simpleinout-mcp

simpleinout_list_statuses

List status-change history for the entire company in a date range. Use created_at (epoch seconds) to filter, e.g. 'start..end'.

Instructions

List status-change history across the whole company within a date range.

    API: GET /statuses

    Args:
        created_at: Required. Date/time range filter, e.g. "1523854800..1524459599"
            (epoch seconds, `..` between two values for a range).

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": {
      "created_at": "<created_at>"
    },
    "name": "simpleinout_list_statuses"
  }
}

Input Schema

Name	Required	Description	Default
`created_at`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 7, 2026

TDQS

A3.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries full responsibility. It discloses the HTTP method (GET) and parameter format, but does not mention any behavioral traits such as auth requirements, pagination, rate limits, or response details. The read-only nature is implied by 'List' but not explicitly stated.

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 succinct and well-structured. The main purpose is front-loaded, the API endpoint is provided in a single line, and the parameter explanation is clear without unnecessary fluff. Every sentence adds value.

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?

For a simple one-parameter tool with an output schema, the description covers the essential aspects: purpose, parameter meaning, and endpoint. It does not explain pagination or potential limitations, but given the simplicity and output schema availability, it is nearly complete.

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

Parameters5/5

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

Schema description coverage is 0%, but the description thoroughly explains the only parameter 'created_at': it is required, acts as a date/time range filter, includes a concrete example, and specifies the epoch seconds format with '..' separator. This fully compensates for the schema's lack of description.

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 tool's function: 'List status-change history across the whole company within a date range.' This uses a specific verb and resource, and explicitly scopes it to the whole company, distinguishing it from sibling tools like simpleinout_list_my_statuses and simpleinout_list_user_statuses.

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?

The description implies usage for company-wide status history within a date range, but it does not explicitly mention when to use this tool versus alternatives. It provides context (whole company) but no direct exclusions or alternative recommendations.

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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