Procore MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'GET' (implying a read-only operation) but does not clarify if it requires authentication, has rate limits, returns paginated results (though parameters like 'page' and 'per_page' hint at this), or what happens on errors. The description lacks essential behavioral traits, leaving significant gaps for a tool with 14 parameters.

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 concise (two short phrases) but poorly structured. It front-loads with a tautological statement and appends an endpoint path that adds little value for an AI agent. While not verbose, it fails to convey useful information efficiently, as the content does not earn its place in aiding tool selection or invocation.

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 (14 parameters, no annotations, no output schema), the description is incomplete. It does not explain the return format, typical use cases, or how parameters interact to filter results. Without annotations or an output schema, the description should provide more context about what 'Look Ahead Data' entails and how to interpret results, but it falls short.

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%, meaning all parameters are documented in the input schema. The description adds no additional parameter semantics beyond what the schema provides (e.g., it does not explain how 'jobTitleIds' or 'assignmentCount' affect the report output). With high schema coverage, the baseline score is 3, as the description does not compensate with extra insights.

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 'Get Look Ahead Data. [Resource Management/Resource Planning] GET /rest/v1.0/workforce-planning/v2/companies/{company_id}/groups/{group_id}/reports/look-ahead' is tautological—it essentially restates the tool name ('get_look_ahead_data') and adds an HTTP method and endpoint path without explaining what 'Look Ahead Data' actually is. It does not specify the verb (e.g., 'retrieve' or 'fetch') or resource (e.g., 'future assignments' or 'workforce projections') in a meaningful way, leaving the purpose vague.

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 provides no guidance on when to use this tool versus alternatives. It does not mention any sibling tools (e.g., other reporting or workforce planning tools) or contextual prerequisites, such as needing specific permissions or data availability. This absence of usage context makes it difficult for an agent to determine appropriate invocation scenarios.

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