Skip to main content
Glama

raggy_link

Link two memories in the knowledge graph to record relationships like cause, resolution, supersession, or contradiction. Use when one memory updates, refines, or corrects another.

Instructions

Connect two related memories in the knowledge graph. Use when a new memory supersedes/refines/contradicts an old one, or when an error resolves a past bug. Relationship types: caused_by, resolved_by, supersedes, refines, contradicts, related_to, follows_from, part_of.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNoDescription of the relationship
source_idYesThe "from" memory ID
target_idYesThe "to" memory ID
relationship_typeYesType of relationship

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.1

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are present, so the description carries the burden of behavioral disclosure. It communicates the core behavior—creating a relationship in the knowledge graph—and the relationship semantics. However, it does not disclose side effects such as whether an existing relationship is replaced, whether duplicate links are allowed, or what the operation returns.

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 compact and front-loaded with the primary action and usage guidance. The list of relationship types partly duplicates the schema enum, but it is still helpful for quick understanding and does not make the description bloated.

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 all parameters are documented in the schema and the description provides clear purpose and usage triggers, an agent can select and invoke this tool correctly. The main gaps are the lack of output/return behavior and edge-mutation semantics, but these are minor for a relatively simple graph-link operation.

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?

The input schema already describes all four parameters with 100% coverage and defines the relationship_type enum. The description adds useful context by mapping scenarios to relationship types, but it does not add significant meaning beyond the schema, so the baseline of 3 applies.

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 uses a specific verb and resource: 'Connect two related memories in the knowledge graph.' It lists eight concrete relationship types, making the tool's purpose unmistakable and clearly distinct from sibling tools like raggy_remember or raggy_recall.

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?

The description explicitly states when to use the tool with concrete scenarios: 'Use when a new memory supersedes/refines/contradicts an old one, or when an error resolves a past bug.' It does not name alternatives or explicitly state when not to use it, so it stops short of a 5.

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