get_questionThe complete published guidance for one questionnaire question: what it is really asking, which of your documents answers it, what evidence to attach, a model answer with the specifics left blank, the ways it usually goes wrong, and the standards that ask it with their verified control references.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The slug from a search_questions result, for example "encryption-at-rest". |
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?
With no annotations provided, the description carries the behavioral disclosure burden. It clearly indicates a read operation via the title 'Read' and the tool name 'get', and it richly describes what the response includes: the real question intent, relevant documents, evidence requirements, model answer, common pitfalls, and associated standards. It does not mention auth or edge cases, but for a simple read-only lookup the disclosure is strong.
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 one long but well-structured sentence with a colon-led list of contents. It front-loads the core purpose and every listed item adds information about the returned guidance, so there is little waste. The length is justified but slightly dense for quick 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?
The tool has a single parameter, no output schema, and no annotations, so the description is the primary source of completeness. It thoroughly describes the return value's contents, including model answers, common mistakes, and standard references, which is enough for an agent to know what it will get and how to use 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 100% description coverage for the single 'slug' parameter, so the schema already explains that the value comes from a search_questions result. The tool description adds no parameter-specific detail beyond saying it is 'for one questionnaire question,' which is adequate but does not improve on the schema.
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 the complete published guidance for a single questionnaire question, then enumerates exactly what that guidance includes. This distinguishes it from search_questions, which would be used to discover questions rather than read full guidance.
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 implies this is the follow-up tool to use after finding a question slug via search_questions, and the schema reinforces that by requiring a slug from search_questions. However, the description itself never explicitly states when to use this versus a sibling tool or when not to use it.
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 purpose: vendor posture, question detail, corpus statistics, standards list, and search. Even search_questions vs get_question are clearly separated by search vs complete lookup.
All tool names use a consistent verb_noun pattern with clear retrieval verbs: get_, list_, and search_. The names are lowercase snake_case and predictable.
Five tools is well-scoped for a focused questionnaire guidance corpus. Each tool covers a necessary access pattern without redundancy.
The tool surface covers searching, retrieving detailed guidance, listing standards, analyzing corpus statistics, and viewing the vendor's own posture. A direct browse-all-questions capability is not present but search covers discovery adequately.