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Mnemosyne MCP

Mnemosyne MCP

by zhadyz
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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get_entity_embedding

Retrieve the vector representation of a specific entity from the knowledge graph memory, enabling semantic search, similarity comparisons, and clustering operations.

Instructions

Get the vector embedding for a specific entity from your Memento MCP knowledge graph memory

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "entity_name": "<entity_name>"
    },
    "name": "get_entity_embedding"
  }
}

Input Schema

NameRequiredDescriptionDefault
entity_nameYesThe name of the entity to get the embedding for

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.2

TDQS

A3.5/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states only that the tool 'gets' an embedding, implying a read operation, but does not disclose return format, error behavior, or any side effects. This is minimal beyond the basic action.

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 a single, concise sentence that front-loads the key action and resource. Every word contributes to clarity without redundancy.

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?

For a simple one-parameter getter, the description provides adequate purpose but lacks completeness because no output schema exists, so return format is not described, and no usage guidelines or error behavior are mentioned. It is minimally viable but with clear gaps.

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

Parameters3/5

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

The input schema fully describes the sole parameter (entity_name) at 100% coverage. The description adds no additional meaning beyond the schema, so baseline score of 3 applies.

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 uses a specific verb ('Get') and resource ('the vector embedding for a specific entity') clearly distinguishing it from siblings like read_graph or get_entity_history. It precisely defines what the tool does.

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 description implies usage (retrieve an embedding when needed) but does not explicitly mention when to use this tool over alternatives like semantic_search. No exclusions or alternatives are named, leaving usage guidance implied rather than explicit.

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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