Look up an npm (Node.js) package: latest version, description, license, repository, last publish date, deprecation status, and last-month download count. Pair with cve_search_by_keyword to check for known vulnerabilities. Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | npm package name, e.g. 'express' or '@scope/pkg'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish a safe, idempotent, read-only operation. Beyond that, the description adds useful behavioral context: the tool is 'Keyless' (no auth setup) and enumerates the exact data points returned, which is especially valuable since there is no output schema. No contradiction 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 two tight sentences with no filler. The main action and returned fields are front-loaded, the vulnerability-check pairing is a useful single clause, and 'Keyless' is a concise credential note.
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 single-parameter lookup tool, this is complete: it identifies the resource, lists the returned fields, notes the lack of authentication, and gives a relevant cross-tool recommendation. With annotations covering safety semantics and no output schema expected, nothing essential 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?
There is only one required parameter, and the schema already documents it thoroughly with an example ('express' or '@scope/pkg'). The description adds no parameter-specific guidance, but schema coverage is 100%, so the baseline of 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 uses a specific verb and resource: 'Look up an npm (Node.js) package' and lists seven concrete data fields it returns (latest version, description, license, repository, last publish date, deprecation status, download count). This clearly differentiates it from registry siblings like cargo_crate and pypi_package.
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 gives clear contextual use ('Look up an npm package') and explicitly recommends pairing with cve_search_by_keyword for vulnerability checks. It does not enumerate exclusions or alternatives, but the purpose is sufficiently scoped that an agent knows when to invoke it.
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.
Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.
The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.
290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.
For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.