Lore

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entity_map

List entities in the knowledge graph, optionally filtered by type, to obtain an overview or entity names for graph queries. Supports D3-compatible JSON output.

Instructions

List entities in the knowledge graph, optionally filtered by type. USE THIS WHEN: you want to see what entities Lore knows about, find entity names for graph queries, or get an overview of the knowledge graph. Set format='json' for D3-compatible graph visualization output.

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": "entity_map"
  }
}

Input Schema

Name	Required	Default
`limit`	No
`format`	No	text
`entity_type`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jun 24, 2026

TDQS

A4/5.0

Behavior2/5

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

No annotations provided, so description must fully disclose behavioral traits. It implies read-only by saying 'List', but does not explicitly state nondestructive behavior, nor address side effects, permissions, or rate limits.

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 plus targeted usage guidance. No unnecessary words, front-loaded with purpose. Efficient and clear.

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?

Given 3 optional params, no annotations, and existing output schema, description covers purpose, usage, and one output format. Lacks mention of pagination or data shape, but output schema fills return value gap.

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?

Schema has 0% description coverage, baseline 4. Description explains 'entity_type' (filtering) and 'format' (JSON for D3), but does not explain 'limit'. This partially compensates for missing schema descriptions.

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?

Description clearly states the tool lists entities in the knowledge graph with optional type filtering. It also provides usage contexts like 'find entity names for graph queries' and 'get an overview', differentiating it from siblings like graph_query.

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?

Explicit 'USE THIS WHEN' section lists scenarios, but does not mention when not to use or name alternatives. Still provides clear context for agent selection.

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