Execute a direct query to the internal LLM engine. Use an optional system prompt to tailor the response.
Execute a direct query using the internal LLM engine (OpenRouter/OpenAI compatible, disabled by default)
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The prompt to send to the internal LLM | |
| systemPrompt | No | Optional system prompt override |
Changes observed during successful MCP inspections.
v1.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It usefully discloses OpenRouter/OpenAI compatibility and that the engine is disabled by default. It does not mention what happens when disabled, the response format, rate limits, or side effects, which are relevant for an LLM query tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One efficient sentence that front-loads the core action ('Execute a direct query') and appends two meaningful qualifiers (compatibility and default-disabled) without any wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter tool, the core purpose is clear, but the absence of an output schema means the return value is left unspecified. The 'disabled by default' caveat is stated but not expanded (e.g., error behavior or configuration requirements), leaving some context gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds no parameter-specific meaning beyond the schema entries; it only restates the direct-query context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Execute a direct query using the internal LLM engine.' The phrase 'direct query' contrasts with specialized siblings like llm_summarize or llm_decompose_task, but does not explicitly name them or explain the distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies a raw, direct LLM query use case, which is a reasonable signal. However, it provides no explicit 'when to use vs alternatives' guidance among the many sibling LLM tools, and no exclusions or alternative tool names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.