itemFetch full details for one NYPL Digital Collections item by id — an NYPL item uuid (from search).
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | e.g. "510d47e4-1234-a3d9-e040-e00a18064a99". | |
| _apiKey | No | NYPL Digital Collections API key. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds contextual detail that the id is an NYPL item uuid from search, but does not disclose return format, pagination, or rate limits; however, given the strong annotations, this level of additional detail is adequate.
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 with no redundant words. It begins with the verb and directly states the resource and key parameter, making it maximally efficient.
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?
The tool is simple with one required parameter and strong annotations covering safety. The phrase 'Fetch full details' implies the return content, and the description provides sufficient context for use. A minor omission is not describing the exact return structure, but given the tool's simplicity, this does not hinder understanding.
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 description coverage is 100%, with both id and _apiKey documented in the schema. The description adds a small semantic cue by noting the id is an NYPL item uuid from search, which helps an agent understand the id's provenance, but it does not substantially go 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 uses the specific verb 'Fetch' and identifies the exact resource ('full details for one NYPL Digital Collections item'), clearly distinguishing it from sibling tools like search or entity_profile. It also specifies the required identifier ('NYPL item uuid'), leaving no ambiguity about what the tool does.
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 states that the id comes 'from search', giving clear context that this tool is used after a search operation, not for searching itself. It stops short of explicitly naming alternatives or exclusions, but the prerequisite is well implied.
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.