list_subjectsSubject headings (LCSH or free tags) ranked by distinct research-item count. Feed a heading into search_research_items.subject.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Default 50, max 300 | |
| offset | No | ||
| keyword | No | Substring filter on the subject heading | |
| vocabulary | No | Only Library of Congress headings or only free tags |
Changes observed during successful MCP inspections.
Input schema / properties / keyword / maxLengthAdded value: +1000Input schema / properties / vocabularyAdded value: +{
+ "description": "Only Library of Congress headings or only free tags",
+ "enum": [
+ "lcsh",
+ "tag"
+ ],
+ "type": "string"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description still adds real behavioral context: the result is ranked by distinct research-item count, and headings are either controlled vocabulary or free tags. Pagination behavior is left to the schema.
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 tight sentences with zero filler, front-loading what the tool returns before the actionable usage hint. Every clause 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, annotation-covered list tool with no output schema, the description conveys both what is returned (ranked subject headings) and how to consume it. Nothing an agent needs to call it correctly is missing.
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 75%, so most parameters are documented structurally. The description's mention of LCSH vs. free tags loosely corresponds to the vocabulary enum but largely restates schema content, adding little syntax or format guidance beyond it. Baseline 3 applies.
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 resource (subject headings, LCSH or free tags) and a precise ordering rule (ranked by distinct research-item count), which an agent can distinguish from siblings like list_categories or list_years. The scope and content are unambiguous.
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?
'Feed a heading into search_research_items.subject' gives explicit downstream usage, telling the agent why and when to call this tool in a workflow. It stops short of naming a when-not-to-use condition or a competing sibling, so it is clear context without exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.