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zcsabbagh

Knowledge Graph MCP Server

by zcsabbagh

add_edge

Create a relationship between two concepts in a knowledge graph. Specify source and target nodes, relation type, and optional strength.

Instructions

Create a relationship between two concepts in the knowledge graph.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
source_conceptYesSource node ID or concept name
target_conceptYesTarget node ID or concept name
relation_typeYesType of relationship. One of: - "prerequisite": Source must be learned before target - "builds_on": Target extends/deepens source concept - "related_to": Concepts are connected (bidirectional semantically) - "contradicts": Common misconception (source is wrong belief about target) - "applies_to": Source concept applies to target domain/topic - "parent_of": Ontological hierarchy (source is parent category of target)
strengthNoConfidence in the relationship from 0.0 to 1.0. Default 1.0.
reasoningNoExplanation of why this relationship exists

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations are provided, and the description does not disclose behavioral traits such as idempotency, side effects, or permission requirements beyond the basic creation action.

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 sentence with no extraneous information, achieving high conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having an output schema, the description does not explain return values or provide behavioral context. With no annotations, the description is insufficient for a tool with 5 parameters.

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?

All five parameters are fully described in the input schema (100% coverage), so the description adds minimal additional meaning, earning the baseline score.

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 creates a relationship between two concepts, distinguishing it from sibling tools like add_node which adds nodes, and query_graph which queries the graph.

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 specify when to use this tool versus alternatives, nor does it mention any conditions for use or avoidance.

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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