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yliuai

Spomory

search_memory

Retrieve relevant context from your personal AI memory by querying stored memories, and get assembled natural-language information to support your current request.

Instructions

Retrieve and assemble a natural-language context relevant to query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
top_kNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.6/5.0
Behavior1/5

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

The description says 'Retrieve and assemble a natural-language context,' which describes a read-only operation, yet annotations declare readOnlyHint: false. This direct contradiction makes the behavior unclear. No additional behavioral context such as side effects, permissions, or output assembly process is disclosed.

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

Conciseness4/5

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

The description is a single, efficient sentence with no filler and key information front-loaded. It is concise, though it omits useful detail about parameters and usage that would make it more helpful.

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

Completeness3/5

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

The output schema exists, so return value structure is covered. However, the description does not explain the meaning of `top_k`, does not offer usage guidance, and is contradicted by the annotation. For a simple two-parameter search tool, the gaps are notable but not fatal.

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

Parameters2/5

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

The description only references `query` and does not mention `top_k` at all. With schema description coverage at 0%, the schema offers no parameter definitions, so the optional parameter's purpose, behavior, and default are left unexplained.

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

Purpose4/5

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

The description uses specific verbs ('Retrieve and assemble') and identifies the resource (natural-language context relevant to the query). It is distinguishable from siblings such as add_memory, forget_memory, and export_memory, though it does not explicitly name memory as the source or compare itself to alternatives.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus export_memory, get_graph, or other sibling tools. There are no exclusions, prerequisites, or contextual signals to help an agent decide between alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.