memory_ingest

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

With no annotations provided, the description fully discloses behavioral aspects: it performs multiple steps (splitting, metadata generation, writing to .md and index, linking), is model-agnostic, and falls back to a heuristic when no LLM API is configured. This is comprehensive and avoids surprises.

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 two concise sentences that front-load the purpose and cover key aspects without redundancy. Every sentence adds value, including fallback behavior and model-agnostic property.

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?

The description thoroughly covers input handling and internal behavior but omits any mention of return values or output format. Given the absence of an output schema, the agent is left without information on what the tool returns, which is a minor gap.

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 input schema covers 100% of parameters with descriptions, so the description does not need to add parameter details. It provides overall pipeline context but no additional parameter-level semantics beyond what the schema offers, meeting the baseline expectation.

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 function: accepting raw text and executing a full pipeline to split into event packets, generate metadata, write to authoritative source with indexing, and establish links. It distinguishes from sibling tools like memory_write (which likely writes a single packet) and memory_link (which creates associations) by describing a more comprehensive ingestion process.

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 implicitly indicates usage for ingesting raw text into the memory system, but does not explicitly state when to use this over alternatives or provide exclusion criteria. The context from sibling tools makes it clear this is for initial ingestion versus querying or linking, but explicit guidance is missing.

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