log_interactionAppend a generic interaction event to the answers log. Use for lookups, AI discussions, and reading-in-context signals.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | Language tag of the word: base ISO with an optional region (en, en-us, pt-br). Scoped on the base, so the variant the session instructions ask you to send always matches. Omit it and the user's own primary learning language is used. | |
| type | Yes | ||
| word | Yes | ||
| metadata | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate a non-read-only, non-destructive operation (readOnlyHint=false, destructiveHint=false). The description adds that it 'appends' a 'generic interaction event', which implies non-destructive append semantics. However, it does not disclose any further behavioral traits like side effects, permission requirements, or log size constraints. With annotations covering the safety profile, this is sufficient but not rich.
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, well-structured sentence that front-loads the core action and then lists concrete use cases. No wasted words; every part contributes to understanding.
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 (4 params, no output schema), and the description covers purpose and usage adequately. However, since parameter semantics are only partially covered and there is no output description, the description is incomplete for fully understanding behavior. It is minimally viable but could benefit from parameter examples or a note about what the log entry contains.
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 description coverage is only 25% (only 'lang' has a description). The description does not explain the 'type', 'word', or 'metadata' parameters, and the enum values for 'type' are not elaborated. Since the schema only clarifies 'lang', the description should compensate for the other parameters but fails to do so, leaving semantic gaps.
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?
Clearly states the verb 'Append' and the resource 'answers log', and specifies the three interaction domains (lookups, AI discussions, reading-in-context). This distinguishes it from sibling tools like capture_word or mark_demonstrated, which are about capturing or marking specific states.
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 states when to use it ('for lookups, AI discussions, and reading-in-context signals'), giving clear contexts. It does not enumerate when-not-to-use or alternatives, but the listed use cases are enough to guide selection relative to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools cleanly separate single-card lookup, batch lookup, due queue, event log, and SRS adjustments. The main overlap is `get_system_instructions` and `get_user_profile`, which both return CEFR level, languages, due count, and weak words, so an agent could mis-select between them.
Every tool follows a consistent lowercase snake_case verb_noun pattern: capture_*, get_*, mark_*, log_*, check_. The verbs are descriptive and predictable, making the set easy to navigate.
13 tools is well within the ideal scope for a language-learning memory/assistant server. Each tool covers a distinct part of the capture, lookup, review, and spaced-repetition workflow without feeling padded.
The core lifecycle is well covered: grammar and vocabulary capture, batch deck checks, card detail, due queue, recent activity, and SRS boosts/penalties. Minor gaps exist: captured grammar mistakes have no retrieval endpoint, and there is no delete/dismiss path for unwanted cards, but agents can work around these.