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falconbradley

Apple Messages MCP

search_messages

Search message bodies across every conversation and all history, using filters like chat, date range, and sender to locate specific messages.

Instructions

Search message bodies across every conversation, over all history.

Backed by a local index of decoded message bodies, which is brought up to date automatically. The first search on a large history has to build that index and may take a while; later searches are fast.

Args: query: Text to look for (case-insensitive substring match). Leave it out to search on the filters alone — chat_id plus a date range with no search term reads one conversation newest-first. limit: Maximum messages to return (default 30). chat_id: Restrict to one conversation from list_chats. from_me: True for only messages you sent, False for only received. after: Only messages at or after this time. before: Only messages at or before this time.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
afterNo
limitNo
queryNo
beforeNo
chat_idNo
from_meNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
totalYes
messagesNo
truncatedNo
Behavior5/5

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

With no annotations, the description carries the full burden of behavioral disclosure and does so thoroughly. It explains the local index, automatic index refresh, potentially slow first search on large histories, case-insensitive substring matching, and the default limit of 30. It also clarifies the effect of combining chat_id with a date range and no search term.

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: a one-sentence purpose, a brief performance caveat, then a clean argument list. Every sentence adds value, and the most important scoping information is front-loaded before parameter details.

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 six optional parameters, no annotations, and an output schema present, the description covers all essential invocation context: what is searched, how to filter, default behavior, performance expectations, and special modes. The output schema handles return-value details, so nothing critical is missing.

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 description coverage is 0%, so the description must compensate entirely, and it does. Every one of the six parameters (query, limit, chat_id, from_me, after, before) gets a meaningful explanation beyond its name, including defaults and the case-insensitive substring behavior of query.

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 opens with 'Search message bodies across every conversation, over all history,' a specific verb and resource that clearly distinguishes search_messages from sibling tools like get_chat_messages and get_message. It also clarifies scope (all conversations, all history) and notes that narrowing via chat_id is possible, reinforcing its identity as the broad search tool.

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 gives clear context for use: full-history body search, optional filters, and a special 'read a single conversation newest-first' mode when query is omitted. It does not explicitly name alternatives or state when not to use this tool, but it gives enough context to decide between search_messages and more targeted retrieval.

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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