ESG benchmarks by sector — carbon intensity Scope 1/2, net zero commitments, SBTi alignment, board independence, pay equity, and ESG composite scores. Sources: EPA GHGRP, MSCI ESG methodology. For sustainability agents and ESG analysts.
| Name | Required | Description | Default |
|---|---|---|---|
| focus | No | ||
| sector | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds useful behavioral context by specifying data sources (EPA GHGRP, MSCI ESG methodology) and the exact metrics included. However, it does not disclose aggregation details, time periods, or potential data caveats.
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 purpose ('ESG benchmarks by sector'), and every phrase adds value. No filler or redundancy.
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 read-only benchmark tool with two optional enum parameters, the description covers the subject matter and intended audience. However, it omits details about output format, units, or how to combine parameters, which would be helpful given no output schema exists.
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 coverage is 0%, so the description must compensate. It partially does by listing metric areas that map to the focus parameter (carbon, social, governance) and stating 'by sector' for the sector parameter. However, it does not explicitly explain how parameters filter or describe the meaning of each enum option.
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 resource as ESG benchmarks by sector and lists specific metrics (carbon intensity, net zero commitments, SBTi alignment, board independence, pay equity, ESG composite scores). It distinguishes from sibling tools by focusing on ESG specifics and naming sources. However, it lacks an explicit verb like 'retrieves', relying on the tool name for the action.
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 a target audience ('for sustainability agents and ESG analysts') but provides no explicit guidance on when to use this tool vs the many sibling benchmark tools. It does not mention alternatives, exclusions, or decision criteria.
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.
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.