list_personal_rag_documents
List document metadata from one owner-scoped private RAG collection.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| offset | No | ||
| collection_id | Yes |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
List document metadata from one owner-scoped private RAG collection.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| offset | No | ||
| collection_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
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 readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful context about the scope ('owner-scoped private') and the fact that it lists metadata rather than full content. However, it does not disclose behaviors like pagination behavior (beyond implicit limit/offset in schema) or authentication requirements. It adds some value beyond annotations but not substantial new behavioral detail.
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, tightly worded sentence. It is front-loaded with the action ('List document metadata') and the scope. There is no filler or redundant phrasing.
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 is a straightforward list operation with an output schema present, so return values are already defined. The description adequately conveys the collection scope and privacy. The main gap is the lack of parameter elaboration, but given the schema's defaults and the simplicity of the operation, the description is mostly complete for an agent to invoke correctly.
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%, so the description must compensate for parameter explanations. The description implies that the collection_id selects the specific collection ('from one... RAG collection') but does not clarify the meaning of limit or offset. It provides minimal semantic value for the required parameter and none for the optional ones, leaving the agent to infer from schema defaults and types.
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 'List' and a specific resource 'document metadata' from a scoped resource 'one owner-scoped private RAG collection'. This clearly differentiates it from siblings like list_personal_rag_collections (which lists collections) and query_personal_rag (which likely queries content), even without naming them.
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 gives no explicit guidance on when to use this tool versus alternatives. It does not mention when not to use it or point to sibling tools. The context signals and sibling list imply distinctions, but the description itself lacks any routing information.
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.