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Graph-Memory

Graph-Memory

by stevepridemore
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Graph Boost

graph_boost
Idempotent

Strengthen graph edges when users confirm recalled information. Use after a positive confirmation to increase relationship weight, saturating at 1.0.

Instructions

Increase an edge's weight when the user confirms recalled information. Call this when the user says 'yes', 'exactly', or confirms something you retrieved from the graph. Persists immediately; weight clamps at 1.0 so repeated boosts saturate rather than overflow. Returns the previous and new weight.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "from_name": "<from_name>",
      "to_name": "<to_name>",
      "relation": "<relation>"
    },
    "name": "graph_boost"
  }
}

Input Schema

NameRequiredDescriptionDefault
amountNoBoost amount (default: 0.15)
reasonNoWhy boosting
to_nameYesTarget entity name or ID
relationYesRelationship type (e.g. WORKS_ON, PREFERS)
from_nameYesSource entity name or ID

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.2.2
  2. Removedv0.1.4
  3. Changed1 schema field changedv1.0.1
    • addedInput schema / properties / amount / default
      Added value: +0.15
  4. First observedv1.0.0

TDQS

A4.4/5.0
Behavior5/5

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

The description adds key behavioral details beyond annotations: immediate persistence, weight clamping at 1.0 to prevent overflow, saturation on repeated boosts, and return of previous and new weight. No contradiction with idempotentHint.

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?

Three concise sentences, each serving a distinct purpose: purpose, usage trigger, and behavioral details. No wasted words, front-loaded with primary action.

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?

The description covers purpose, trigger, behavior, and return value. While it lacks explicit mention of error handling (e.g., missing edge), it is largely sufficient for a simple boost tool. No output schema exists, so the return info is valuable.

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% with good parameter descriptions. The tool description adds context about user confirmation and clamping but does not significantly elaborate on individual parameters beyond what the schema provides. Baseline 3 is appropriate.

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 increases an edge's weight when the user confirms recalled information, which is a specific verb and resource. It distinguishes it from sibling tools like graph_weaken (weight decrease) and others by tying usage to user confirmation.

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?

Explicitly states when to call: when the user says 'yes', 'exactly', or confirms recalled information. However, it does not mention when not to use it or contrast with alternatives like graph_weaken for decreasing weight.

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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