Find the exact glossary entry for a term or alias with case-insensitive matching. Use it to resolve abbreviations and synonyms from your notes' glossary.
Exact lookup by term or alias (case-insensitive).
Args: term: Term to look up (e.g., "usaf", "USAF", "US Air Force")
Returns: Glossary entry if found, or error dict
| Name | Required | Description | Default |
|---|---|---|---|
| term | Yes |
Changes observed during successful MCP inspections.
v1.1.0v1.0.46v1.0.42Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that lookups are case-insensitive and returns either a glossary entry or an error dict. It does not explicitly state that it performs no side effects, but that is reasonable for a lookup tool. The behavior is largely transparent, but could mention read-only intent.
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 compact and well-structured: a single purpose line, followed by Args and Returns sections. There is no fluff, and every sentence serves a purpose. The most critical information (exact, case-insensitive) is front-loaded.
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 tool, the description covers the essential details: what it does, parameter examples, and return type. It lacks a precise definition of the glossary entry structure, but no output schema exists. The description is sufficient for an agent to know when and how to call it, though it could be more explicit about the return format.
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 only the parameter name 'term' without any description (0% coverage). The description compensates excellently by explaining the parameter's meaning and offering concrete examples ('usaf', 'USAF', 'US Air Force'), and noting aliases are accepted. This adds significant semantic value beyond the schema.
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 (exact lookup), the resource (term or alias), and the case-insensitivity. However, it does not explicitly differentiate from sibling tool 'search_glossary' which likely supports fuzzy/partial matching. The term 'exact' implies a distinction, but it's not named.
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 usage: if you have an exact term or alias, use this tool; otherwise, use search_glossary. However, it does not explicitly mention alternatives or when not to use this tool. The context of 'exact lookup' suggests a dichotomy, but the guidance is implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.