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observe_conversation

Save completed conversation turns and extract structured knowledge (preferences, decisions, constraints) as nodes and edges in a knowledge graph for persistent memory.

Instructions

Automatically observe a completed user-assistant turn. ALWAYS persists the verbatim turn first. Then runs extraction (graph inference) as optional enrichment. If extraction fails, the verbatim turn is still stored. Use after turns containing preferences, decisions, constraints, requirements, corrections, project facts, or meaningful task outcomes. Do not ask the user to trigger this. Returns: turn_id, verbatim_stored (bool), nodes_extracted (count), edges_inferred (count), extraction_errors (non-fatal). Required fields: 'user_message' (the user's text) and 'assistant_response' (the assistant's reply). Do NOT use 'user_text' or 'assistant_text' — those field names are not accepted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectNoOptional project or workspace name used to partition memory.
agent_idNoOptional agent or client identifier used to partition memory.
session_idNoOptional conversation or run identifier used to partition memory.
user_messageYesThe user's message from the completed turn.
assistant_responseYesThe assistant's response from the completed turn.
Behavior5/5

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

Since no annotations are provided, the description fully discloses behavior: always persists verbatim, extraction is optional and non-fatal, and what happens on failure, along with return fields.

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 well-structured with distinct sections, but slightly lengthy with some redundancy; however, it front-loads the core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description covers return values, error handling, and required fields thoroughly, making it complete for a 5-parameter tool.

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

Parameters4/5

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

Schema coverage is 100%, but the description adds value by warning against incorrect field names and explaining the optional parameters for partitioning.

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 specifies the action ('observe') and resource ('completed user-assistant turn'), and distinguishes from siblings like 'store_node' by focusing on persisting turns and optional graph extraction.

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?

It explicitly states when to use the tool (after turns with specific content) and instructs not to ask the user to trigger it, but does not name alternative tools for when not to use.

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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