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leshchenko1979

Fast MCP Telegram

Search messages globally

search_messages_globally
Read-onlyIdempotent

Search across all Telegram chats simultaneously using comma-separated terms, with optional filters for date range, chat type, and public username to find relevant 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
okNo
errorNo
operationNo
codeNo
paramsNo
exceptionNo
actionNo
error_codeNo
messagesNo
has_moreNo
total_countNo
_warningNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.37.0
  2. Removed
  3. Changed3 schema fields changedv0.36.0
    • removedOutput schema / additionalProperties
      Removed value: -true
    • addedOutput schema / description
      Added value: +"Return type for ``search_messages_globally`` and ``get_messages``."
    • addedOutput schema / properties
      Added value: +{
      +  "_warning": {
      +    "type": "string"
      +  },
      +  "action": {
      +    "type": "string"
      +  },
      +  "code": {
      +    "type": "integer"
      +  },
      +  "error": {
      +    "type": "string"
      +  },
      +  "error_code": {
      +    "type": "string"
      +  },
      +  "exception": {
      +    "additionalProperties": true,
      +    "type": "object"
      +  },
      +  "has_more": {
      +    "type": "boolean"
      +  },
      +  "messages": {
      +    "items": {
      +      "additionalProperties": true,
      +      "type": "object"
      +    },
      +    "type": "array"
      +  },
      +  "ok": {
      +    "type": "boolean"
      +  },
      +  "operation": {
      +    "type": "string"
      +  },
      +  "params": {
      +    "additionalProperties": true,
      +    "type": "object"
      +  },
      +  "total_count": {
      +    "type": "integer"
      +  }
      +}
  4. First observedv0.21.0

TDQS

A4.2/5.0
Behavior4/5

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

The description discloses a non-obvious behavior (global search ignores include_total_count) and states the success return shape ('message list and metadata dict'). This adds context beyond the readOnly/idempotent annotations. No contradictions exist with the annotations.

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?

Three concise sentences front-load the core purpose, followed by key search behavior and a documentation link. Every sentence earns its place with no redundancy or fluff.

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 8-parameter schema with full descriptions, the description covers global scope, return format, and a behavioral caveat, plus a documentation link for deeper reference. It's sufficiently complete for an agent to select and invoke the tool correctly without needing additional information.

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 coverage is 100% with rich parameter descriptions, so the baseline is 3. The description restates query semantics ('Comma-separated query terms') and summarizes filter options, but doesn't add significant new parameter details beyond what the schema already provides. The include_total_count caveat is a small extra.

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 the action ('Search all Telegram chats at once') and resource (messages across all chats). The parenthetical 'not scoped to one chat' explicitly differentiates it from sibling tools like get_messages, giving it a distinct and unambiguous purpose.

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 implies use when needing cross-chat search by stating 'Search all Telegram chats at once' and contrasting with scoped search. It also provides a specific behavioral caveat ('Global search ignores include_total_count'). However, it doesn't explicitly name alternative tools for single-chat search, so the guidance is clear but not fully explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.