search_bookmark
Search your saved bookmarks with natural language queries to retrieve relevant URLs and metadata from your collections.
Instructions
Search for bookmark in the vector store.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
Search your saved bookmarks with natural language queries to retrieve relevant URLs and metadata from your collections.
Search for bookmark in the vector store.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions 'vector store' which hints at semantic search, but it does not disclose whether this is a read-only operation, how results are ranked, any limitations, or what the return format looks like. This is minimal disclosure beyond the basic action.
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 short sentence that is immediately understandable and front-loaded. Every word earns its place without unnecessary fluff. It is concise but not so minimal that it becomes meaningless.
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 tool's simplicity (1 parameter, no output schema, no annotations), the description is too sparse. It does not explain the expected query format, results behavior, or when to choose this over the sibling tool. The overall context is insufficient for an agent to use the tool confidently.
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 description has 0% schema coverage and does not explain the 'query' parameter at all. While the parameter name 'query' is suggestive, the description does not clarify what kind of input is expected (e.g., natural language, exact text, keywords) or how it is processed. The description fails to compensate for the low schema coverage.
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 action ('Search') and the resource ('bookmark'), and the addition of 'in the vector store' adds useful context. It distinguishes from the sibling 'save_bookmark' by implying a read/retrieval operation versus a write operation.
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?
No explicit guidance is given on when to use this tool versus the sibling 'save_bookmark'. The description does not mention any prerequisites, scenarios, or exclusions. The usage is only implied by the tool's name and the contrast with 'save_bookmark'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/pree-dew/mcp-bookmark'
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