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get_changelog

Retrieve recent updates to the AI Dictionary, including new terms and modifications, to track changes in AI phenomenology terminology.

Instructions

Get recent changes to the AI Dictionary — new terms added and modifications.

Args: limit: Number of recent entries to show (default 20, max 50)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes a read operation ('Get'), implying it's likely non-destructive, but doesn't specify permissions, rate limits, or what 'recent changes' entails (e.g., time frame, format of changes). The description lacks details on output behavior, though an output schema exists, which mitigates this gap somewhat. It's minimal but not contradictory.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded. The first sentence states the purpose clearly, and the second provides essential parameter details in a structured 'Args:' format. There's no wasted text; every sentence adds value, making it efficient and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (1 optional parameter) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers the purpose and parameter semantics adequately. However, it lacks usage guidelines and detailed behavioral context (e.g., how 'recent' is defined), which could be improved for a tool in a server with many siblings.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds meaningful semantics beyond the input schema. The schema has 1 parameter ('limit') with 0% description coverage, providing only type and default. The description explains 'limit' as 'Number of recent entries to show (default 20, max 50)', clarifying its purpose, default, and constraint. This compensates well for the low schema coverage, though it doesn't cover other potential parameters (none exist here).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get recent changes to the AI Dictionary — new terms added and modifications.' It specifies the verb ('Get') and resource ('recent changes to the AI Dictionary'), making it understandable. However, it doesn't explicitly differentiate from sibling tools like 'search_dictionary' or 'lookup_term', which might also involve accessing dictionary data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'dictionary_stats' (which might provide summary data) or 'search_dictionary' (which might retrieve specific terms), leaving the agent to infer usage based on the name alone. There's no explicit when/when-not or alternative recommendations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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