Explore how any algorithm applies to actual engineering problems. Enter an algorithm slug to retrieve concrete, real-world use cases that illustrate practical implementations and design decisions.
Get real-world engineering application cases for an algorithm
| Name | Required | Description | Default |
|---|---|---|---|
| algorithm | Yes | Algorithm slug, e.g. 'hash-table', 'binary-search' |
Changes observed during successful MCP inspections.
v0.3.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry behavioral disclosure. It implies a read-only retrieval ('Get') but gives no details about the response format, possibility of empty results, pagination, or any side effects. This is insufficient for a tool with zero annotation support.
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 a single sentence with no filler and the core action is front-loaded. It is appropriately sized for a simple tool, though it leans toward under-specification rather than rich structure.
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?
There is no output schema and no annotations, so the description should clarify what a 'real-world engineering application case' looks like and what the tool returns. It does not explain the output format or how an agent should interpret the response, making it incomplete for reliable invocation.
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?
The schema provides 100% coverage for the single parameter 'algorithm', including a description and example format. The description adds no additional semantic meaning beyond what the schema already states, so it meets the baseline for high schema coverage.
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 states a clear verb ('Get') and resource ('real-world engineering application cases') targeted at 'an algorithm', which distinguishes it from siblings like get_solution and get_theory. However, it does not explicitly name alternatives or highlight differentiators, so it is clear but not fully differentiated in text.
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?
No guidance is provided on when to use this tool versus siblings like get_solution or get_theory. There is no mention of context, prerequisites, or alternative tools, leaving the agent to infer usage from the name and sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.