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total-agent-memory

total-agent-memory

by vbcherepanov
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

memory_graph_stats

Read-onlyIdempotent

Retrieve knowledge graph statistics including node and edge counts, community structure, top concepts, and health metrics.

Instructions

Knowledge graph statistics: nodes, edges, communities, top concepts, health metrics.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "memory_graph_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

The annotations already indicate read-only, non-destructive behavior, and the description does not contradict them. The mention of 'health metrics' hints at additional informational output, but no further behavioral traits are disclosed beyond what the annotations cover.

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 concise and directly lists the output categories. Every word contributes meaning, and the structure is clear without redundancy.

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?

Given that no output schema is provided, the description still lists the key statistics returned. This is sufficient for a user to understand the tool's purpose and typical output, making the description complete in context.

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 tool has no parameters, so the schema coverage is complete. The description adds no parameter-specific semantics, but none are needed; the baseline 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 clearly states the tool provides knowledge graph statistics, listing specific elements: nodes, edges, communities, top concepts, and health metrics. This is distinct from sibling tools like memory_graph (which likely fetches the graph structure) and memory_concepts (which focuses on concepts).

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 does not explicitly state when to use this tool versus alternatives. It merely describes what it returns, without noting situations where a user should prefer this over memory_graph or memory_concepts, or when it might be inappropriate.

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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