Look up correct terminology to avoid hallucinations and ensure accurate use of terms in life sciences CRM migration.
Look up correct vs incorrect terminology to avoid hallucinations.
Args: term: Term to look up (e.g. 'CLM', 'Approved Email', 'ICSR', 'CIOMS')
| Name | Required | Description | Default |
|---|---|---|---|
| term | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v1.0.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry behavioral disclosure. It indicates the tool returns correct-vs-incorrect terminology, which is useful, but does not explicitly state that it is read-only or describe the data source, coverage, or any auth/rate-limit behavior.
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 short and front-loaded: purpose first, then parameter examples. The Args block is compact and there is no redundant prose.
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 one-parameter lookup with an output schema, the description covers purpose and gives example terms. However, it omits when to prefer it over sibling knowledge tools and does not clarify the meaning of the empty default term, leaving gaps.
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 0% and the single parameter has no description. The description compensates partially by naming the parameter and giving example terms ('CLM', 'Approved Email', 'ICSR', 'CIOMS'), but it does not explain the empty default or requiredness.
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 uses a specific verb ('Look up') and resource ('correct vs incorrect terminology'), and states its purpose (avoid hallucinations). It does not distinguish itself from sibling tools like get_taxonomy or search_knowledge, so a 4 rather than 5.
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 explicit guidance on when to use this tool versus alternatives such as get_taxonomy or search_knowledge. The purpose ('avoid hallucinations') implies a use case, but there are no conditions, exclusions, or routing instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.