searchSearch the NYPL Digital Collections collection by keyword. Returns matching items with ids (pass an id to item), titles, creators/sources, dates and links.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Page (1-based, default 1). | |
| limit | No | Max results (1-100, default 20). | |
| query | Yes | Keyword(s). | |
| _apiKey | No | NYPL Digital Collections API key (auto-injected by the platform). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this as a safe, read-only, idempotent operation. The description adds value by disclosing the return fields (ids, titles, creators/sources, dates, links) and suggesting how to use the ids with the 'item' tool. No contradictions 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, front-loaded sentence that immediately states the action and resource. Every clause adds useful context (return fields, related tool reference), and there is no redundancy or fluff.
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?
Despite having no output schema, the description lists the key return fields and mentions how to proceed with results. Combined with schema details for page/limit and the read-only annotations, the description is sufficient for a straightforward search tool, though it lacks mention of sort or filtering behavior.
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%, so each parameter has a description. The tool description does not add meaning beyond what the schema already provides; 'by keyword' mirrors the query parameter description, and no additional syntax or format details are given.
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 ('Search'), a clear resource ('NYPL Digital Collections collection'), and a method ('by keyword'). It also mentions returning item ids and directs users to pass an id to the 'item' tool, distinguishing it from the sibling tool 'search_within'.
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 for keyword-based search on NYPL Digital Collections, but does not explicitly state when to use this tool over alternatives or provide any exclusions. It offers a hint about using returned ids with the 'item' tool, but no direct when-to-use guidance.
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.
Most tools have distinct purposes, with clear descriptions differentiating similar ones like ask_pipeworx and ask_pipeworx_grounded. A few overlaps exist (multiple Polymarket analysis tools), but descriptions sufficiently resolve ambiguity.
Tool naming is inconsistent, mixing descriptive phrases (entity_profile, polymarket_edges) with verb-object patterns (generate_llms_txt, search). No strong convention is followed, and the 'polymarket_' prefix is applied to some betting tools but not others.
32 tools is high but not extreme. However, the scope is too broad for a single server, covering data queries, betting, memory, NYPL, and more, making the set feel bloated and unfocused.
The domain is unclear due to mixed tools, but within the NYPL subset there are clear gaps (only search and item, no CRUD). For the other domains, coverage is uneven and lacks clear lifecycle completeness.