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

Annotations already declare readOnly, idempotent, closed-world, non-destructive behavior, so the safety profile is covered. The description adds real context beyond that: the numbers come from actual measurements (llama.cpp on CPU, 1/2/4 concurrent users, quality check) and match a published sizing page, which tells the agent results are deterministic estimates rather than live probes.

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 core purpose and provenance are front-loaded in the first clause, and the return values are listed compactly at the end. It is a dense, somewhat run-on sentence, but every clause carries information and nothing is redundant with the title.

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?

With 8 optional parameters, no required fields, and no output schema, the description carries the burden of explaining returns and does so concretely: servers, memory, typical and p95 answer time, answers per hour, quality score, and a cross-model comparison. It also states the measurement methodology and source, leaving nothing essential for a correct call missing.

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%, and the schema already explains each enum value, default, and range in detail. The description only reinforces that model choice and concurrency levels drive the result; it adds no parameter syntax or interaction detail beyond the schema, so baseline 3 is appropriate.

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 names a specific capability: computing CPU server count and memory for a local LLM assistant for Directum RX, including the measurement basis (llama.cpp, load tests at 1/2/4 users, quality check). It is clearly distinguishable from size_rx, which sizes the RX server itself rather than the AI assistant hardware.

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?

Usage context is explicit: this is for sizing hardware for a local LLM assistant that talks to RX through MCP tools, based on measured benchmarks. There are no explicit exclusions or a direct 'use size_rx instead when...' statement, so it falls short of a 5.

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