identifyNCI CACTUS one-shot lookup: given any chemical identifier (name, CAS, SMILES, InChI, or InChIKey), return SMILES, InChIKey, IUPAC name, molecular formula, molecular weight, and CAS number in a single call. Fields that cannot be resolved are returned as null. Keyless, plain-text API.
| Name | Required | Description | Default |
|---|---|---|---|
| identifier | Yes | Chemical name, CAS number, SMILES, InChI, or InChIKey (e.g. "ibuprofen", "15687-27-1"). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnly, idempotent, openWorld), the description adds useful behavioral details: unresolvable fields are returned as null, and the API is keyless and plain-text. This provides context about response shape and access requirements that annotations do not cover.
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 sentences, front-loaded with the core action, and every clause adds value (inputs, outputs, null behavior, API characteristics). No fluff or repetition.
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 with rich annotations and no output schema, the description fully explains what the tool returns, input types, and potential null fields. It provides all necessary information for an agent to select and invoke the tool correctly.
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 description already fully covers the `identifier` parameter, listing all accepted identifier types with an example. The main description repeats this information without adding further parameter-specific semantics, so baseline 3 is appropriate.
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 tool performs a one-shot chemical identifier lookup via NCI CACTUS, lists accepted input types (name, CAS, SMILES, InChI, InChIKey) and specific output fields. This distinguishes it from sibling tools like resolve_entity, which likely handle general entities.
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 makes the intended context clear: use when you have a chemical identifier and need standardized chemical data in one call. It does not explicitly mention alternatives or exclusions, but the specialization inherently differentiates it from sibling tools.
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 clearly differentiated roles, but several pairs blur boundaries: ask_pipeworx and ask_pipeworx_beta are currently functionally identical, and identify vs resolve both wrap the same NCI CACTUS service. The detailed descriptions rescue most selections, but an agent could easily mispick between the research and chemical lookup options.
Names are mostly snake_case and readable, but conventions are mixed: some are verb-first (ask_pipeworx, validate_claim, search_within) while many are noun-first or domain-prefixed (entity_profile, polymarket_edges, recent_changes, pipeworx_trending). There is no single predictable pattern for a new tool's name, though subfamilies (polymarket_*, ask_pipeworx_*) are internally consistent.
At 33 tools, this is well above the typical well-scoped range and carries real selection overhead. The unusually broad purpose—a data router plus prediction-market analysis, memory, subscriptions, and several standalone utilities—partially justifies the count, but it still feels heavy and could be consolidated.
For its varied subdomains, coverage is strong: memory has remember/recall/forget, subscriptions have subscribe/unsubscribe/list/recent_alerts, and prediction markets span research, edge scanning, arbitrage, fill-risk, and edge telemetry. Minor gaps exist—such as no direct tool to fetch a specific citation URI by identifier, and the redundant stable/beta router pair—but there are no obvious dead ends.