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ClassMarker MCP by usefulapi

Recent results for one group exam

classmarker_recent_group_exam_results
Read-only

Recent finished results for ONE exam in ONE group (ids from classmarker_list_groups_links_exams), oldest first, PAGED by finish time. Contains personal data: use small pages. Counts as one of ClassMarker's 30 requests per hour per API key. API: GET /v1/groups/{group_id}/tests/{test_id}/recent_results.json.

Input Schema

NameRequiredDescriptionDefault
limitNoResults per request, 1-200 (default 25; ClassMarker's own default is 200).
test_idYesThe exam (test) id.
group_idYesThe group id.
include_monitor_eventsNoInclude each result's detailed monitor_events.events list (default false: only the counts).
finished_after_timestampNoUNIX time in SECONDS: only results finished after this are returned (oldest first). Default: 14 days ago. Maximum look-back is 3 months. To page, pass next_finished_after_timestamp from the previous reply.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior5/5

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

Goes well beyond the readOnlyHint annotation by disclosing ordering ('oldest first'), pagination mechanism ('PAGED by finish time'), a personal-data warning, and a concrete rate limit (30 requests/hour per API key). These are exactly the operational facts an agent needs and none are derivable from the annotations or schema.

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 purpose and scope, followed by constraints and the API path. It is dense but every clause (paging, personal-data caution, rate limit, id source) carries real information; only minor tightening is possible.

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 read-only, 5-parameter tool with no output schema, the description covers scope, ordering, paging, rate limit, and privacy. It omits any indication of the response shape, but with no output schema and readOnlyHint present, this is a minor gap rather than a blocking one.

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

Parameters3/5

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

Schema description coverage is 100%, so all five parameters are already documented, including the finished_after_timestamp paging hint. The description reinforces 'oldest first' ordering and 'small pages' but adds no syntax or format detail beyond what the schema already provides, making the 3 baseline appropriate.

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

Purpose4/5

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

States a specific verb and resource with precise scope: 'Recent finished results for ONE exam in ONE group,' which implicitly separates it from group-only and link-based siblings like classmarker_recent_group_results and classmarker_recent_link_exam_results. It stops short of naming a contrasting sibling explicitly, so the differentiation relies on the reader comparing scope statements.

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?

Tells the agent where the required ids come from (classmarker_list_groups_links_exams) and advises using small pages because output contains personal data. It does not state explicit when-not conditions or name competing result tools, but the practical usage context is clear.

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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