DeepMark

Mark a Student Submission from Text

submission_mark

Mark a student's typed answers against an existing exam paper. Provide the exam_paper_id and per-question answers keyed by question_number. DeepMark creates a submission and grades it through its canonical marker (per mark point / level descriptor) — results stream into the teacher's editor live. Grading is asynchronous: this returns a submission_id; poll submission_get for the per-question grades.

Input Schema

TableJSON Schema

Name	Required	Description
`answers`	Yes	One entry per answered question, keyed to its question by number. DeepMark grades each answer against that question's mark scheme via the canonical marker.
`student_name`	No	The student's name, surfaced in results. Optional.
`exam_paper_id`	Yes	The exam paper to mark these answers against.

Output Schema

TableJSON Schema

Name	Required	Description
`poll_with`	Yes	The tool to poll this submission with (submission_get).
`exam_paper_id`	Yes
`submission_id`	Yes
`answers_submitted`	Yes	How many answers matched a question and were queued for grading.

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the annotations (which only indicate it's not read-only), the description discloses that DeepMark creates a submission, grades via the canonical marker (per mark point/level descriptor), streams results live to the teacher's editor, and is asynchronous, returning a submission_id. This is substantial behavioral context that aids the agent in understanding side effects and expected flow.

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?

The description is three sentences, each serving a purpose: stating the action, specifying required inputs, and explaining the asynchronous outcome. It is front-loaded with the core purpose and wastes no words.

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?

Given that an output schema exists (so return values don't need explanation), the description covers all necessary context: what the tool does, what inputs are needed, that grading is asynchronous, and what to do next (poll submission_get). It is complete for a tool of this complexity.

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 schema already describes all three parameters clearly, so baseline is 3. The description adds meaning by explaining how answers are keyed to question_number (as printed) and that grading is done per mark point/level descriptor. This enhances understanding beyond the raw schema without being redundant.

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 clearly states the verb 'Mark' and the resource 'a student's typed answers against an existing exam paper.' It distinguishes from siblings like submission_get, which retrieves grades, and paper_get, which fetches papers. The title and opening sentence align, leaving no ambiguity.

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 gives clear context: use when you have an exam_paper_id and per-question answers. It suggests polling submission_get for results, which implies an alternative, though it doesn't explicitly say 'do not use this for retrieving grades.' This provides usable guidance without a full exclusion list.

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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