Enhanced Knowledge Graph Memory 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 action 'find' but does not describe what the tool returns (e.g., list of components, their sizes, or graph structure), performance implications, or any side effects. This is inadequate for a 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 a single, clear sentence with no wasted words. It is front-loaded with the core purpose and efficiently communicates the tool's function without unnecessary details, making it highly concise and well-structured.

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 of graph operations and the lack of annotations and output schema, the description is incomplete. It does not explain what 'connected components' means in this context, the format of the output, or any behavioral traits like computational cost or data returned, leaving significant gaps for an AI agent to understand and use the tool effectively.

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?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description does not mention any parameters, which is appropriate and avoids redundancy. However, it does not add any semantic context beyond the schema, such as default behaviors or implicit inputs, keeping it at a baseline level.

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 'find' and the resource 'connected components in the knowledge graph', which is specific and unambiguous. However, it does not explicitly distinguish this tool from sibling tools like 'find_shortest_path' or 'find_all_paths', which also operate on graph connectivity, so it misses 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. With sibling tools like 'find_shortest_path' and 'find_all_paths' that involve graph traversal, there is no indication of the specific context or criteria for choosing 'get_connected_components' over them, leaving usage unclear.

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