregated signal list is front-loaded. It is efficiently structured, though the fragment style leans on the reader to fill in the verb.
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?
An output schema exists, so return values need not be explained, but the description still leaves the one input parameter undefined and offers no usage context. For a simple single-argument aggregation tool that is marginally adequate rather than complete.
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 0% and the single 'entity' parameter is undocumented. The phrase 'per AI company' hints that entity is a company name or identifier, but the description does not confirm the expected format, and the default of "" (which presumably means all companies) is never explained.
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 phrase 'Cross-signal intelligence: incidents + jobs + funding per AI company' names the resource (AI company) and the aggregated signals, which distinguishes it from single-signal siblings like get_ai_incidents and get_ai_jobs. It is a noun phrase rather than a verb, but the scope is specific enough for an agent to recognize it as a consolidated per-company view.
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?
There is no explicit when-to-use guidance, no prerequisites, and no named alternatives. The 'cross-signal' framing weakly implies it should be preferred over the individual incident/job tools for a consolidated view, but that inference is left entirely to the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.