Fetch a single class journal entry with its items and practice log. Only returns it if it belongs to your account (mcp_token).
Args:
class_id: The class to fetch.
mcp_token: Your personal VenuVidya token — GET /api/auth/mcp-token.
| Name | Required | Description | Default |
|---|---|---|---|
| class_id | Yes | ||
| mcp_token | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It explains that the tool fetches a specific entry with items and practice log, and importantly discloses the ownership check (only returns if it belongs to your account) and the auth token requirement (mcp_token). It does not mention error handling or response format, but as a read operation the verb 'fetch' implies non-mutating. The ownership constraint is a meaningful addition beyond the schema.
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 extremely concise: a two-sentence purpose statement plus a two-line Args list. The main purpose and key constraint are front-loaded, and every sentence adds value. There is zero fluff.
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 fetch tool, the description covers the essential aspects: what it fetches (single class journal entry with items and practice log), the ownership constraint, and the argument meanings. It does not detail the exact response fields, but with no output schema, a little more on the returned structure could be helpful. However, the tool's simplicity and the described content make it adequate. No crucial information for correct invocation is missing.
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?
Despite 0% schema description coverage, the description fully explains both parameters in the Args section: class_id is 'The class to fetch' and mcp_token is 'Your personal VenuVidya token — GET /api/auth/mcp-token', including how to obtain it. This adds significant meaning beyond the schema's type-only definitions and completely compensates for the low schema coverage.
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 states a specific verb ('fetch') and a specific resource ('a single class journal entry') with its associated items and practice log. This clearly distinguishes it from siblings like list_classes (which lists many) and get_lesson (which fetches a different resource type).
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 implies usage for retrieving a single class journal entry but does not explicitly state when to use this tool versus alternatives like get_lesson or list_classes. It provides the ownership condition ('Only returns it if it belongs to your account') but no explicit when-not-to-use or alternative routing. This leaves some inference required.
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.
Each tool has a clearly distinct purpose: ask_venuvidya is a RAG chatbot, search_syllabus is keyword search, list/get_lesson and list/get_class handle distinct resources, and log_practice_session writes practice data. No two tools have overlapping responsibilities.
All tools follow a consistent verb_noun pattern in snake_case (ask, get, list, log, search). Even 'ask_venuvidya' uses a verb + proper noun, matching the style. Naming is predictable and scannable.
Seven tools cover the core flows of a study guide: browsing lessons, searching content, logging practice, and retrieving class journals. This is well-scoped—not too sparse, not bloated.
The surface covers viewing lessons and classes, searching, and logging practice, but lacks write operations for lessons or classes (e.g., annotating or updating). However, the server's role is a study guide, and the missing operations are not essential for its stated purpose.