query_matrix
Search a knowledge matrix by keyword to retrieve relevant information from your connected data.
Instructions
Search Knowledge Matrix
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| keyword | Yes |
Search a knowledge matrix by keyword to retrieve relevant information from your connected data.
Search Knowledge Matrix
| Name | Required | Description | Default |
|---|---|---|---|
| keyword | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
v2.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral disclosure burden. It only says 'Search,' which implies a read operation, but it does not disclose whether the search is read-only, how results are returned, whether there are rate limits, or any other behavioral constraints.
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 three words with no padding, so it is concise and front-loaded. However, it is so terse that it leaves important semantic gaps, so it is not fully appropriately sized for an opaque tool with no annotations or output schema.
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?
For a simple tool, the description still provides insufficient context: no annotations, no output schema, and no explanation of what the Knowledge Matrix contains or how results are presented. An agent would have little basis for knowing whether to call query_matrix instead of siblings like get_pipeline or search_gmail.
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 the undocumented 'keyword' parameter. It does not explain matching rules, accepted formats, case sensitivity, or how the keyword is interpreted beyond the obvious implication that it is a search term.
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 verb ('Search') and a resource ('Knowledge Matrix'), so it is not a pure tautology; however, it never defines what the Knowledge Matrix is or what kind of results a query returns, leaving the tool's exact purpose vague. It also does not differentiate it from sibling tools like search_gmail or get_pipeline.
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?
There is no guidance about when to use this tool versus alternatives. The description does not mention exclusions, fallback tools, or any condition that should trigger selection of query_matrix over other lookup-oriented siblings.
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/PromptishOperations/ziggy-mcp'
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