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TylerIlunga

Procore MCP Server

update_weather_log

Modify weather log entries in Procore projects to track and document daily weather conditions for construction management.

Instructions

Update Weather Log. [Project Management/Daily Log] PATCH /rest/v1.0/projects/{project_id}/weather_logs/{id}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYesUnique identifier for the project.
idYesUse log date as your ID. Format YYYYMMDD ie:20161108
weather_logYesweather_log
attachmentsNoWeather Log Attachments. To upload attachments you must upload the entire payload as `multipart/form-data` content-type and specify each parameter as form-data together with `attachments[]` as files.
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions 'PATCH' which implies a partial update, but doesn't disclose behavioral traits like whether it's idempotent, what permissions are required, if it's destructive, or how errors are handled. The description is minimal and misses critical mutation context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with no wasted words, consisting of a brief title-like phrase and API endpoint. However, it's overly terse and lacks front-loaded essential information, making it less helpful despite being structurally simple.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a mutation tool with 4 parameters, nested objects, and no annotations or output schema, the description is incomplete. It fails to explain what the tool returns, error conditions, or behavioral expectations, leaving significant gaps for an AI agent to understand how to use it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters (project_id, id, weather_log, attachments). The description adds no additional meaning beyond the schema, such as explaining the structure of 'weather_log' object or usage of 'attachments'. Baseline 3 is appropriate as the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the action ('Update Weather Log') and resource ('Weather Log'), but it's vague about what specific fields can be updated. It doesn't distinguish from sibling tools like 'create_weather_log' or 'update_weather_log_v1_1', leaving ambiguity about scope or version differences.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives like 'create_weather_log' or 'update_weather_log_v1_1'. The description lacks context about prerequisites, such as needing an existing weather log ID, or exclusions like when not to use it.

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

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