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hwillGIT
by hwillGIT

library_message_record

Save a conversation message with role and content to a session for later retrieval. Use to persist assistant replies or other messages for durable AI context storage.

Instructions

Shelve one conversation message for a virtual-context session. External model gateways should record assistant replies after each response.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
roleYes
contentYes
collectionNoOptional library collection/namespace; defaults to the configured collection.
importanceNo
session_idNocodex
Behavior3/5

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

Annotations are minimal (all false), so description carries the burden. The description says 'Shelve one conversation message' which implies a write operation (consistent with readOnlyHint=false). It adds context about external model gateways and recording replies, but doesn't disclose details like whether this replaces existing messages, if there are size limits, or if it handles different roles (though role is in schema). No contradiction with annotations.

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 concise (two sentences) and front-loaded with the core action. No wasted words, but it's slightly terse given the tool's complexity and the need to clarify parameter semantics.

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 5 parameters, only 2 required, and no output schema, the description is thin. It doesn't explain what happens after shelving (return value?), how roles are handled, or the significance of 'importance'. The tool's purpose is clear, but the description lacks completeness for an agent to use it effectively without digging into schema defaults.

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

Parameters2/5

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

Schema coverage is low (20%) - only 'collection' has a description. The tool description doesn't elaborate on any parameters besides saying 'one conversation message'. It fails to explain the meaning of 'importance', 'session_id', or the role/content relationship. The description adds minimal value beyond the schema, leaving the agent to infer semantics from the enum and defaults.

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's purpose: to shelve one conversation message for a virtual-context session. It identifies the resource (conversation message) and the action (shelve/record). It distinguishes from siblings by mentioning 'virtual-context session' and specifying 'assistant replies', though it doesn't explicitly differentiate from library_shelve or library_shelve_document.

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 provides a clear usage context: 'External model gateways should record assistant replies after each response.' This specifies when to use (after each response) and for what (assistant replies). It doesn't explicitly exclude other tools or mention alternatives, but the context is clear enough for an agent.

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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