mcp_engram_query_with_momentum

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It explains the internal mechanism (q and p tensors, 80/20 blend) and how momentum detects evolving concepts. While it doesn't mention side effects or auth needs, it adequately discloses the behavioral trait of prioritizing dynamic versus static knowledge.

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 detailed but each sentence adds value. It is structured with a clear 'WHEN TO USE' section. Slightly lengthy but not wasteful.

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 simple parameters and no output schema, the description is complete: explains purpose, usage context, and internal mechanism. No gaps.

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?

Both parameters have descriptions in the schema already (100% coverage). The description adds no extra meaning beyond the schema; it mentions 'natural language query' which is consistent. Baseline 3 is appropriate.

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 it performs momentum-assisted recall that blends semantic similarity and conceptual trajectory, distinguishing it from the sibling 'recall' tool. It specifies the tensor components and their weights.

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?

Explicitly provides when to use: 'WHEN TO USE INSTEAD OF recall' with an example query. Also advises when to use regular recall for stable knowledge. This is exemplary guidance.

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