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Get Upcoming Deadlines

get_upcoming_deadlines

Retrieve assignments with approaching submission deadlines from your ITU Ninova snapshot. Specify days ahead to see what is due soon and plan your work.

Instructions

Return assignments whose submission deadline is approaching based on the stored tracking snapshot.

Input Schema

NameRequiredDescriptionDefault
daysNo
refreshNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv1.2.1
    • removedOutput schema / additionalProperties
      Removed value: -true
    • addedOutput schema / properties
      Added value: +{
      +  "result": {
      +    "additionalProperties": true,
      +    "title": "Result",
      +    "type": "object"
      +  }
      +}
    • addedOutput schema / required
      Added value: +[
      +  "result"
      +]
    • changedOutput schema / title
      Previous value: -"get_upcoming_deadlinesDictOutput"New value: +"get_upcoming_deadlinesOutput"
  2. First observedv0.2.0

TDQS

C2.9/5.0
Behavior3/5

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

Annotations declare readOnlyHint=false, openWorldHint=true, and idempotentHint=false, so the agent knows this is not a purely local read. The phrase 'based on the stored tracking snapshot' usefully discloses that results come from cached data rather than a live fetch, but the description never explains what the non-read-only behavior is or what `refresh` changes on the backend.

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?

One sentence, front-loaded with the action and resource, with no filler. It is efficient, though its brevity is partly under-specification rather than disciplined conciseness.

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

Completeness2/5

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

An output schema exists, so return values need no explanation. But for a tool with 0% parameter coverage, a non-read-only annotation, and many overlapping assignment siblings, the description leaves the agent without the window semantics, the refresh behavior, or any routing guidance.

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

Parameters2/5

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

Schema description coverage is 0% for both parameters. The description implies a time window ('approaching') but never states that `days` controls that window, nor what `refresh` does (presumably bypassing the stored snapshot). With no schema-level documentation, the description fails to compensate.

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 ('Return') and a well-defined resource ('assignments whose submission deadline is approaching'), so an agent knows exactly what it retrieves. It does not, however, distinguish itself from near siblings like get_dashboard_assignments or get_course_assignments, which also surface assignment data.

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

Usage Guidelines2/5

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

There is no statement of when to use this tool versus the assignment-listing siblings, nor any guidance on the `days` window or when to set `refresh`. The agent must infer the use case entirely from the name.

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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