ai-workers-mcp

ai_generate

Generate plain text from a prompt using a free LLM. Supports optional system context to guide output.

Instructions

Generate text from a prompt using a configured free LLM. Plain prose only — no code, secrets, or file paths.

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": {
      "prompt": "<prompt>"
    },
    "name": "ai_generate"
  }
}

Input Schema

Name	Required	Description	Default
`prompt`	Yes	Generation prompt (plain prose, no code)
`context`	No	Optional system context (plain prose, no code)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Jun 14, 2026

TDQS

A4.1/5.0

Behavior3/5

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

No annotations are provided, so the description carries the burden. It mentions using a 'configured free LLM', which implies a model-based API with no side effects, but lacks details on latency, quotas, or idempotency. The constraint on input types is helpful but behavioral traits are minimally disclosed.

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 extremely concise with two sentences. The first sentence states the core action and model, the second adds critical constraints. Every word is necessary; no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description could have explained return format or usage considerations. It covers the purpose and input constraints adequately but leaves out behavioral details like whether the LLM is stateless or if there are rate limits. For a simple generation tool, it is minimally sufficient but incomplete.

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

Parameters4/5

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

Schema coverage is 100% with both parameters documented. The description adds context by reinforcing the 'plain prose' constraint across both parameters, going beyond the schema descriptions. However, it does not elaborate on the 'context' parameter's role beyond being optional system context.

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 'Generate text from a prompt' with specific verb and resource. It distinguishes from sibling tools like ai_summarize and ai_rewrite by emphasizing general generation without specialized constraints.

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 explicitly bounds usage to 'plain prose only — no code, secrets, or file paths', providing clear constraints. While it doesn't directly compare to siblings, the sibling list implies this is for free-form generation when specific tasks (e.g., summarization) are not needed.

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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