Memory 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 states the tool validates props to prevent hallucination and checks existence in '@props observations,' but doesn't describe what happens during validation (e.g., returns success/failure, error messages), whether it's read-only or has side effects, or any constraints like rate limits. This is a significant gap for a validation tool with zero annotation coverage.

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 extremely concise and front-loaded, consisting of two sentences that directly state the purpose and method. Every sentence earns its place by clarifying the tool's function without unnecessary details, making it efficient and easy to understand.

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 (validation with 2 parameters), no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., validation results, errors), behavioral traits, or usage context. This leaves gaps for an AI agent to understand how to interpret results or handle failures.

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%, with clear descriptions for both parameters: 'componentName' and 'propsToCheck.' The description adds minimal value beyond the schema by implying the validation context ('@props observations'), but doesn't provide additional syntax, format details, or examples. 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 tool's purpose: 'Validate component props to prevent hallucination. Checks if props exist in @props observations.' It specifies the verb 'validate' and the resource 'component props,' with a clear goal of preventing hallucination. However, it doesn't explicitly differentiate from sibling tools like 'verify_graph_integrity' or 'search_nodes,' which might involve validation or checking, so it lacks sibling distinction.

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 mentions checking props in '@props observations,' but doesn't specify context, prerequisites, or exclusions. For example, it doesn't clarify if this should be used before creating entities or as a standalone check, leaving usage ambiguous with siblings like 'verify_graph_integrity.'

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