Create an owner-isolated private RAG collection within the configured quota.
| Name | Required | Description | Default |
|---|---|---|---|
| collection | Yes |
| 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?
Annotations already declare idempotentHint=true and destructiveHint=false, so the safety profile is covered. The description adds a quota constraint and owner-isolation, which provides behavioral context beyond the annotations. It does not contradict the annotations, and the added details are useful for understanding side effects and limitations.
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, terse sentence that front-loads the action and key constraints. There is no filler or repetition, and every phrase adds meaningful context.
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 is adequate for a simple create operation, especially with an output schema present (so return values need no explanation). However, it omits parameter specifics and does not mention potential failures (e.g., quota exceeded behavior), which could be important for correct invocation. It covers the core purpose but lacks some situational detail.
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?
Schema description coverage is 0%, meaning the description provides no explanation of the 'name' or 'description' fields inside the 'collection' parameter. The description mentions no parameters at all, forcing the agent to rely solely on the schema. Given the low coverage, the description should have compensated, but it does not.
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 states a specific verb ('Create') and resource ('personal RAG collection'), and adds distinctive modifiers ('owner-isolated private', 'within the configured quota') that clarify scope. It is not a tautology and clearly distinguishes this creation action from listing, deleting, or querying tools, though it does not explicitly compare to sibling create tools like create_problem.
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 no guidance on when to use this tool versus alternatives, no mention of exclusions, and no indication of prerequisites (e.g., quota availability). While the purpose is self-evident, the description fails to explicitly state when an agent should choose this over related collection-management tools.
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 resource and action: problem lifecycle, knowledge reading/search/context, candidate submission/review, and experience verification. Even similar tools like get_knowledge, search_knowledge, and retrieve_context are clearly differentiated by their descriptions.
All tool names follow a consistent snake_case verb_noun pattern with no mixed conventions or vague verbs. Names accurately reflect their actions and objects, making the set predictable and easy to navigate.
At 16 tools, the count is slightly above the typical 3-15 range but still reasonable given the multi-faceted domain (problems, knowledge, candidates, experiences). Each tool appears to have a specific purpose, though a few could potentially be consolidated.
Core workflows are covered: create/claim/manage problems, submit/review candidates, publish/retrieve knowledge, and verify experiences. Minor gaps exist such as no explicit close/cancel operation for problems, but agents can work around these with existing tools.