Return the existing validated relation between two exact modelKeys; a relation is not automatically a direct alternative.
| Name | Required | Description | Default |
|---|---|---|---|
| sourceModelKey | Yes | ||
| targetModelKey | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | ||
| sources | Yes | ||
| coverage | No | ||
| evidence | Yes | ||
| freshness | Yes | ||
| citationUrl | No | ||
| limitations | Yes | ||
| schemaVersion | Yes | ||
| knowledgeVersion | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds meaningful interpretive context: the relation is pre-validated and not necessarily a direct alternative, which helps the agent reason about the result. No contradiction with annotations.
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, tightly written sentence that front-loads the action and object, followed by one focused semantic caveat. Every word earns its place.
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 simple two-parameter, read-only tool with an output schema and safety annotations, the description covers the core operation and an important interpretation warning. It does not describe behavior when no relation exists, but the presence of an output schema reduces the need for return-value detail.
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?
Input schema descriptions have 0% coverage, so the description carries the burden. It clarifies that both parameters are 'exact modelKeys', which is useful, but it does not distinguish sourceModelKey from targetModelKey or add much beyond the schema's own pattern and names. Compensation is only partial.
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 specific verb ('Return') and resource ('validated relation between two exact modelKeys'), making the tool's function immediately clear. The caveat that a relation is not automatically a direct alternative helps distinguish it from search or context tools like search_vehicle_knowledge.
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 this tool is for precise model-key comparisons, but it never explicitly states when to use it over sibling tools or what alternatives exist. The caveat about direct alternatives aids interpretation but does not provide clear usage routing or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.