Climate financial risk benchmarks — physical risk (flood, hurricane, wildfire, heat), transition risk (carbon pricing scenarios, stranded assets), and lender implications. Source: FEMA NFIP, NGFS scenarios. For ESG and risk agents.
| Name | Required | Description | Default |
|---|---|---|---|
| region | No | ||
| risk_type | No | ||
| property_type | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the description doesn't need to repeat safety. It adds contextual behavior by disclosing data provenance (FEMA NFIP and NGFS scenarios) and the scope of risk categories. This is valuable context beyond the structured fields, though it doesn't discuss rate limits or return formats.
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 three sentences, all informative. It front-loads the core purpose, then adds sources and audience. No filler or redundant information—every sentence earns its place.
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?
With no output schema, the description should explain what the agent receives. It gives a solid overview of the dataset's content (risk types, sources, lender implications) but does not mention response format, default behavior when no parameters are provided, or how multiple risk types are structured. This is adequate for a simple benchmark tool, but not fully complete.
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 provides enums for region, risk_type, and property_type, but the description only partially compensates for 0% schema_description_coverage. It elaborates on risk_type by defining physical and transition with examples, helping the agent select appropriate values. However, it does not explain the region or property_type parameters nor their default behavior, leaving some gaps.
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 identifies the tool's purpose: providing climate financial risk benchmarks. It specifies the resource (climate risk), the categories (physical and transition risk), and even gives examples (flood, hurricane, carbon pricing) and sources (FEMA NFIP, NGFS). It distinguishes from sibling tools by focusing explicitly on climate risk rather than generic ESG or other benchmarks.
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 the intended audience and use case: 'For ESG and risk agents.' It implicitly signals that this is the tool for climate-related risk benchmarks, setting it apart from generic ESG or financial benchmarks. However, it does not explicitly name alternatives or provide when-not-to-use guidance, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Multiple tools have overlapping purposes, e.g., get_inflation_benchmark and get_bls_inflation_components, get_commodity_benchmark and get_agricultural_commodity_benchmark. Descriptions provide some differentiation, but many benchmark tools cover similar domains, leading to high potential for misselection.
All tools follow a consistent 'get_' prefix with snake_case nouns, e.g., get_inflation_benchmark, get_ma_multiples_benchmark. No mixing of conventions or irregular naming patterns.
46 tools is excessive for a server focused on financial benchmarks and intelligence. While the domain is broad, many tools could be consolidated. The high count may overwhelm agents and suggests insufficient scoping.
The toolset covers a wide range of financial data—benchmarks, regulatory filings, commodity prices—but lacks granular tools like individual stock prices or sector-specific indices. Some areas (e.g., credit unions) are well-covered, but other common financial operations (e.g., portfolio analytics) are absent.