Get unanswered engineering questions from the knowledge base.
These represent the frontier of what needs to be figured out. Each question is linked to the entry that raised it.
Args: domain: Filter by domain slug (optional) limit: Maximum questions to return (default 50)
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| domain | No |
| 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?
With no annotations, the description carries the full burden of behavioral disclosure. It explains that questions are unanswered and linked to the entry that raised them, providing some context about the return structure. However, it does not mention side effects, auth requirements, or rate limits, though the operation appears read-only based on the 'Get' verb.
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 concise and well-structured: a clear summary line, a motivational note, a detail about linkage, and an Args list. Every sentence adds value; there is no redundant or filler content.
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?
Given that an output schema exists, the description does not need to detail return values. It covers the tool's purpose, parameter semantics, and behavioral context well. A slight gap is that it does not explicitly clarify what counts as an 'unanswered engineering question,' but this is largely evident from the context.
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 schema has no property descriptions (0% coverage), so the description's Args section compensates well. It explains that 'domain' filters by domain slug and 'limit' sets the maximum number of questions with a default of 50. This is sufficient for an agent to understand parameter semantics.
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 states the tool fetches unanswered engineering questions from the knowledge base, with a specific verb ('Get') and resource ('unanswered engineering questions'). This distinguishes it from siblings like search_knowledge or read_node, which serve different purposes.
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 (to retrieve open/unanswered questions) and adds useful context that these represent the frontier of what needs to be figured out. However, it does not explicitly mention alternatives or when not to use it, so it lacks a strong exclusionary guideline.
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 has a distinct purpose: retrieving entries, cross-references, parameters, stats, open questions, domains, search, registration, and submission. No two tools appear to perform the same function, and the descriptions clarify when to use each.
All tool names follow a consistent verb_noun pattern with snake_case: get_cross_references, list_domains, read_node, submit_proposal, etc. The verb prefixes (get, list, read, search, register, submit) align with their operations, making the pattern predictable.
With 9 tools, the set is well-scoped for a knowledge base system. Each tool covers a core functionality (retrieval, search, stats, submission, registration) without redundancy or bloat, striking the right balance for the stated purpose.
The tool surface covers the main workflows: reading, searching, cross-referencing, statistics, and submission. Minor gaps exist, such as no direct listing of all entries in a domain and no update/delete operations for submissions, but these are workable around and do not severely hinder typical usage.