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Telegram MCP

analyze_chat_activity

Compute deterministic activity statistics for a Telegram chat, enabling agents to analyze message frequency and trends and write narrative insights from exact metrics.

Instructions

Compute deterministic activity statistics; the calling agent writes narrative analysis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chatYes
limitNo
accountNo
topic_idNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.3.0

TDQS

C2.6/5.0
Behavior2/5

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

With no annotations, the description carries the full behavioral burden. It discloses that the computation is deterministic and that narrative analysis is left to the agent, but it says nothing about permissions, rate limits, what statistics are produced, or whether it is a safe read operation. This is too little for a tool with zero annotation coverage.

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 a single efficiently structured sentence with the core action front-loaded. The semicolon clause adds useful scope, and there is no wasted text, though the overall brevity leaves it under-informative rather than optimally concise.

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 four undocumented parameters, no annotations, and no output schema, the description is far too sparse to tell an agent how to call the tool correctly or what to expect back. It conveys only the highest-level idea and omits necessary operational and return-value context.

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

Parameters1/5

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

Schema description coverage is 0% across four parameters (chat, limit, account, topic_id), and the description does not mention or explain any of them. It fails to compensate for the complete lack of schema-level parameter documentation.

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 states a specific verb (Compute) and resource (deterministic activity statistics), which clearly separates it from the read/search/list siblings. It does not explicitly name any alternative tool, so it falls short of full sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains that the calling agent writes narrative analysis, which clarifies a division of labor, but it gives no guidance on when to use this tool versus alternatives like search_chat or read_chat, nor any prerequisite conditions. The agent is left to infer the use case.

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