Diagnose vector memory by retrieving total count, per-category breakdown, embedding dimension, and model path. Ideal for checking state before or after bulk operations.
Vector memory store stats: total count, per-category breakdown, embedding dim, model path. For diagnostics and before/after bulk operations. Lazy-loads ONNX+sqlite-vec on first call.
| Name | Required | Description | Default |
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Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses a key behavioral trait: 'Lazy-loads ONNX+sqlite-vec on first call,' which implies a potential performance cost and side effect. It also implies a read-only operation via 'stats,' though it doesn't explicitly state no mutations. This is good coverage for a simple stats tool.
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 concise: two sentences, front-loaded with the core content (what stats are provided), then usage context, then a behavioral note. Every sentence earns its place with zero waste.
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 (no parameters, no output schema, no annotations), the description covers the return values by listing the stats fields, the usage scenario, and the lazy-load behavior. It does not explain error handling or exact output formatting, but that is not critical for a stats tool. It is sufficiently complete for an agent to call it 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?
The tool has zero parameters, so the baseline is 4. The description correctly omits parameter details since there are none, and it does not need to add anything beyond the schema, which is already empty.
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 what the tool does: provides vector memory store stats including total count, per-category breakdown, embedding dimension, and model path. It uses a specific resource ('vector memory store') and implies a retrieval action ('stats'), making it distinguishable from sibling memory tools like memory_search or memory_add.
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 explicit usage context: 'For diagnostics and before/after bulk operations.' This tells the agent when to invoke it, though it does not explicitly name alternatives or state when not to use it. However, the context is clear and sufficient for a diagnostics tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.