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Search messages globally

search_messages_globally
Read-onlyIdempotent

Search across all Telegram chats simultaneously using comma-separated terms. Apply date, chat type, and public username filters to locate messages.

Instructions

Search all Telegram chats at once (not scoped to one chat). Comma-separated query terms; optional filters by date, chat kind, and public username. Success: message list and metadata dict. Global search ignores include_total_count. Full documentation: https://github.com/leshchenko1979/fast-mcp-telegram/blob/main/docs/Tools-Reference.md

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch terms, comma-separated for multiple terms (OR-style global search). Required.
limitNoMaximum messages to return (recommended 50 or less).
min_dateNoInclusive minimum date filter (ISO 8601 date or datetime). Omit for no lower bound.
max_dateNoInclusive maximum date filter (ISO 8601 date or datetime). Omit for no upper bound.
chat_typeNoComma-separated chat kinds: private, bot, group, channel. Case-insensitive; extra spaces allowed.
publicNoIf true, prefer chats with a public username; if false, without. Does not apply to private DMs. Omit to skip this filter.
auto_expand_batchesNoExtra search batches to run when filters narrow results. Higher values may return more matches at the cost of latency.
include_total_countNoIf true, response may include total_count where supported (per-chat search; ignored for global search).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Annotations already mark readOnlyHint and idempotentHint. The description adds behavioral context: 'Global search ignores include_total_count' and mentions success format. No contradictions.

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?

Two efficient sentences plus a documentation link. Front-loaded with purpose; every sentence adds value. No redundancy.

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 8 parameters and global scope, the description covers core functionality, success response, and a doc link. Output schema exists, so return values are covered. Could mention batching behavior, but schema covers it.

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

Parameters3/5

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

Schema covers all 8 parameters with descriptions (100% coverage). Description adds minimal extra: comma-separated OR-style and ignoring include_total_count. Baseline 3 is appropriate.

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 clearly states it searches all Telegram chats at once, distinguishing it from siblings like `get_messages` which is scoped to one chat. The verb 'search' and resource 'messages globally' are specific.

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 explicitly says 'not scoped to one chat' and lists optional filters, implying when to use this tool for global search. However, it does not explicitly name alternative tools or state when not to use, though sibling names provide context.

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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