grafeo-mcp

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graph_info

Retrieve counts, labels, edge types, and schema of your graph database to understand its state before writing queries or after mutations.

Instructions

Get an overview of the graph database: counts, labels, edge types, and schema.

Use this tool when: you need to understand what data is in the graph before writing queries, or to verify the database state after mutations. Do NOT use this for: retrieving specific node/edge data (use get_node, search_nodes_by_label, or execute_gql).

Returns: JSON with database info (mode, node_count, edge_count, persistence), schema (labels with counts, edge_types with counts, property_keys), and detailed statistics.

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": "graph_info"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0May 29, 2026

TDQS

A4.6/5.0

Behavior4/5

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

No annotations provided, so description carries full burden. It clearly states the tool returns database info, schema, and statistics, implying a read-only operation. Could be more explicit about idempotency, but sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise, front-loaded with purpose, and uses bullet points for the return section. A minor redundancy (the 'Returns:' line could be integrated) but still efficient.

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 zero parameters and an output schema, the description covers use cases, non-use cases, and return details comprehensively. No gaps for the intended purpose.

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?

No parameters; trivially 100% schema coverage. Description adds value by detailing the return structure (JSON with mode, node_count, edge_count, persistence, labels, edge_types, property_keys, statistics), helping the agent understand output beyond schema.

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?

Explicitly states 'Get an overview of the graph database: counts, labels, edge types, and schema,' and distinguishes from siblings by listing alternatives (get_node, search_nodes_by_label, execute_gql) for what not to use.

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

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear when-to-use: 'when you need to understand what data is in the graph before writing queries, or to verify the database state after mutations,' and explicit when-not-to with sibling tool names.

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