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. It mentions the normalization actions but doesn't disclose behavioral traits such as whether this is a read-only or mutating operation (the 'persist' parameter suggests potential storage changes), performance implications, or error handling. The description is too brief to cover these aspects adequately for a tool with parameters.

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 highly concise and front-loaded: two sentences that directly state the purpose and benefit. There is no wasted language, and it efficiently communicates the core functionality without unnecessary details.

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 (3 parameters, nested objects, no annotations, no output schema), the description is incomplete. It lacks details on what 'normalize' entails operationally, how results are returned, or the impact of the 'persist' parameter. For a tool that might modify data, more context is needed to guide safe and 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 67% (2 out of 3 parameters have descriptions). The description adds no additional parameter semantics beyond what's in the schema—it doesn't explain the 'entityName' parameter's role in normalization or the 'options' sub-parameters' effects. With moderate schema coverage, the baseline is 3, as the description doesn't compensate for the gaps.

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: 'Normalize entity observations by resolving pronouns and anchoring relative dates.' It specifies the action (normalize), the target (entity observations), and the methods (resolving pronouns, anchoring dates). However, it doesn't explicitly differentiate this from sibling tools like 'analyze_query' or 'smart_search' which might also process text, though the normalization focus is reasonably distinct.

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 minimal guidance: 'Improves search matching quality' implies it should be used to enhance search results, but it doesn't specify when to use this tool versus alternatives like 'analyze_query' or 'semantic_search', nor does it mention prerequisites or exclusions. No explicit when/when-not instructions are given.

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