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HarleyVader

LLM Toolshed MCP Server

by HarleyVader

cag_context

Builds a Context Augmented Generation (CAG) from the BambiSleep knowledge graph by traversing relationships from a specified entity to supply relevant context for AI responses.

Instructions

Build CAG (Context Augmented Generation) from BambiSleep knowledge graph

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNoDepth of relationship traversal
entityYesEntity to build context around (e.g., trigger name, session name)
Behavior2/5

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

With no annotations provided, the description must disclose behavioral traits, but it only says 'Build CAG' without explaining side effects, output format, or any processing details. It gives a minimal hint of construction but fails to convey what the agent should expect regarding safety or results.

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, front-loaded sentence that directly conveys the action and resource. It is appropriately concise with no wasted words. It could be slightly expanded to include usage context, but as written it 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?

Given the absence of annotations and output schema, the description is incomplete. It tells what the tool does but does not explain what the generated CAG looks like, how depth affects the result, or any return format. An agent would lack critical invocation guidance.

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 description coverage is 100% (both 'entity' and 'depth' have descriptions). The tool description adds no extra parameter meaning beyond what the schema provides, which aligns with the baseline 3 for high coverage. It does not compensate with additional context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Build') and resource ('BambiSleep knowledge graph'), and introduces the acronym CAG (Context Augmented Generation). It clearly differs from sibling tools like rag_query or semantic_search by emphasizing construction of context. However, it does not explain what CAG output entails, so it is clear but not fully precise.

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?

No guidance is given for when to use this tool versus alternatives. There is no mention of prerequisites, scenarios, or exclusions. The description only states the action, leaving the agent to infer usage without support.

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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