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labyrinth-analytics

LoreConvo

Official

Get Context for Topic

get_context_for

Get relevant context about a topic from past sessions to guide current conversations.

Instructions

Get relevant session context for a topic.

Use at the start of a session to load prior decisions and context about a topic. Returns the most relevant session excerpts.

Args: topic: The topic to find context for (e.g., 'K-1 parser', 'rental insurance') max_results: Max excerpts to return (default 5) include_external: If True, include sessions flagged as external_tool_session. Default False. semantic: If True, use LanceDB hybrid search (Pro only). Falls back to FTS5 if index not yet built.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYes
semanticNo
max_resultsNo
include_externalNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations exist, so the description carries full burden. It explains return value ('most relevant session excerpts'), parameter behaviors (semantic fallback, include_external filtering), and notes Pro-only feature. However, it does not disclose edge cases like empty results or if the tool modifies state (though it appears read-only).

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 brief and well-structured: a one-line summary, a usage recommendation, then a parameter list. Every sentence adds value, and the layout is easy to scan.

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 the 4 parameters and presence of an output schema, the description covers key aspects: purpose, when to use, parameter details, and return type. It lacks minor details like case-sensitivity or partial match behavior, but overall provides sufficient context for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, meaning all parameter meaning is provided by the description. Each parameter (topic, max_results, include_external, semantic) is clearly explained with examples, defaults, and behavioral details (e.g., semantic fallback). This fully compensates for the lack of schema descriptions.

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 clearly states the tool retrieves session context for a topic, with specific usage guidance at session start. However, it does not explicitly differentiate from sibling tools like get_related_sessions or get_docs_for_session, which share similar retrieval functions.

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?

The description suggests using at session start to load prior context, but does not mention when to avoid this tool or direct users to alternatives. Usage context is implied rather than explicit.

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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