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ILP Drone Delivery MCP Server

ILP Drone Delivery MCP Server

by RheaBose
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Local

check_drone_availability

Find available drones for delivery by specifying required capacity, temperature needs, and date to match logistics requirements.

Instructions

Check which drones are available to handle a delivery with specific requirements

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "capacity": "<capacity>"
    },
    "name": "check_drone_availability"
  }
}

Input Schema

NameRequiredDescriptionDefault
capacityYesRequired capacity in kg
heatingNoRequires heating capability
coolingNoRequires cooling capability
dateNoDate in YYYY-MM-DD format

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It implies a read-only query but doesn't disclose response format, pagination, error conditions, or performance characteristics. For a tool with 4 parameters and no output schema, this leaves significant gaps.

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 a single, efficient sentence that front-loads the core purpose without unnecessary words. Every element earns its place, making it appropriately concise for the tool's complexity.

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?

Given the tool has 4 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what 'available' means operationally, how results are returned, or error handling. For a query tool with multiple filtering parameters, more context is needed.

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 100%, so the schema fully documents all parameters. The description adds no additional parameter semantics beyond implying filtering by requirements, which is already covered by the parameter descriptions in the schema. Baseline 3 is appropriate when schema does the heavy lifting.

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?

The description clearly states the tool's purpose as checking drone availability for deliveries with specific requirements, using a specific verb ('check') and resource ('drones'). It distinguishes from siblings like 'list_available_drones' by emphasizing requirement-based filtering, but could be more explicit about the distinction.

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?

The description provides no guidance on when to use this tool versus alternatives like 'list_available_drones' or 'plan_delivery'. It mentions 'specific requirements' but doesn't clarify prerequisites, exclusions, or comparative contexts with sibling tools.

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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