knowledge_stats

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 describes what data is retrieved (statistics like total memories, category breakdown) but lacks details on permissions required, rate limits, response format, or potential side effects. For a read-only tool with zero annotation coverage, this leaves significant gaps in understanding its operational 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 concise and front-loaded, with two sentences that efficiently convey the tool's purpose and scope. Every sentence adds value: the first lists specific statistics, and the second provides context as a dashboard overview. There is no redundant or extraneous information.

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 (a read-only statistical summary with 0 parameters), the description is reasonably complete. It outlines the types of statistics returned, and since an output schema exists, it does not need to detail return values. However, without annotations, it could benefit from more behavioral context, such as data freshness or access constraints, to fully inform usage.

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 tool has 0 parameters with 100% schema description coverage, so the schema fully documents the inputs. The description does not need to add parameter semantics, and it appropriately avoids discussing parameters, focusing instead on the output scope. This meets the baseline expectation for a parameterless tool.

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 with a specific verb ('Get') and resource ('knowledge statistics'), listing concrete metrics like total memories, category breakdown, tag distribution, growth by month, and date range. However, it does not explicitly distinguish this from sibling tools like 'provenance_stats' or 'memory_health', which might also provide statistical insights, leaving some ambiguity in 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 mentions 'A dashboard overview of everything Tessera knows,' which implies a broad summary context, but does not specify prerequisites, exclusions, or compare it to other tools like 'provenance_stats' or 'search_analytics' that might offer overlapping or complementary functionality.

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