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by io.teddit
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Add prompts

add_prompts

Add up to 200 buyer questions (prompts) to a site; returns how many were added and which failed, with the reason. Write each as the full question a person would ask an assistant, without the brand or any vendor name: a question that names the brand cannot show whether the assistant names it on its own. New prompts are active unless status says draft. For raw keyword lists use import_queries instead.

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": {
      "projectId": "<projectId>",
      "prompts": "<prompts>"
    },
    "name": "add_prompts"
  }
}

Input Schema

NameRequiredDescriptionDefault
promptsYesThe questions to add, 1–200.
projectIdYesThe project id, from list_projects.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare write semantics (readOnlyHint=false), non-destructive, and closed-world, so the safety profile is covered. The description adds genuine behavioural context on top: partial success reporting with per-failure reasons and the default activation state ('New prompts are active unless status says draft'). It does not mention rate limits or rollback characteristics, which is the main remaining gap.

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?

Front-loaded with the action and limits, then input rules, then the alternative. Every clause carries information — batch cap, return shape, authoring rule, default status, sibling routing — with no filler.

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?

With no output schema, the description compensates by stating what the response reports (count added plus failures with reasons). Combined with the 200-item cap, default status, and the import_queries alternative, an agent has everything needed to call and interpret it correctly.

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%, and the prompt text rule and default status are already stated in the schema descriptions, so the description largely restates structured data. The added value is the rationale (a brand-named question can't test whether the assistant names the brand), which clarifies intent more than parameter mechanics. Baseline 3 is appropriate.

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?

States a specific verb and resource (add buyer questions/prompts to a site), the batch ceiling of 200, and the return summary ('how many were added and which failed, with the reason'). It also distinguishes itself from the sibling import_queries, so an agent can separate it from its neighbours without opening the schema.

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

Usage Guidelines5/5

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

Gives explicit routing guidance: use this for buyer questions written as natural-language questions, and 'For raw keyword lists use import_queries instead.' It also states the input-shaping rule (no brand or vendor names) and why it exists, leaving little to inference.

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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