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shaneholloman

mcp-knowledge-graph

aim_memory_get

Retrieve specific memories and their relations using exact entity names. Use for precise data retrieval when you know the exact names.

Instructions

Retrieve specific memories by exact name. Use this when you know exactly what you're looking for.

VS aim_memory_search: Use aim_memory_get for exact name lookup. Use aim_memory_search for fuzzy matching or when you don't know exact names.

RETURNS: Requested entities and relations between them. Non-existent names are silently ignored.

FORMAT OPTIONS:

  • "json" (default): Structured JSON for programmatic use

  • "pretty": Human-readable text format

EXAMPLES:

  • aim_memory_get({names: ["John", "TechConf2024"]}) - JSON format

  • aim_memory_get({names: ["Shane"], format: "pretty"}) - Human-readable

  • aim_memory_get({context: "work", names: ["Q4_Project"], format: "pretty"})

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextNoOptional memory context. Retrieves entities from the specified context's knowledge graph or master database if not specified.
locationNoOptional storage location override. 'project' for .aim directory, 'global' for configured directory.
namesYesAn array of entity names to retrieve
formatNoOutput format. 'json' (default) for structured data, 'pretty' for human-readable text.
Behavior4/5

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

No annotations provided, so description carries burden. Discloses silent ignoring of missing names and format options. For a read-only tool, this is sufficient, though could mention safety or auth.

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?

Well-structured with clear sections, examples, and sibling differentiation. No wasted words.

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

Completeness5/5

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

Covers all parameters, format, examples, sibling comparison, and return values (entities and relations). Complete for a retrieval tool with no output schema.

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

Parameters4/5

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

Schema coverage is 100%. Description adds value with examples and explanation of format options and context parameter, going beyond 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?

Clearly states it retrieves specific memories by exact name. Distinguishes from sibling aim_memory_search by specifying exact vs fuzzy matching.

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

Usage Guidelines5/5

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

Explicitly says when to use (exact name known) and when not (use aim_memory_search for fuzzy). Also notes non-existent names are silently ignored.

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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