get_semantic_model

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

With no annotations provided, the description carries the full behavioral burden. It discloses the return shape ('A string containing the details of the semantic model or an error message'), which is genuinely useful. However, it does not explain failure modes beyond a generic 'error message', what 'details' includes, whether the optional workspace falls back to the current context, or the behavior when both optional parameters are omitted. Non-zero disclosure, but several behaviors remain opaque.

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 structure is clean and front-loaded: a one-line summary followed by a compact Args block and a Returns block. Every line earns its place given the 0% schema coverage. The only blemish is the 'ctx: Context object containing client information' line, which describes a parameter not present in the input schema and could mislead an agent into trying to pass it.

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 simplicity — a get-by-ID read with no output schema and no annotations — the description covers the essentials: purpose, both parameters, and return format. But material gaps remain: it never clarifies what occurs when both optional parameters are unused, and it offers no pointer to list_semantic_models for discovering valid model IDs before calling this tool. Adequate for a simple tool, but not thorough.

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% — the schema offers only bare titles ('Model Id', 'Workspace') with no descriptions. The description compensates by defining workspace as 'Name or ID of the workspace' and model_id as 'ID of the semantic model', adding real semantic meaning the schema lacks. It does not go as far as explaining the relationship between the two options (e.g., whether workspace is needed to disambiguate a model_id), but the compensation is solid.

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 a clear verb+resource statement: 'Get a specific semantic model by ID.' The 'by ID' qualifier meaningfully distinguishes this from the sibling list_semantic_models tool, which enumerates models rather than fetching one. It stops short of a 5 because it does not explicitly name the sibling alternatives it is not, leaving the agent to infer 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?

Usage context is only implied through the 'by ID' phrasing — an agent can infer this is for retrieving a single known model, while list_semantic_models would be for discovery and get_model_schema for schema details. There is no explicit when-to-use/when-not-to-use statement, no mention of prerequisites (e.g., needing a valid workspace context), and no routing to sibling tools, so the guidance is adequate but entirely implicit.

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