DeepMark

Get Submission Grading Result

submission_get

Read-onlyIdempotent

Read the grading state and per-question marks for a submission (created via submission_mark). Returns each question's marking_status, awarded/max marks, level, and feedback, plus an overall status ('grading' until every question is marked). Poll after submission_mark.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`submission_id`	Yes	ID of the submission to read grading results for.

Output Schema

TableJSON Schema

Name	Required	Description
`status`	Yes	Overall grading status.
`questions`	Yes
`total_max`	Yes	Total marks available, null until grading is complete.
`student_name`	Yes	Detected student name, or null if none was read.
`exam_paper_id`	Yes
`grading_error`	Yes	Failure detail when status is failed.
`submission_id`	Yes
`total_awarded`	Yes	Total marks awarded, null until grading is complete.
`questions_marked`	Yes	Progress as "marked/total", e.g. "3/6".

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, and the description adds behavioral details: it reveals the polling nature ('Poll after submission_mark') and explains the overall status transition ('grading' until every question is marked), which goes beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the core action, and no wasted words. The first sentence explains what is returned, and the second gives usage guidance. Every word earns its place.

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?

For a read-only, single-parameter tool with annotations and a detailed description of return values, this is complete. The description mentions each question's marking_status, marks, level, feedback, and overall status, which is sufficient for an agent to understand the call and interpret results.

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?

The sole parameter submission_id is fully described in the schema (100% coverage), so the baseline is 3. The description adds value by linking the ID to a submission created via submission_mark, giving the agent practical context about where the ID comes from.

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?

The description opens with 'Read the grading state and per-question marks for a submission', giving a specific verb ('read') and resource (submission grading result). It clearly distinguishes itself from sibling tools like submission_mark (which creates) and markResult_get (which may read a different result).

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?

The description states the tool is for submissions 'created via submission_mark' and instructs to 'Poll after submission_mark', providing a clear when-to-use context. It does not explicitly discuss alternatives or exclusions, but the relation to submission_mark effectively frames its intended use.

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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TDQS

A4/5.0

Disambiguation4/5

The tools map well to distinct resources and workflow stages, and the async flows are clearly explained. The main overlap risk is paper_create/paper_confirm and markResult_get/submission_get, where names point at similar actions but the descriptions make the boundaries usable.

Naming Consistency4/5

Most names follow the same resource_action snake_case pattern, e.g. paper_get, submission_mark, markScheme_update. markResult_get breaks the pattern slightly by mixing camelCase into an otherwise snake_case set, but the naming stays readable and predictable overall.

Tool Count5/5

Ten tools is a well-scoped size for this domain: paper creation, confirmation, retrieval, question/mark-scheme correction, and submission grading are covered without excess granularity or obvious filler.

Completeness4/5

The main end-to-end workflow—create extraction job, confirm paper, mark submission, poll results—is complete and coherent. Minor gaps exist, such as no paper deletion, no submission listing, and no direct way to fetch a mark scheme separately, but they are not blocking for the core grading loop.

Resources

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