Approve a pending memory in the Inbox and begin its ingestion process.
Args:
memory_id: The ID of the memory (e.g., 'mem_123').
| Name | Required | Description | Default |
|---|---|---|---|
| memory_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It does disclose a key side effect ('begin its ingestion process') and a prerequisite (the memory must be pending in the Inbox), but it does not mention whether the action is irreversible, whether it removes the memory from the Inbox, or whether approval triggers asynchronous processing.
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, with the primary action stated first and the parameter explanation kept minimal. No word is wasted.
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 tool is simple (one required parameter), an output schema exists, and the description explains the state prerequisite and action. It misses only minor contextual details like how to obtain memory IDs or what an already-approved memory should do, which are not critical for a first correct call.
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 schema provides only the parameter name and type, so schema description coverage is 0%. The description compensates by clearly defining memory_id as the ID of the memory and giving a concrete format example ('mem_123'), which gives the agent enough to construct a valid call.
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 uses a specific verb 'approve' on a clearly identified resource ('pending memory in the Inbox') and states the follow-on effect ('begin its ingestion process'). This distinguishes it from siblings like discard_memory or delete_memory, which handle memories differently.
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 intended context is clear: it is for pending memories in the Inbox that should be accepted. However, it never explicitly contrasts with alternative tools such as discard_memory (the reject path) or update_memory, so the agent must infer when this tool is the right choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most tools map cleanly to distinct operations such as semantic search, date search, inbox approval/discard, and tag listing. The main ambiguity is between save_memory and upsert_memory, both of which can create memories with similar arguments; get_memory/get_document and delete_memory/discard_memory are also distinguishable mainly by ID prefix or inbox state.
The names are consistently lowercase snake_case and verb-first, which makes the set fairly predictable. Minor deviations include search_by_date breaking the verb_noun pattern, get_inbox/get_all_tags acting as list operations while list_memories uses 'list', and upsert_memory being an unusual verb.
At 13 tools, the surface is well-scoped for a memory/document vault: create, upsert, update, retrieve, list, semantic and date search, tags, and inbox workflow are all represented without excessive granularity or redundant filler.
The toolset covers the core memory lifecycle well, including save, upsert, update, get, search, list, delete, and inbox approval/discard. Minor gaps remain: update_memory only accepts mem_ IDs, so documents found via search have no document-specific update path, and workspace management is only handled through optional name strings.