Vectra AI MCP Server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return type ('JSON string with list of assignments'), which is helpful, but lacks details on permissions, rate limits, pagination, error handling, or whether it's a read-only operation. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 brief and front-loaded with the core purpose in the first sentence. The second sentence about returns is useful but could be integrated more seamlessly. Overall, it's efficient with minimal waste, though minor structural improvements could enhance readability.

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 moderate complexity (2 parameters, no annotations, but with an output schema), the description is partially complete. It covers the basic purpose and return format, but lacks behavioral context (e.g., permissions, pagination) and usage guidelines. The output schema reduces the need to explain return values, but more operational details would improve completeness.

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 both parameters (user_id and resolved). The description adds no additional parameter semantics beyond what's in the schema, such as format examples or constraints. Baseline 3 is appropriate when the schema handles parameter documentation effectively.

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 ('List') and resource ('investigation assignments assigned to a user/analyst'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_assignments' (which might list all assignments without user filtering) or 'get_assignment_detail_by_id' (which retrieves details for a specific assignment), missing full sibling differentiation.

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 sibling tools like 'list_assignments' (for all assignments) or 'get_assignment_for_entity' (for entity-based assignments), nor does it specify prerequisites or exclusions, leaving usage context unclear.

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