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memdb

memdb

by j0hanz
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

Recall Memories

recall
Read-only

Find memories connected by relationships and return a graph cluster for deeper context. Choose traversal depth from zero to three to control how far to follow knowledge links.

Instructions

Search for memories and traverse relationships to return a connected graph cluster. Use this for deeper context retrieval that follows knowledge graph connections.

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": {
      "query": "<query>"
    },
    "name": "recall"
  }
}

Input Schema

NameRequiredDescriptionDefault
depthNoHow many relationship hops to follow (0-3, default 1). 0 = search only, no graph traversal.
queryYesSearch query to find initial memories

Output Schema

NameRequiredDescriptionDefault
okYes
errorNo
resultNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.4.0

TDQS

A4.2/5.0
Behavior4/5

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

The annotation readOnlyHint=true already signals safety, and the description adds meaningful behavior: it searches memories, traverses relationships, and returns a connected graph cluster. This goes beyond the annotation without contradicting it. It doesn't detail edge cases like no matching memories or depth limits, but those are partially covered by the schema and output schema.

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 two sentences with no filler. The first sentence states the core mechanism and result, and the second sentence gives the intended use case. It is concise, front-loaded, and every sentence earns its place.

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?

The read-only annotation, fully described parameters, and presence of an output schema cover most operational concerns. The description still leaves some implicit routing choices—e.g., how it differs from a plain search_memories call—but overall it provides sufficient context for correct invocation.

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?

Schema description coverage is 100%, so both 'query' and 'depth' are already documented in the schema. The description adds minimal parameter-specific meaning beyond the general notion of graph traversal, which is acceptable given the schema's thoroughness.

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 action ('Search for memories and traverse relationships') and the resource/output ('connected graph cluster'). It also distinguishes itself from siblings like search_memories and get_memory by emphasizing knowledge-graph traversal, so an agent can tell which tool is intended.

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

Usage Guidelines4/5

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

The description gives explicit guidance on when to use the tool: 'Use this for deeper context retrieval that follows knowledge graph connections.' It does not explicitly name alternatives or state when not to use it, but the context is clear enough to route an agent appropriately.

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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