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jonmmease
by jonmmease

get_recent_messages

Retrieve recent iMessage conversations from macOS to access message history, filter by date, and manage pagination for AI assistants.

Instructions

Get the most recent messages across all conversations.

Args: limit: Maximum number of messages to return (default: 50) offset: Number of messages to skip (default: 0) after_date: Only return messages after this date (ISO8601 format)

Returns: Dictionary with messages list and pagination metadata.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
offsetNo
after_dateNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

With no annotations provided, the description carries full burden. It mentions the return format ('Dictionary with messages list and pagination metadata'), which is helpful, but doesn't disclose critical behavioral traits like whether this is a read-only operation, rate limits, authentication requirements, or what 'most recent' means in terms of sorting. For a tool with 3 parameters and no annotations, this leaves significant gaps.

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 well-structured and front-loaded with the core purpose, followed by clear sections for Args and Returns. Every sentence earns its place, with no wasted words, making it efficient and easy to parse.

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 tool's moderate complexity (3 parameters, no annotations, but with an output schema), the description is reasonably complete. It covers the purpose, parameters, and return structure, and the output schema handles return values. However, it lacks details on behavioral aspects like sorting order or error handling, which could be important for full context.

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?

The description adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains each parameter's purpose: 'limit' for maximum returns, 'offset' for skipping messages, and 'after_date' for filtering by date with format specified (ISO8601). This compensates well for the schema's lack of descriptions, though it doesn't detail constraints like valid ranges.

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 verb ('Get') and resource ('most recent messages across all conversations'), making the purpose immediately understandable. It distinguishes from siblings like 'get_conversation_messages' (which focuses on specific conversations) and 'search_messages' (which likely involves filtering/searching). However, it doesn't explicitly mention these distinctions, keeping it at 4 rather than 5.

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 implies usage for retrieving recent messages broadly, but doesn't explicitly state when to use this vs. alternatives like 'get_conversation_messages' (for specific conversations) or 'search_messages' (for filtered searches). It provides basic context but lacks explicit guidance on exclusions or preferred scenarios.

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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