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total-agent-memory

total-agent-memory

by vbcherepanov
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

memory_graph

Read-onlyIdempotent

Explore the unified knowledge graph by retrieving the neighborhood of a specified node, with options to set traversal depth and filter by node types such as rules, skills, concepts, and entities.

Instructions

Query the unified knowledge graph. Returns neighborhood of a node: connected rules, skills, memories, concepts, entities.

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": {
      "node": "<node>"
    },
    "name": "memory_graph"
  }
}

Input Schema

NameRequiredDescriptionDefault
nodeYesNode name or ID to explore
depthNoTraversal depth (1-3)
typesNoFilter by node types (rule, skill, concept, etc.)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description's use of 'Query' aligns with these hints and adds no contradictory claims. It does not elaborate on edge cases like missing nodes or empty results, but given the annotation coverage, this is sufficient.

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 sentence that directly conveys the core functionality and return types. It is concise, well-structured, and contains no superfluous information.

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 query tool with a relatively simple output, the description adequately informs about the nature of results (connected rules, skills, memories, concepts, entities). It does not specify the exact output format (e.g., list vs. graph), but given the tool's simplicity and lack of an output schema, this is acceptable and does not leave major 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 schema provides descriptions for all three parameters (node, depth, types) with coverage of 100%. The tool description adds no additional semantic detail about these parameters, so the baseline score of 3 is appropriate.

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 clearly states the tool's action ('Query the unified knowledge graph') and its primary output ('neighborhood of a node' with listed connected types). However, it does not distinguish itself from several closely related sibling tools (e.g., memory_graph_index, kg_at, or memory_recall), so its unique purpose is not fully explicit.

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

Usage Guidelines1/5

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 the many sibling tools that query or manipulate memory/knowledge graphs. There is no mention of appropriate contexts, prerequisites, or scenarios where this tool is preferred over alternatives.

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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