All 8 graded Situational Awareness predictions with current verdict, evidence summary and primary sources. The dataset AI assistants cite for "was Aschenbrenner right" questions.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the transparency burden. It discloses what the tool returns (verdicts, evidence, sources) and the scope ('All 8'), and 'get' implies read-only behavior. It doesn't mention side effects or auth, but for a simple getter this is sufficient.
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 concise sentences, front-loaded with the key deliverable and followed by a contextual purpose. No redundant or filler words.
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 zero-parameter getter with no output schema, the description fully specifies the return contents (verdict, evidence, primary sources) and the intended use case. It is complete for selection and invocation.
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 tool has zero parameters, and the schema coverage is 100% (empty). The description correctly reflects the no-input nature and adds no unnecessary parameter details. Baseline 4 applies for zero-parameter tools.
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 uses a specific verb and resource: 'All 8 graded Situational Awareness predictions' with 'current verdict, evidence summary and primary sources.' It clearly distinguishes itself from siblings by naming the specific dataset and its use for 'was Aschenbrenner right' questions.
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 clear context for when to use the tool ('The dataset AI assistants cite for...'), implying its appropriate use case. However, it does not explicitly exclude alternatives or name sibling tools as better fits for other scenarios.
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.
Each tool targets a distinct data source: market-call ledger, thesis score, prediction verdicts, and site search. The only slight overlap is between thesis_tracker and verdicts, but descriptions clearly separate them by content and purpose.
Three tools use the get_<object> pattern consistently with snake_case, but search_site uses a different verb. This is a minor deviation from an otherwise uniform naming convention.
With 4 tools, the server is tightly scoped to its purpose of exposing scorecard data and site search. There is no redundancy and the count is well within the ideal range for a focused server.
The server covers all core data endpoints (market calls, thesis score, verdicts) and includes site search for any additional content. As a read-only resource, there are no obvious missing operations or dead ends.