Look up the TRIZ contradiction matrix for improving and worsening engineering parameters; blank or diagonal cells fail closed to prevent guessed recommendations.
Look up the canonical contradiction matrix for an improving and worsening engineering parameter. Blank and diagonal cells fail closed; never guess a recommendation.
| Name | Required | Description | Default |
|---|---|---|---|
| improving | Yes | Parameter to improve (1-39) | |
| worsening | Yes | Parameter that worsens (1-39) |
Changes observed during successful MCP inspections.
v0.5.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations supplied, the description carries the full burden and does disclose a real behavioral trait: blank and diagonal cells fail closed rather than returning a fabricated recommendation. That tells an agent how to interpret empty results and discourages hallucinated principles. It does not describe the successful return payload (presumably principle IDs) or how to map those IDs to principles.
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?
Two short sentences, front-loaded with the purpose and followed by the failure-mode caveat; nothing is padded or redundant.
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?
For a two-parameter lookup with full schema coverage and no output schema, the description covers purpose, failure semantics, and an anti-guessing rule. The remaining gaps—what the successful result contains and a pointer to triz_parameter for obtaining valid parameter IDs—are minor but real.
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 coverage is 100% and both parameters are documented with their 1-39 range in the schema, so the baseline is 3. The description confirms the semantic roles ('improving' vs 'worsening' engineering parameter) but adds no guidance on where valid parameter numbers come from, which would be the only meaningful addition here.
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 gives a specific verb+resource ('Look up the canonical contradiction matrix') and names the two inputs (improving/worsening engineering parameter), which is enough to separate it from sibling lookups like triz_principle or triz_parameter. It stops short of naming triz_matrix_multi as the multi-pair alternative, so differentiation is implied rather than stated.
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?
Usage is implied by the input signature: use this when you have one improving parameter and one worsening parameter. The 'never guess a recommendation' warning is useful behavioral guidance but is not a when-to-use/when-not-to-use rule, and no alternative tool (e.g., triz_matrix_multi) is named for multi-pair queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.