calibrate_mmm

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

With no annotations supplied, the description carries the full behavioral burden. It usefully states that calibration produces a new artifact rather than modifying in place and that lineage is recorded. However, it does not disclose potential side effects such as long-running MCMC computation, random-seed behavior, or failure modes.

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?

A single sentence delivers the action, the subject, the mechanism, and the output with no filler. The most decision-relevant information is front-loaded ('Calibrate an existing MMM') before the more detailed outcome.

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 complex calibration tool with no output schema and no annotations, the description is adequate but not complete. It communicates the core purpose and the main output behavior, but omits details such as the necessity of a prior fit_mmm step, how sampler settings affect the run, and what the lineage-linked artifact means operationally.

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?

The description only loosely implies the required inputs ('existing MMM' and 'experimental lift test measurements') without naming model_id, lift_tests, or sampler. Since schema description coverage is 0% and the top-level parameter has no description, the tool description needed to document the parameter structure; it does not. Nested schema $defs include per-field descriptions, which partially help, but this dimension credits the description text.

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 opens with the specific verb 'Calibrate' and a precise object, 'an existing MMM', and names the method, 'experimental lift test measurements'. It also states the outcome ('a new calibrated model artifact linked via lineage'), which separates it from siblings like fit_mmm or recommend_next_measurement.

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 phrase 'existing MMM' clearly establishes the prerequisite that a fitted model must already exist, and 'experimental lift test measurements' identifies the data condition that triggers this tool. It does not explicitly list exclusions or alternatives, but the context is clear enough for an agent to distinguish this from initial model fitting.

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