Bulk Updates For Daily Logs

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

The description discloses significant behavioral details beyond the annotations: it states the operation is an update (consistent with readOnlyHint=false and destructiveHint=false), mentions the status-only restriction, explains that project_id defaults to procore_set_config, and maps parameters to the request payload ('nested under "updates"'). It also documents error responses (401, 403, 404) and the success return value. No contradiction with the annotations exists.

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 information-dense and well-organized: it starts with the core purpose, then limits, usage preference, parameter mapping, defaults, return value, error handling, and API endpoint. Every sentence adds value, though the length is substantial. The structure aids readability, with a logical flow from action to constraints to invocation details.

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 (19 parameters, no output schema), the description covers most essential context: the 300-log limit, the default for project_id, error codes, and the API endpoint. It also gives a high-level mapping of how parameters translate to the request. The main gap is the lack of detail on what each 'Update Data for Log Type' entry must contain, but the description still provides enough for an agent to make an initial correct invocation.

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?

Although the schema parameter descriptions are generic ('array of Update Data for Log Type'), the description adds meaningful semantics: 'Pass the record's fields as top-level arguments — they are nested under "updates" in the request payload for you.' It also clarifies the project_id default and confirms it is required. However, it does not detail the structure of each log's update data, leaving some ambiguity in how to populate the arrays.

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 function: 'Updates multiple daily logs with single request.' It specifies the resource (daily logs) and the operation (bulk update), and distinguishes itself from single-record updates by recommending it over repeated single-record calls. The phrase 'Currently only status update is available' further narrows scope, making the purpose 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 gives explicit usage guidance: 'Prefer this over repeated single-record calls when handling many bulk updates at once.' It also sets a clear limitation ('300 logs maximum is allowed per request') and clarifies the current capability ('Currently only status update is available'). This enables the agent to decide when to use this tool versus alternatives.

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