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mcasdfgf

MCP Roo Memory

graph_add_relation

Create a relation between two nodes using one of 22 predefined types to model hierarchical, semantic, index, chronological, or dependency connections.

Instructions

Create a relation between two nodes. Supports 22 relation types: Hierarchical (contains, decomposes_to, belongs_to), Semantic (derives_from, supports, contradicts, related_to, questions, answers), Index (indexes Entity->Fileref, extracted_from Fact/Chunk->Fileref, references, implements, relates_to_file), Chronological (sequel_to, supersedes, leads_to, resolves, triggers), Dependency (depends_on, blocks, constrained_by).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
from_idYesSource node ID
to_idYesTarget node ID
typeYesRelation type (22 types available)
weightNoRelation strength 0.0-1.0
Behavior2/5

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

No annotations are provided, so the description bears full responsibility. It does not disclose side effects (e.g., overwriting existing relations), prerequisites (e.g., node existence), or constraints (e.g., weight bounds). This is a significant gap for a mutation tool.

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?

The description is a single sentence with a parenthetical list, front-loaded with the core action. While the list of 22 types is lengthy, it is necessary for clarity and the overall structure is efficient.

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 no output schema, the description does not explain return values, error handling, or preconditions (e.g., that nodes must exist). This lack of completeness makes it harder for an agent to use correctly without guessing.

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%, so the baseline is 3. The description adds value by categorizing the 22 enum values into groups (Hierarchical, Semantic, etc.), which is not in the schema. However, no additional meaning is provided for 'weight' beyond what the schema states.

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?

Describes the action with a specific verb+resource ('Create a relation between two nodes') and enumerates 22 relation types in categories, clearly distinguishing it from sibling tools like graph_add_node (which creates nodes).

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

Usage Guidelines3/5

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

No explicit when-to-use or alternatives are mentioned. The relation type categories provide implicit context, but the description does not guide the agent on when this tool is preferable to other graph tools (e.g., graph_decompose).

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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