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AgentWong

Knowledge Graph Memory Server

by AgentWong
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Read Graph

read_graph

Retrieve the complete knowledge graph to access stored user information across conversations for persistent memory.

Instructions

Read the entire knowledge graph

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
entitiesYes
relationsYes

Implementation Reference

  • optimized_memory_mcp_server/knowledge_graph_manager.py:288-292 (handler)
    The implementation of the read_graph tool in the KnowledgeGraphManager class. It retrieves the knowledge graph by calling the _check_cache helper method, which handles loading from a file and caching.
    async def read_graph(self) -> KnowledgeGraph:
    """
    Read the entire knowledge graph using the cached version if available.
    """
    return await self._check_cache()

Tool Definition Quality

B3/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It only states the action without disclosing behavioral traits such as performance implications (e.g., large data returns, rate limits), authentication needs, or what 'entire' means operationally (e.g., pagination, format). This leaves critical gaps for a read operation.

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, efficient sentence with zero waste. It's front-loaded and appropriately sized for the tool's simplicity, making it easy to parse without unnecessary elaboration.

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?

Given the tool's low complexity (0 params, output schema exists), the description is minimally adequate but incomplete. It lacks context on usage versus siblings and behavioral details, which are needed despite the output schema covering return values. This results in a baseline viable but with clear gaps.

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

Parameters4/5

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate, but it implies no inputs are required, aligning with the schema. Baseline 4 is given as it meets expectations for a zero-param tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the action ('Read') and resource ('the entire knowledge graph'), which provides basic purpose. However, it lacks specificity about what 'read' entails (e.g., retrieving all nodes/relations vs. metadata) and doesn't distinguish from siblings like 'search_nodes' or 'open_nodes', making it vague rather than clear.

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?

No guidance is provided on when to use this tool versus alternatives. It doesn't mention scenarios like retrieving full graph data versus filtered searches with 'search_nodes' or accessing specific nodes with 'open_nodes', leaving the agent without context for tool selection.

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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