ai-agent-logs-mcp

create_prompt

Log an AI agent prompt with project, mode, duration, device, rate, and full text. Auto-authenticates when needed to create a persistent record.

Instructions

Create a prompt log row (POST /api/v1/prompts). Auto-logs in if needed.

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": {
      "project": "<project>",
      "title": "<title>",
      "mode": "<mode>",
      "duration_ms": "<duration_ms>",
      "device": "<device>",
      "rate": "<rate>",
      "prompt": "<prompt>"
    },
    "name": "create_prompt"
  }
}

Input Schema

Name	Required	Description
`mode`	Yes	Agent mode (e.g. agent, ask, plan)
`rate`	Yes	Integer prompt rate 0–10
`title`	Yes	Short title
`device`	Yes	Device / hostname
`prompt`	Yes	Full prompt text
`project`	Yes	Project name
`duration_ms`	Yes	Duration in milliseconds
`occurred_at`	No	Optional RFC3339 timestamp; defaults to now on the API

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 22, 2026

TDQS

A3.6/5.0

Behavior3/5

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

The description discloses one behavioral trait: 'Auto-logs in if needed.' It does not mention error handling, response behavior, or side effects beyond creation. With no annotations, this is a moderate level of transparency.

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 concise, consisting of one short sentence with an optional technical detail (HTTP method). Every word adds value, and there is no unnecessary verbiage.

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 creation tool, the description is mostly complete. It lacks explicit mention of the return value (e.g., the created row) and error conditions, but given the schema's thorough parameter descriptions and the auto-login hint, it is adequate for typical usage.

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?

The schema provides 100% coverage of parameters with descriptions (e.g., mode, rate, duration_ms). The tool description adds no additional parameter-specific meaning, so it meets the baseline for high schema coverage.

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 purpose with a specific verb ('Create') and resource ('prompt log row'), and includes the HTTP endpoint. It is distinct from sibling tools like list_prompts and list_sessions.

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 doesn't explicitly state when to use this tool over alternatives. It implies creation vs. listing, but there is no explicit 'use this for' or 'instead of' guidance, and the mention of auto-login is more about behavior than usage context.

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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