ILP Drone Delivery MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves a delivery path in GeoJSON format but doesn't disclose key behavioral traits: whether this is a read-only operation (implied by 'Get' but not confirmed), if it requires authentication, rate limits, error conditions, or what happens with invalid inputs. The mention of geojson.io adds minor context for visualization, but overall, the description lacks sufficient detail for a mutation-free tool with no 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded: it starts with the core purpose ('Get the delivery path in GeoJSON format for map visualization') and adds a useful tip ('Can be used with geojson.io') in a second sentence. Every sentence earns its place by providing value without redundancy, making it efficient and easy to parse for an AI agent.

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

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

Given the tool's moderate complexity (5 parameters, no output schema, no annotations), the description is partially complete. It covers the purpose and output format but lacks details on behavioral aspects (e.g., safety, errors) and doesn't explain the return values beyond 'GeoJSON format.' Without an output schema, the description should ideally hint at the response structure, but it doesn't, leaving gaps in understanding how to interpret results.

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

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

Schema description coverage is 100%, so the schema already documents all parameters (deliveryLng, deliveryLat, capacity, heating, cooling) with clear descriptions. The description adds no additional parameter semantics beyond what the schema provides, such as explaining how these inputs affect the GeoJSON output or their interrelationships. With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract from the schema's documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get the delivery path in GeoJSON format for map visualization.' It specifies the verb ('Get'), resource ('delivery path'), and output format ('GeoJSON format'), distinguishing it from siblings like 'check_drone_availability' or 'plan_delivery' that focus on availability or planning rather than path retrieval. However, it doesn't explicitly differentiate from 'get_drone_details' or 'list_available_drones' in terms of path vs. drone data, leaving some ambiguity.

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

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

The description implies usage context with 'for map visualization' and 'Can be used with geojson.io,' suggesting it's for visualizing delivery paths on maps. However, it lacks explicit guidance on when to use this tool versus alternatives like 'plan_delivery' (which might also involve paths) or prerequisites such as needing delivery coordinates. No when-not-to-use or clear alternatives are mentioned, leaving usage somewhat inferred rather than explicitly defined.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.