get_register
The ratified register: language constructs with observed corpus adoption plus project protocols whose adoption status is not_applicable.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
The ratified register: language constructs with observed corpus adoption plus project protocols whose adoption status is not_applicable.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must carry the full transparency burden. It states the content of the register but does not disclose behavioral traits such as read-only-ness (though 'get' implies it), authorization needs, rate limits, side effects, or response format. Since it is a simple retrieval with no parameters, the lack of buthlavioral detail is less severe, but the description adds no transparency beyond the content. It does not contradict any annotations because none exist.
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 sentence of moderate length, no waste. However, the structure is a noun phrase with a colon, which is slightly less readable than a verb-first declarative sentence. It is concise but could be front-loaded more clearly. Still, every word serves a purpose.
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 description explains what the register contains but does not describe the output format (e.g., is it a list, an object, a JSON structure?) or provide any context about the 'ratified register' concept. With no output schema, the burden is on the description to inform about return values. It fails to do so. The domain is specialized and the description omits necessary foundational explanation for an AI agent to understand the result.
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 zero parameters and schema description coverage is 100% trivially. According to instructions, 0 parameters baseline is 4. The description does not need to add parameter meaning since there are none. It correctly omits param reference.
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 defines the resource as 'the ratified register' and specifies its content (language constructs with adoption, plus project protocols with not_applicable status). The action 'get' is implied by the tool name and the noun-phrase description is interpretable as a retrieval operation. However, it lacks an explicit verb like 'retrieve' or 'list', and the jargon may confuse an agent unfamiliar with the domain. Still, it distinguishes from siblings like get_register_pack and get_protocols by defining the scope of the register.
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 does not provide any guidance on when to use this tool versus alternatives. Among siblings (get_register_pack, get_protocols, get_changelog, etc.), there is no explanation of when get_register is appropriate or when to prefer another tool. No context signals about prerequisites, filters, or derived usage are given. This leaves the agent to infer from the name alone.
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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