MCP Time Server

Convert Timezone

convert_time

Read-onlyIdempotent

Convert a specific time from one IANA timezone to another. Provide the time, source timezone, and target timezone to get the converted result.

Instructions

Converts a time from a source IANA timezone to a target IANA timezone.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "time": "<time>",
      "sourceTimezone": "<sourceTimezone>",
      "targetTimezone": "<targetTimezone>"
    },
    "name": "convert_time"
  }
}

Input Schema

Name	Required	Description
`time`	Yes	The time to convert (e.g., "2025-03-23 12:30:00")
`sourceTimezone`	Yes	Source IANA timezone name (e.g., "Asia/Shanghai")
`targetTimezone`	Yes	Target IANA timezone name (e.g., "Europe/London")

Output Schema

Name	Required	Description	Default
`convertedTime`	Yes
`hourDifference`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.0.5Aug 1, 2026

TDQS

A4.1/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds the IANA timezone detail, which is useful. However, it does not disclose edge-case behaviors (e.g., invalid timezones, DST handling) beyond what the schema and annotations provide. With annotations present, a score of 3 is appropriate.

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 a single, well-structured sentence with no redundant information. Every word contributes to the meaning, and it is front-loaded with the core operation.

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 the tool's simplicity, the presence of a complete output schema, and thorough annotations, the description is sufficiently complete. It correctly identifies the operation without needing to cover return values or error cases, which are handled elsewhere.

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% — every parameter has a description with examples. The description adds no additional parameter semantics beyond what the schema already provides, so the baseline of 3 applies.

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 explicitly states the tool's verb ('converts'), resource ('a time'), and scope ('from a source IANA timezone to a target IANA timezone'). It clearly distinguishes this from sibling tools like current_time or relative_time, which serve different time-related purposes.

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 implies a clear usage context: use this tool when you need to convert a time between two IANA timezones. It does not explicitly mention alternatives or exclusions, but the clarity of the purpose makes it easy for an agent to select the tool appropriately. A slight deduction for lacking explicit contrast with sibling tools.

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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