Decide Test MCP

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool parses and generates outputs, but doesn't cover critical aspects like error handling (e.g., invalid formats), performance (e.g., large file handling), or output specifics (e.g., format of test case specifications). This leaves significant gaps for a tool with potential complexity in parsing and generation.

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, efficient sentence that front-loads the core action and key details (formats and output). There is no wasted text, and every word contributes to understanding the tool's purpose, making it highly concise and well-structured.

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 complexity (parsing multiple formats and generating specifications) and lack of annotations and output schema, the description is incomplete. It doesn't explain the output format, error conditions, or behavioral traits, leaving the agent with insufficient information to use the tool effectively beyond basic 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?

Schema description coverage is 100%, so the schema already documents both parameters ('table_path' and 'format') fully. The description adds minimal value by mentioning the formats (CSV, JSON, Markdown) and auto-detection, but doesn't provide additional syntax, examples, or constraints beyond the schema. This 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 clearly states the action ('parse') and resource ('decision table'), specifying the input formats (CSV, JSON, Markdown) and output ('test case specifications'). It distinguishes from siblings like 'execute_api_test' or 'generate_test_code' by focusing on parsing rather than execution or code generation. However, it doesn't explicitly contrast with all siblings (e.g., 'get_api_test_guidance'), keeping it from a perfect score.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as needing a valid file path, or compare it to sibling tools like 'generate_test_code' for similar test-related tasks. Usage is implied by the action but lacks explicit context or exclusions.

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