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?
With no annotations, the description carries full behavioral disclosure. It states the exact count (8), the dynamic nature ('current verdict'), and the content structure (evidence summary and primary sources), giving the agent a clear expectation of what the call returns. It does not mention limitations or side effects, but this is a simple read-only 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 two concise sentences with no filler. The first sentence front-loads the core function and content, and the second adds valuable contextual usage. Every word earns its place.
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?
The tool has low complexity (0 params, no annotations, no output schema), and the description is fully sufficient. It explains both the returned data and the intended use case, leaving no ambiguity about when to invoke this tool.
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, so the baseline is 4. The description does not need to add parameter semantics, and the schema is trivially complete. No additional parameter information is required.
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 tool as retrieving all 8 graded Situational Awareness predictions with verdicts, evidence summaries, and primary sources. It also states the specific use case for 'was Aschenbrenner right' questions, distinguishing it from sibling tools like get_sunwatch_track_record and get_thesis_tracker.
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 an explicit use context: it is the dataset AI assistants cite for 'was Aschenbrenner right' questions. While it does not name alternatives or state when-not-to-use, the guidance is clear enough for an agent to select this tool for that specific query type.
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 aspect: market-call ledger, thesis score, prediction verdicts, and site search. No overlap in purpose or output.
Three of four tools follow the get_ prefix pattern, but search_site deviates by using a bare verb_noun form. Still mostly consistent and predictable.
Four tools is well-scoped for an informational read-only server covering a niche site. Each tool earns its place without redundancy.
Covers the core data sets (track record, thesis, verdicts) and site search. Minor gap: no direct tool for other content types (e.g., articles), but search mitigates this.