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 'calculate centrality metrics' but doesn't mention whether this is a read-only operation, if it requires specific permissions, potential performance impacts, or what the output looks like (e.g., format, size). For a tool with 6 parameters and no annotations, this is a significant gap in transparency.

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, efficient sentence that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy to parse quickly.

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 a 6-parameter tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., read/write nature, performance), usage context, and output format, which are crucial for an AI agent to invoke it correctly in a graph analysis environment.

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 100% description coverage, with each parameter well-documented (e.g., algorithm choices, direction options, defaults). The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline of 3 for high schema coverage without compensating value.

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 action ('calculate') and the resource ('centrality metrics for entities in the graph'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'get_graph_stats' or 'analyze_query', which might also involve graph analysis, so it misses full 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. With sibling tools like 'get_graph_stats' and 'analyze_query' that might overlap in graph analysis, there's no explicit context, exclusions, or named alternatives mentioned, leaving usage unclear.

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