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asd-noor

Memory Engine MCP Server

by asd-noor

query_memory

Search stored memories using natural language or keyword queries to retrieve matching results with similarity scores.

Instructions

Query memories using semantic and keyword search.

Args:
    query: Natural language search string
    top_k: Number of results to return (default: 3)

Returns:
    A list of matching memories with similarity scores

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
top_kNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions semantic and keyword search and returns a list of matching memories with similarity scores, which is useful context. However, it does not explicitly state that it is a read-only operation or disclose any limitations, such as behavior with empty queries or potential side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured, with a clear one-sentence purpose followed by parameter definitions and return value explanation. Every sentence contributes meaningful information without unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter read tool, the description is fairly complete. It covers the purpose, parameter semantics, and return format. The existence of an output schema means the return value details are adequately supplemented, though it lacks explicit usage guidance, which is a minor gap given the tool's simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description fully compensates by defining 'query' as a natural language search string and explaining 'top_k' as the number of results to return with a default value of 3. This adds significant meaning beyond the bare type information in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool queries memories using semantic and keyword search. The verb 'Query' plus the resource 'memories' and the specific search methods distinguish it from sibling tools like save_memory, delete_memory, and update_memory.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The usage is implied by the tool's name and description, but there is no explicit guidance on when to use this tool versus alternatives. It does not mention scenarios like 'when you need to retrieve stored information' or exclude contexts where other memory tools are more appropriate.

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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