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Capture word

capture_word

Capture a target-language word or phrase to the user's Minddory vocabulary deck (a flashcard in the "Chat Discoveries" folder when the word is new, otherwise a context encounter). The user connected Minddory so their assistant builds their deck from real conversations, so use this proactively to log notable target-language vocabulary they encounter, use, or ask about. By connecting Minddory the user opted IN to automatic capture, so just capture as you chat - do not pause to ask permission for each word, and do not turn capturing into a yes/no question. You do not need to announce each capture, but answer honestly if the user asks what you have saved. Use the words array to capture several from one message; include a gloss (short source-language translation) and context (the sentence) when you can. Skip stop words, proper nouns, numbers, and words shorter than 3 characters (CJK / Hangul exempt). ALWAYS pass lang — the ISO code of the captured word's own language (you know it from the conversation); the call is rejected without it and the target language is never guessed. source_lang (the user's native language) is optional and defaults to their deck. This is the primary tracking signal of Minddory, so capture diligently.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoREQUIRED. ISO code of the captured word's own language (en, de, es, zh, ...). The call is rejected if omitted — never leave it blank or guess from prior context.
wordNoSingle-word capture (legacy). Use `words` for more than one.
glossNoShort translation in the source language. Strongly recommended.
wordsNoBatch capture. Each entry is {word, gloss?, context?}. Prefer this when capturing 2+ words from the same message.
contextNoThe sentence where the word appeared.
source_langNoISO code of the user's native/source language. Optional — defaults to the source language of the user's existing deck.

TDQS

A4.3/5.0
Behavior4/5

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

Annotations only indicate the tool is a write operation (readOnlyHint: false, destructiveHint: false). The description adds valuable behavioral context: the call is rejected without `lang`, the user has opted into automatic capture, no confirmation is needed, captures happen silently, and honesty is required if asked. This goes well beyond the annotations and sets clear expectations for agent behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but each sentence adds functional value: purpose, user consent, usage policy, batching, language requirements, and capture heuristics. It is front-loaded with the action and destination, then branches into practical rules. While it could be trimmed slightly (e.g., repeating 'capture diligently'), the density is justified by the tool's central role.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a write tool with no output schema, the description covers the essential operational context: when to use, what to pass, how to structure multiple captures, and critical failure conditions. It does not describe return values or post-capture behavior, but that is not required for a capture action. The main omission is any note about idempotency or capacity limits, but overall it is sufficiently complete for reliable invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description enriches it: it flags `lang` as mandatory (schema lacks a required list), explains the legacy `word` vs. batch `words` distinction, clarifies `gloss` and `context` usage, and notes `source_lang` defaults to the deck's language. This adds meaning beyond the raw property descriptions, such as the rejection behavior and batching preference.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb+resource: 'Capture a target-language word or phrase to the user's Minddory vocabulary deck,' and clearly distinguishes its scope as the primary word-capture tool, contrasted with siblings like capture_grammar_mistake. It states the exact action, destination, and context (flashcard vs. context encounter), making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidance: use proactively, don't pause for permission, don't announce every capture, batch with `words`, skip stop words/proper nouns/numbers/short words, and always pass `lang`. It does not explicitly compare against alternative capture tools (e.g., capture_grammar_mistake), but the context 'primary tracking signal of Minddory' implies precedence. Overall, strong guidance but no explicit exclusions for alternatives.

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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TDQS

A4.1/5.0
Disambiguation4/5

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.

Naming Consistency5/5

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.

Tool Count5/5

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.

Completeness4/5

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.