get_cardSingle card detail by word (case-insensitive). Returns translation, mastery, and last 10 events.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | Language tag of the word: base ISO with an optional region (en, en-us, pt-br). Omit it and the user's own primary learning language is used. | |
| word | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / lang / descriptionAdded value: +"Language tag of the word: base ISO with an optional region (en, en-us, pt-br). Omit it and the user's own primary learning language is used."Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description adds useful behavioral details beyond that: lookups are case-insensitive and the result includes translation, mastery, and the last 10 events. Since there is no output schema, these return details meaningfully set expectations. It does not cover not-found behavior, but enough is disclosed.
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?
Two short sentences deliver the purpose, lookup behavior, and expected return fields with no filler or redundancy. Every phrase in the description adds useful information.
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?
For a simple read-only single-card lookup with two parameters and no output schema, this description is mostly complete: it identifies the key parameter, mentions case-insensitivity, and enumerates return contents. It could add a short note on not-found behavior or a clearer 'use this when' hint, but the current level is solid for this low-complexity tool.
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 coverage is 50%; the schema documents 'lang' thoroughly, while 'word' only has length constraints. The description clarifies that 'word' is the lookup key and case-insensitive, adding some meaning beyond the schema. However, it does not fully compensate for all param semantics and does not mention lang behavior beyond what the schema already says.
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 identifies the operation as 'Get single card detail by word', with a specific resource (card), a specific lookup key (word), and explicit scope (a single card). This distinguishes it from sibling list-oriented tools like get_known_words and get_active_vocab.
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 usage context is implied: use this tool when you need the detail of one card by its word. However, it does not explicitly say when not to use it, mention prerequisites, or contrast with sibling tools such as check_words or get_known_words. Given the context is clear but exclusions are absent, an implied-usage score is appropriate.
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.