Search the Legends of Learning K-8 games catalog and standards alignment
Scored across 3 tools
Each tool has a distinct purpose: browsing standards, searching games, and fetching game details. No overlap or ambiguity in their functionality.
All tools follow the 'legends_' prefix with a consistent 'verb_noun' naming pattern (browse_standards, get_game, search_games), making it predictable.
With only 3 tools, the set is minimal but sufficient for the server's purpose of accessing the public Legends of Learning catalog. It could benefit from a few more, but it's reasonable.
The tools cover the core workflows: exploring standards, searching for games, and retrieving detailed game info. No obvious gaps for a read-only public API.
Browses the public Legends of Learning curriculum taxonomy.
Without standard_set, lists the public standard sets (e.g. id "NGSS" for Next Generation Science Standards, "CCSS" for Common Core math), each with id, name, and subject_area; query filters them by name.
With standard_set (a set id from the list above), lists the standards in that set. Each standard has id, code (e.g. "2.OA.A.1" or an NGSS code), title, description, grades ("k" and "1"-"12"), and subject. Use a standard's id in legends_search_games' learning_objective_ids to find games aligned to it. query free-text matches code, title, and description; grade (integer, 0 = kindergarten) narrows to one grade level.
page (default 1) and page_size (default 10, max 25) paginate both modes.
Example: {"standard_set": "CCSS", "grade": 2, "query": "addition"} finds 2nd-grade Common Core standards about addition.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | ||
| grade | No | Only standards for this grade level; 0 means kindergarten | |
| query | No | Free-text filter (set names, or standard codes/titles/descriptions) | |
| page_size | No | ||
| standard_set | No | A standard set id (e.g. "NGSS", "CCSS"); omit to list the sets |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint: true, so the description adds value by detailing pagination, the two-mode behavior, filtering logic, and the fields returned. It does not mention error handling for invalid inputs, but overall provides substantial behavioral context beyond annotations.
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 well-structured with clear paragraphs, an example at the end, and no superfluous sentences. Each sentence adds value, and the information is front-loaded with the main purpose.
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?
Despite lacking an output schema, the description thoroughly covers the tool's behavior: two modes, pagination, filtering, and the data fields in results. The example further clarifies usage. This is complete for an agent to understand and use the tool correctly.
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 description adds meaning beyond the input schema by explaining the two-mode use of standard_set, clarifying grade as 0 for kindergarten, and specifying that query matches code, title, and description. It also provides defaults and max for page and page_size. With 60% schema coverage, the description compensates well.
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 it browses the public Legends of Learning curriculum taxonomy, with two distinct modes (list standard sets or list standards within a set). It distinguishes from sibling tools (legends_get_game, legends_search_games) that focus on games, not curriculum taxonomy.
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 provides clear guidance on when to use each mode (with or without standard_set) and explains how the query and grade parameters work. It also hints at integration with legends_search_games by mentioning using standard ids. However, it does not explicitly state when not to use this tool or compare it directly to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetches the full public detail for one Legends of Learning game by its numeric id (from legends_search_games).
Returns the game's id, title, description, image, estimated_duration in minutes, type, grades served, teacher/student ratings, instructions, vocabulary, discussion questions, capability flags (supports_ipad, supports_tts, multi_language, saves_progress), lexile_level, the public url, and learning_objectives — the standards the game is aligned to, each with id, title, code, and standard_set.
Example: {"game_id": 1445} returns that game, or an error result if no such game is publicly available.
| Name | Required | Description | Default |
|---|---|---|---|
| game_id | Yes | Numeric game id, e.g. from legends_search_games results |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true. The description adds behavioral context: it returns full public detail, specifies error handling (if no game is publicly available), and provides an example. No contradiction.
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?
Three concise sentences: purpose, full return fields list, and example. No wasted words. Front-loaded with purpose.
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?
No output schema exists, so the description compensates by listing all expected fields. Includes error case and relation to sibling tool. Complete for a lookup 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?
The parameter is well-described in schema (100% coverage). The description reinforces the source of the id and provides an example JSON, adding value beyond the schema.
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 it fetches full public detail for a game by numeric id, distinguishing it from sibling tools. It specifically mentions the id source (legends_search_games) and lists all returned fields.
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?
It provides context that the game_id comes from legends_search_games, implying prior use of that tool. It does not explicitly state when not to use, but the linking is clear enough for typical usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Searches the public Legends of Learning game catalog (curriculum-aligned learning games for grades K-12 in math, science, and social studies).
Arguments (all optional): query free-text matches game titles and vocabulary; grades narrows to games for those grade levels (integers, 0 = kindergarten through 12); game_type is one of "instructional", "question", or "simulation"; learning_objective_ids narrows to games aligned to those learning objectives (get ids from legends_browse_standards); page (default 1) and page_size (default 10, max 25) paginate.
Returns games (each with id, title, description, image, estimated_duration in minutes, type, grades served, teacher/student ratings, and the game's public url) plus page, page_size, total_entries, and total_pages.
Example: {"query": "fractions", "grades": [3, 4], "page_size": 5} finds up to five 3rd-4th grade games about fractions.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | ||
| query | No | Free-text search over game titles and vocabulary | |
| grades | No | Grade levels to include; 0 means kindergarten | |
| game_type | No | ||
| page_size | No | ||
| learning_objective_ids | No | Learning objective ids from legends_browse_standards |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations include readOnlyHint: true, confirming read-only behavior. The description adds value by explaining the search behavior, pagination, and available filters, plus an example. No contradictions.
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 well-structured with a clear purpose, parameter list, return fields, and example. It is front-loaded but slightly verbose in places (e.g., repeating schema descriptions). Still concise enough for effective use.
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?
Given the tool has 6 optional parameters and no output schema, the description covers all parameters, return object fields, pagination details, and provides an example. This is complete for an agent to understand input and output structure without ambiguity.
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 50% (3 of 6 parameters have descriptions). The description compensates by explaining all parameters in detail, including defaults, constraints, and an example. This adds significant meaning beyond what the schema provides.
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 searches the public Legends of Learning game catalog, specifying it is for curriculum-aligned K-12 games in math, science, and social studies. This distinguishes it from sibling tools: legends_browse_standards browses standards and legends_get_game retrieves a specific game, so the search function is distinct.
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 provides detailed parameter explanations and an example, implying appropriate usage. However, it does not explicitly state when not to use this tool or mention alternative tools for different needs, such as using legends_get_game for a known game or legends_browse_standards for learning objectives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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