Analyse your stored knowledge and surface recurring themes: principles you repeat without naming, patterns in how you think. Especially powerful after adding several voice notes.
| Name | Required | Description | Default |
|---|---|---|---|
| focus | No | Focus analysis on a specific type, e.g. "principle" or "voice-note" |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description confirms it is a non-destructive analysis tool. It adds context about what it does (surfacing patterns) but does not describe any side effects or limitations beyond the 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?
The description is brief, two sentences, and front-loaded with the purpose. No redundant or extraneous information. Every sentence serves a purpose.
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 simple with one optional parameter and good annotations. However, there is no output schema, and the description does not explain the return format or how results are presented. For a pattern-finding tool, the output structure is important to understand.
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 input schema has only one parameter, 'focus', with a clear description and an example ('principle', 'voice-note'). Schema coverage is 100%, so the description adds value by providing a concrete usage example beyond the schema's basic description.
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 tool's action: analyzing stored knowledge to surface recurring themes. It uses specific verbs ('analyse', 'surface') and identifies the resource ('stored knowledge'). It distinguishes itself from sibling tools by focusing on pattern discovery rather than document retrieval or editing.
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 suggests using the tool after adding several voice notes, providing a usage hint. However, it does not specify when not to use it or compare it to similar sibling tools like search_for_creation or get_by_type. No exclusions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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