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Cubite LMS: Learning Management System

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Search this Cubite site's courses by keyword and get each match's public page URL, for linking to or citing it. This is the search half of the search/fetch pair that ChatGPT connectors and deep research require. The whole query is matched as one case-insensitive phrase, so use one or two distinctive words. Courses match on name or description and are listed in name order; a course without a URL slug has no public page and is never returned, while courses hidden from the catalog are. Returns up to 10 results as {id, title, url}; pass a result's id to fetch for its citable text. It is not an inventory: to list, count or manage courses use course_list, which has no keyword filter but returns every course, with or without a slug, with its id, settings and enrollment count.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesWord or short phrase to find in titles and descriptions, matched as one case-insensitive substring (max 200 characters; longer input is cut).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior4/5

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

Annotations cover the safety profile (readOnlyHint=true, openWorldHint=false), and the description adds substantial behavior beyond them: max 10 results, results ordered by course name, slug-less courses are never returned, catalog-hidden courses still are, returns {id, title, url}, and the query is truncated at 200 characters. It does not mention pagination or ranking nuances, so it falls just short of the top.

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?

Front-loaded with the core action and return shape before constraints and the sibling contrast. It is dense and nearly every sentence carries information, though the length is on the higher side for a single-parameter tool.

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

Completeness5/5

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

Output schema exists, so return values needn't be re-explained, yet the description still signals the {id, title, url} shape and the fetch handoff. Combined with coverage of filtering quirks, result cap and ordering, an agent has everything needed to call this correctly.

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%, so the baseline is 3, but the description adds practical meaning: the whole query is matched as one case-insensitive phrase (so multi-word intent is constrained) and recommends one or two distinctive words. That is real guidance beyond the raw schema string.

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?

States a specific verb (search) and resource (this site's courses) plus the scope (keyword match). It explicitly distinguishes itself from siblings course_list ('not an inventory') and fetch ('the search half of the search/fetch pair'), so an agent can route correctly without opening other schemas.

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

Usage Guidelines5/5

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

Gives explicit when-to-use (pair with fetch for citable text, use for linking/citing) and when-not (listing, counting or managing courses belongs to course_list). It also advises query shaping ('use one or two distinctive words') and notes the keyword-less alternative's tradeoff.

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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