delete_personal_rag_collection
Delete one owned private collection and all its live documents and FTS entries.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| collection_id | Yes |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Delete one owned private collection and all its live documents and FTS entries.
| Name | Required | Description | Default |
|---|---|---|---|
| collection_id | 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 destructiveHint=true and idempotentHint=true; the description adds behavioral context by explicitly stating the cascade: it deletes 'all its live documents and FTS entries.' This goes beyond the annotation's simple destructive flag and clarifies the full impact of the operation. No contradiction with annotations.
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?
A single sentence contains all necessary information: the operation, the object, and the cascading effects. It is front-loaded with the verb and resource, with no redundant words. Excellent conciseness.
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 the simple nature of the tool (one parameter, no nested objects) and the presence of an output schema, the description is complete. It clearly states what the tool does and its side effects. No additional information is needed for correct invocation.
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?
There is only one parameter, collection_id, and the schema has 0% description coverage. The description does not explicitly explain the parameter, but the phrase 'one owned private collection' implies that collection_id identifies the target collection. The description adds little beyond the parameter name, but for a single ID field it is minimally sufficient.
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 verb 'delete' and the resource 'personal RAG collection', and further specifies scope as 'one owned private collection' with cascading effects on 'live documents and FTS entries.' This distinguishes it from the sibling delete_personal_rag_document, which operates on individual documents.
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 implicitly indicates when to use this tool: when you need to delete an entire collection. It does not explicitly mention alternatives or exclusions (e.g., 'for deleting a single document, use delete_personal_rag_document'), but the name and scope make the usage context clear. The absence of explicit routing to alternatives prevents a perfect score.
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.