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. It states 'GET' (implying a read-only operation) but doesn't disclose other behavioral traits like pagination behavior (implied by page/per_page parameters but not explained), rate limits, authentication requirements, or error handling. The description is minimal and lacks context beyond the HTTP method.

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 (one sentence plus a technical endpoint) but lacks structure. It front-loads the purpose but wastes space on a verbose endpoint string that adds little value for an AI agent. The sentence is clear but could be more informative without sacrificing brevity.

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 complexity (13 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the return format (e.g., what fields are included, pagination structure), behavioral constraints, or error cases. For a data retrieval tool with many optional parameters, more context is needed to guide effective use.

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%, so the schema fully documents all 13 parameters. The description adds no parameter semantics beyond what's in the schema—it doesn't explain relationships between parameters (e.g., that boolean flags control inclusion of specific fields in the response) or provide examples of typical usage. 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.

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

The description clearly states the verb ('Get') and resource ('Person's Assignment History Data'), making the purpose explicit. It distinguishes from siblings by specifying it's about a person's assignment history, whereas many siblings are about projects or other resources. However, it doesn't explicitly differentiate from similar tools like 'get_projects_assignment_history_data' (a sibling tool), which slightly reduces clarity.

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 doesn't mention prerequisites (e.g., needing company_id and person_id), nor does it compare with sibling tools like 'get_projects_assignment_history_data' for project-level data. The agent must infer usage from the tool name and parameters alone.

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