mcp_engram_query_pure

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

With no annotations, the description fully discloses behavioral traits: it is pure geometric (no fallback), intent-only, searches high-priority/hot blocks or BVH, and returns ranked concepts with scores and CRS. It also notes it is a fast path for hot ritual rehydrate, providing complete 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 dense but well-structured: first sentence defines the type, second explains the mechanism, third provides usage context. Some jargon (e.g., 'p-blend', 'BVH', 'CRS') may reduce accessibility, but every sentence adds specific value.

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 moderate complexity, lack of output schema, and no annotations, the description is remarkably complete. It covers the tool's mechanism, use case, fallback avoidance, return type, and performance context (fast path for hot rehydrate), fully enabling correct selection and invocation.

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 coverage is 100%, but the description adds value by explaining that 'intent' is natural language to encode as a phase vector and that 'k' controls max results (default 6, max 20). It also clarifies the output context (ranked concepts + scores + CRS), exceeding baseline 3.

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 identifies the tool as a pure geometric K-NN discovery, explicitly distinguishing it from hybrid approaches (no keyword/file-path fallback). It states the verb 'discover' and resource 'concepts via geometric search', and contrasts with sibling tools like mcp_engram_query_with_momentum and mcp_engram_search_by_relation.

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 specifies the tool is used for fast anchor discovery in optimized wake-up and that it replaces broader tools like list_concepts and search_by_relation for ritual/trace/goal contexts. However, it does not explicitly state when not to use it or provide exclusion criteria, leaving some ambiguity.

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