Skip to main content
Glama
Sign In
Mnemograph

Mnemograph

by tm42
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

recall

Retrieve relevant context from your knowledge graph with automatic token management. Use depth levels for summaries, semantic search, or full exploration.

Instructions

PRIMARY RETRIEVAL TOOL. Get relevant context with automatic token management. Use focus=['EntityName'] to get full details on specific entities. shallow=quick summary, medium=semantic search+neighbors, deep=full exploration.

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

Input Schema

NameRequiredDescriptionDefault
depthYesshallow=summary (~500 tokens), medium=search+1-hop (~2000), deep=2-hop traverse (~5000)medium
focusNoEntity names to retrieve in full detail (replaces open_nodes). Use this to expand specific entities.
queryNoWhat you're looking for (used for medium/deep semantic search)
formatNoOutput format: prose (human-readable) or graph (JSON structure)prose
max_tokensNoOverride default token budget

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.1

TDQS

A4.2/5.0
Behavior4/5

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

Discloses token management, depth behaviors with token budgets, and focus usage. No annotations provided, so description carries full burden; it adequately describes the read-only retrieval behavior.

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?

Two sentences, no wasted words. Key information is front-loaded. Perfectly concise.

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?

Explains core retrieval purpose, token management, and depth semantics. Does not cover output format or max_tokens override, but those are in schema. Adequate for a retrieval tool without output schema.

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 coverage is 100% with detailed parameter descriptions. The description adds some additional context (e.g., automatic token management, focus usage) but largely overlaps with schema content.

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 is the PRIMARY RETRIEVAL TOOL for getting relevant context with automatic token management. Distinct from sibling write or analysis tools.

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?

Explicitly labeled as primary retrieval tool, guiding usage. Lacks explicit when-not-to-use or comparison with similar tools like find_similar, but context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools