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telegram_agent_enroll

Enroll a Telegram chat for agentic handling with auto, advisory, or notify mode, and define a scenario policy covering persona, data scope, and toolset.

Instructions

Enroll a Telegram chat for agentic handling with a scenario. mode: auto (self-converse) | advisory (suggest, human sends) | notify. scenario (optional dict): persona, default_toolset, data_scope {models,fields,pricelist}, forbid[], company_id, quote_send (approve|auto), skills[] ({name,toolset,triggers[]}). Confidentiality is enforced by data_scope. Enrolments are per-principal — you never see or change another principal's.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNonotify
chat_idYesChat/group id (e.g. -100123...)
scenarioNoScenario policy (see description)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.31.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description provides some transparency about side effects: it creates an enrollment and enforces per-principal data isolation. It does not mention whether enrollment overwrites existing configurations, requires authorization, or can be reverted, leaving some behavioral aspects opaque.

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 brief and scannable, conveying the core purpose and key configuration options in a compact form. It could be slightly more structured (e.g., bullet points for mode and scenario), but it is not verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

It gives important contextual detail about per-principal isolation and data scope, which is relevant for safe use. It does not describe expected return values, error conditions, or post-enrollment behavior, leaving some gaps for a mutation operation without an output schema.

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?

Two of the three parameters have schema-level descriptions (chat_id and scenario), and the main description explains the mode enum and the nested scenario structure. This gives good coverage of parameter meaning, though the mode parameter itself lacks a direct schema description.

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 action ('Enroll a Telegram chat for agentic handling') and identifies the resource. It distinguishes itself from related tools like unenroll or set_scenario by using the specific verb 'enroll', but it does not explicitly name sibling alternatives.

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?

It explains the meanings of 'mode' and 'scenario' which helps in choosing appropriate values, and mentions per-principal isolation. However, it does not explicitly state when to use this tool versus alternatives like 'telegram_agent_set_scenario' or prerequisites such as authentication.

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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