mcp_engram_tensor_recall

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

With no annotations provided, the description fully covers behavior: two modes, caps (12 entries/32 edges with truncation flag), 1-hop bond expansion limited to tensor:/design:, and the prerequisite for semantic mode. It does not mention authentication, rate limits, or side effects, but as a recall tool it is likely read-only.

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 a single compact paragraph that front-loads the core purpose and then systematically details modes, caps, and deprecated parameters. It is not overly verbose, but could benefit from bullet points or clearer separation of concepts for faster scanning.

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 (two modes, caps, bond expansion) and absence of an output schema, the description covers core behavior but omits return format, error handling, and precise behavior when truncated. This leaves gaps for an agent to fully understand the call's outcome.

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?

All 5 parameters have schema descriptions (100% coverage), but the description adds significant value: it explains how 'query' behaves in pin vs semantic mode, that 'seed_concept' forces inclusion, and that 'scope' and 'include_presentation' are deprecated and ignored. This goes beyond the schema to clarify semantics.

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 is a 'Solid-State Tensor — addressable working memory for agents' and distinguishes between pin mode (direct fetch by name) and semantic mode (BVH over tensor:/design:). It uniquely differentiates from sibling tools like mcp_engram_recall by specifying tensor-specific operations.

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 defines when to use pin mode (query with tensor:/design: name) vs semantic mode (natural language, requires nvme_recall_ready via get_backend_readiness). However, it does not provide explicit exclusion criteria like 'do not use for non-tensor recall' or compare directly to other recall tools.

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