run_agent_with_external_model

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

The description discloses important behavioral traits: the API key is never passed as an argument, it must be set as an environment variable, and the model/base_url parameters override environment variables. It also explains the journal parameter behavior (leave blank and the agent will ask the user). This goes beyond what annotations provide (none) and gives the agent actionable operational knowledge.

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?

The description is well-structured with a clear opening sentence followed by parameter explanations. It's slightly longer than necessary but every sentence adds value. The parameter list is front-loaded after the main purpose statement, making it easy to scan.

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?

Given the tool's complexity (5 params, 0% schema coverage, no annotations), the description covers the key operational details: agent selection, journal behavior, environment variable overrides, and external endpoint configuration. The output schema exists, so return values don't need explanation. Minor gap: task_input is not explicitly described, but its purpose is clear from context.

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 0%, so the description carries the full burden. It explains agent_name (with the six valid values), journal (with reference to list_supported_journals and behavior when blank), model (overrides AQUA_MODEL), and base_url (overrides AQUA_BASE_URL with default). The only parameter not explicitly described is task_input, but its meaning is inferable from the tool's purpose. This is strong compensation for the schema gap.

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 clearly states the tool's purpose: run one of six manuscript-writing agents against an external OpenAI-compatible model. It names the specific agents and distinguishes this from the default model usage. The verb 'run' plus the resource 'manuscript-writing agents' is specific and unambiguous.

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 explains when to use this tool (when an external model is needed instead of the MCP client's default) and provides setup context (AQUA_API_KEY environment variable). It doesn't explicitly name alternatives or exclusions, but the context is clear enough for an agent to select it appropriately.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.