The Invest dataset: how the eight graded Situational Awareness predictions map onto 17 listed AI equities, how eight well-known investors are positioned per their public SEC 13F filings, and what copying them would have returned priced on the FILING DATE (not quarter end, which no real person could have traded). Educational only — never investment advice.
| Name | Required | Description | Default |
|---|---|---|---|
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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 disclosure burden, and it does a good job: it flags that returns are priced on the FILING DATE rather than quarter end to reflect tradability, and it adds the 'never investment advice' caveat. It does not describe response format or pagination, but those are less critical for a no-parameter getter.
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 a single dense sentence that front-loads the dataset identity and packs in relevant detail about scope, methodology, and the educational caveat. It is not flabby, though the long clause chain could be broken up for easier scanning.
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 no-parameter tool with no output schema, the description conveys the dataset's content and key pricing nuance, but it never states what the tool actually returns or in what shape. The phrase 'what copying them would have returned' is about hypothetical investment returns, not the tool's response, leaving a small ambiguity about output.
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 has no parameters and schema coverage is effectively complete at 100%, so parameter explanation is not needed. The baseline of 4 applies, and the description's dataset context adds value without needing to document params.
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 identifies the Invest dataset and enumerates its contents: eight graded predictions mapped to 17 AI equities, eight investors' SEC 13F positions, and hypothetical filing-date returns. It distinguishes itself from siblings by focusing on the Invest domain, but it never states an explicit verb such as 'returns' or 'fetches', so the action is implied rather than stated.
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 explains what the dataset contains and adds an 'educational only' caveat, but it does not specify when to use this tool relative to siblings like get_thesis_tracker or get_verdicts, nor any exclusions. An agent must infer applicability solely from the name and content.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.