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Whatsonyourmind

oraclaw-mcp-server

by Whatsonyourmind
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

solve_schedule

Read-onlyIdempotent

Assign tasks to time slots by matching their energy requirements with slot energy levels to maximize productivity. Ideal for deep-work scheduling, shift planning, and exercise scheduling.

Instructions

Assign tasks to time slots maximizing productivity, matching task energy requirements with slot energy levels. Use specifically for task→slot assignment with energy matching (deep-work scheduling, shift planning, exercise scheduling). For general resource allocation with arbitrary linear constraints, use solve_constraints. For sequence/route problems, use plan_pathfind. Deterministic.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tasksYes
slotsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
assignmentsYes
unassignedTasksNoTask IDs that did not fit.
totalScoreNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed9 schema fields changed
    • removedInput schema / properties / slots / description
      Removed value: -"[{id, name, duration, energyLevel}]"
    • addedInput schema / properties / slots / items / properties
      Added value: +{
+  "duration": {
+    "type": "number"
+  },
+  "energyLevel": {
+    "maximum": 1,
+    "minimum": 0,
+    "type": "number"
+  },
+  "id": {
+    "type": "string"
+  },
+  "name": {
+    "type": "string"
+  }
+}
    • addedInput schema / properties / slots / items / required
      Added value: +[
+  "id",
+  "duration"
+]
    • addedInput schema / properties / slots / minItems
      Added value: +1
    • removedInput schema / properties / tasks / description
      Removed value: -"[{id, name, duration, priority, energyRequired}]"
    • addedInput schema / properties / tasks / items / properties
      Added value: +{
+  "duration": {
+    "description": "Required slot duration (minutes).",
+    "type": "number"
+  },
+  "energyRequired": {
+    "description": "0..1 energy demand.",
+    "maximum": 1,
+    "minimum": 0,
+    "type": "number"
+  },
+  "id": {
+    "type": "string"
+  },
+  "name": {
+    "type": "string"
+  },
+  "priority": {
+    "description": "Higher = more important.",
+    "type": "number"
+  }
+}
    • addedInput schema / properties / tasks / items / required
      Added value: +[
+  "id",
+  "duration"
+]
    • addedInput schema / properties / tasks / minItems
      Added value: +1
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "assignments": {
+      "items": {
+        "properties": {
+          "score": {
+            "type": "number"
+          },
+          "slotId": {
+            "type": "string"
+          },
+          "taskId": {
+            "type": "string"
+          }
+        },
+        "required": [
+          "taskId",
+          "slotId"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "totalScore": {
+      "type": "number"
+    },
+    "unassignedTasks": {
+      "description": "Task IDs that did not fit.",
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "assignments"
+  ],
+  "type": "object"
+}
  2. First observedv1.0.1

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, etc. The description adds 'Deterministic' behavior, which is beyond annotations. It does not contradict annotations. It could detail edge cases but is sufficient given annotation coverage.

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, efficient and front-loaded with the main action and key concepts. No unnecessary words or repetition.

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 the tool's moderate complexity (two array parameters with nested objects), annotations covering safety and idempotency, and an output schema (present), the description provides sufficient context: purpose, selection criteria, and behavioral note (deterministic). It does not need to explain return values due to output schema.

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 0%, so the description should compensate. It adds the key semantic of 'matching task energy requirements with slot energy levels' which explains the 'energyRequired' and 'energyLevel' fields. However, it doesn't elaborate on task 'priority', slot 'duration', or other properties beyond what names and schema describe.

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 (Assign tasks to time slots) and resource (schedule), and explicitly distinguishes from siblings 'solve_constraints' and 'plan_pathfind' by naming them and specifying different use cases (general resource allocation, sequence/route problems).

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

Usage Guidelines5/5

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

The description provides explicit when-to-use guidance ('Use specifically for task→slot assignment with energy matching') and when-not-to-use by naming alternatives for different problem types. It also lists concrete applications (deep-work scheduling, shift planning, exercise scheduling).

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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