Calculate per-tag value rollups (count, total, average) for cohorts, aggregating across tag/time-block groups to filter out single-memory noise.
Per-tag value rollup (count / total value / average). Reported at the cohort level on purpose: at n-of-1 a single memory's value is noise; the tag/time-block is where the signal is real.
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Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behavioral traits: it performs aggregations (count, total, average) and emphasizes cohort-level results. With no annotations, this provides sufficient transparency about the tool's nature and limitations.
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 with two focused sentences. The first sentence front-loads the core purpose, and the second adds valuable context. Every word contributes to clarity.
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 has no parameters and no output schema, the description fully conveys what the tool does (rollup statistics) and why it exists (noise reduction). It is complete for a parameterless aggregation tool.
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?
There are zero parameters, and the schema description coverage is 100%. The description adds significant meaning by explaining why there are no parameters (fixed aggregation) and what the output represents, surpassing the baseline of 4.
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 performs per-tag value rollups including count, total value, and average. It distinguishes from siblings by focusing on aggregation at cohort level, unlike tools like recall which likely retrieve individual memories.
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 explicitly explains why the tool is designed for cohort-level reporting and warns against using it for individual memories ('at n-of-1 a single memory's value is noise'). This provides clear context for appropriate usage, though it does not explicitly name alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.