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generate_prompt

Generate a deployment-ready AI agent system prompt with eight structured sections including identity, capabilities, guidelines, and constraints for any professional role.

Instructions

Generate a deployment-ready AI agent system prompt using Prompt Forge's v2 engine. Returns a complete 8-section system prompt (Identity, Capabilities, Guidelines, Domain Knowledge, Interaction Protocol, Output Format, Constraints, First Message). Requires ANTHROPIC_API_KEY environment variable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
industryNoIndustry context (e.g. "Finance", "Healthcare"). Helps the engine reference industry-specific tools and frameworks.
agent_descNoShort description of the agent specialty (e.g. "Automated scoring, loan analysis"). If omitted and agent_name matches a registry agent, the registry description is used.
agent_nameYesThe agent role name (e.g. "Credit Risk Underwriter", "Medical Scribe", "SOC Analyst Agent"). Can be any role — not limited to the built-in registry.
user_contextNoYour role and situation for personalization (e.g. "I'm a solo financial advisor serving retirees in Florida"). Pro feature on the web app, available to all MCP users.
Behavior3/5

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

No annotations are provided, so the description must cover behavioral traits. It states the tool requires an API key and returns an 8-section prompt, but it does not disclose side effects (e.g., if it makes external API calls), safety implications, or whether it is read-only. Some key behavioral context is missing.

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 two short sentences. The first sentence immediately states the purpose and output, and the second adds the key requirement. There is no wasted text, and the structure is front-loaded for quick understanding.

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?

The description outlines the output structure (8 sections) and the environment variable requirement, providing good context for a tool with no output schema. However, it could be more complete by specifying the return type (e.g., string or object) and any additional details about the generation process.

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?

All four parameters have descriptions in the input schema (100% coverage), so the schema already explains parameter meaning. The description adds context about the API key requirement and the output structure, but does not provide additional semantics beyond what the schema offers, resulting in a baseline score of 3.

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 generates a deployment-ready AI agent system prompt using the v2 engine. It specifies the exact output (8-section system prompt) and distinguishes it from sibling tools (list_agents, list_industries) which only retrieve lists, not generate prompts.

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 mentions the requirement of the ANTHROPIC_API_KEY environment variable, giving clear usage context. However, it does not explicitly state when not to use this tool or suggest alternative approaches, leaving a gap in guidance for the AI agent.

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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