Return the QUANTUM scoring methodology, statutory weights, citation patterns, refusal list, and live data endpoint URLs. Use first when an AI agent or user is unfamiliar with QUANTUM or needs to cite QUANTUM properly.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, but the description fully discloses the tool's behavior: it returns methodology, weights, patterns, refusal list, and URLs. With zero parameters and a read-only nature (implied by 'Return'), the description covers all relevant behavioral traits without contradiction.
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 long, front-loads the key purpose in the first sentence, and contains zero filler. Every word contributes to understanding the tool's function and recommended usage.
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 parameterless tool with no output schema, the description thoroughly enumerates the returned data (methodology, weights, citations, refusal list, endpoints). It also provides usage context ('Use first when...'), making it self-sufficient for an agent to decide when to invoke it.
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 are no parameters, so schema coverage is 100% by default. The description adds meaningful context by listing the specific data categories returned (e.g., 'statutory weights', 'citation patterns'), which compensates for the lack of parameters and justifies a baseline-adjusted score of 4.
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 explicitly states it returns 'QUANTUM scoring methodology, statutory weights, citation patterns, refusal list, and live data endpoint URLs.' This specific verb+resource combination clearly distinguishes it from sibling tools like 'get_compound_details' or 'search_compounds', which serve different purposes.
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 provides direct guidance: 'Use first when an AI agent or user is unfamiliar with QUANTUM or needs to cite QUANTUM properly.' It implicitly excludes use cases where the user already knows the methodology or needs compound-level data, though it doesn't explicitly list alternatives.
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 target distinct lookups: search, detail, methodology, plan status, registry, recent changes, and top-ranked opportunities. There is some overlap in statutory status returns between get_compound_details, urban_renewal_registry, and planning_status_by_plan, but their descriptions clarify the source and scope enough to avoid major confusion.
The naming mixes verb-led patterns like get_, list_, and search_ with noun-style names like planning_status_by_plan and urban_renewal_registry. It is still readable and mostly predictable, but the lack of a uniform verb_noun convention makes the pattern less consistent.
Seven tools is well-scoped for a domain-specific analyzer server. Each tool addresses a distinct user need, and the count feels neither thin nor bloated.
The surface covers core analysis workflows: searching, retrieving detailed compound data, checking official registry status, inspecting live plan status, seeing recent statutory changes, and finding top mispriced opportunities. Minor gaps exist—like a general list/filter tool or historical comparison—but agents can work around them.