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itu-mcp

itu-mcp

by yatuk
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Get Tracked Updates

get_updates
Read-onlyIdempotent

Read stored Ninova tracking history to return recent detected changes, filtered by course, entity type, or limit.

Instructions

Read the stored Ninova tracking history and return recent detected changes.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_updates"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
courseNo
entity_typeNo

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_updatesDictOutput"New value: +"get_updatesOutput"
  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 already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety is covered. The description adds that results come from previously stored tracking history rather than live fetches, which is useful context, but it omits ordering (newest-first?), pagination behavior, and the prerequisite that tracking/watching exists.

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?

A single well-formed sentence with no filler; the purpose is front-loaded. It is arguably under-specified for the amount of behavior left unexplained, but it wastes nothing.

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 not be described, but with zero parameter documentation and no usage or prerequisite guidance for a filtered history query, the definition leaves an agent without enough to invoke it correctly in edge cases.

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 all three parameters. The word 'recent' loosely gestures at 'limit', but neither 'course' nor 'entity_type' is mentioned, and no accepted values or filtering semantics are given for 'entity_type'. The description fails to compensate for the coverage gap.

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 ('Read') and resource ('stored Ninova tracking history') and clarifies the return content ('recent detected changes'). It is distinguishable from nearby siblings like diff_snapshot and check_watched_course_sections, but it never names those alternatives to sharpen the contrast.

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 when-to-use or when-not-to-use guidance. An agent cannot tell from the text whether this is the right tool versus check_watched_course_sections, diff_snapshot, or get_dashboard_announcements, nor whether tracking must first be enabled via watch_course_sections.

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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