company_peer_comparisonBenchmark a company against same-industry/same-country peers — employee distribution, jurisdiction mix, founding-decade histogram, and where the subject ranks within the peer set.
| Name | Required | Description | Default |
|---|---|---|---|
| lei | No | Resolve subject by GLEIF LEI. | |
| scope | No | industry_country | |
| domain | No | Resolve subject by domain. | |
| ticker | No | Resolve subject by ticker. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds useful behavioral detail beyond annotations by specifying the exact nature of the comparison and the output dimensions, giving the agent a concrete picture of what the tool will produce.
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?
A single sentence that is information-dense without padding. The main action is front-loaded, followed by concrete output specifics. Every phrase contributes to understanding.
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?
The description states outputs and scope but leaves gaps: no output schema, no guidance on required identifiers, no mention that one of lei/domain/ticker is presumably needed, and no detail on how 'scope' alternatives behave. For a 4-parameter tool with zero required parameters, this is a notable completeness gap.
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 covers 75% of parameters with descriptions for lei, domain, and ticker. The description does not add meaningful semantics about the 'scope' parameter beyond implying default same-industry/country behavior. It does not clarify how to choose among resolution identifiers or that at least one is expected, though schema provides basic resolution meaning.
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 opens with a specific verb, 'Benchmark', and a clear resource: a company against same-industry/same-country peers. It lists concrete output dimensions (employee distribution, jurisdiction mix, founding-decade histogram, rank) that distinctly separate this tool from sibling tools like company_enrich or company_industry.
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 clearly conveys the context for use: when a peer benchmark is needed, including what the comparison covers. It does not explicitly mention when not to use this tool or name alternative tools, but the context is unambiguous enough to guide selection.
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 genuinely blurry boundaries: company_change vs company_changes differ only by singular/plural yet serve different purposes, company_domain vs company_classify vs company_lookup_auto all accept a domain, geo_zip_lookup vs geo_enrich vs geo_zip_batch all return ZIP profiles, and email_validate subsumes much of email_disposable and email_free_provider. The domain prefixes help narrow search space, but within many domains an agent cannot reliably predict which tool is the right one.
All 129 tools uniformly follow a snake_case [domain]_[topic] convention (company_, fx_, geo_, dns_, weather_, tax_), which is highly predictable and consistent. Minor deviations include the confusing company_change/company_changes pair, and inconsistent suffix usage (_batch appears on address_validate_batch, company_domains_batch, geo_zip_batch but not on equivalent lookup tools elsewhere).
129 tools far exceeds the 50+ extreem-mismatch threshold, bundling roughly 28 unrelated data domains (weather, fx, tax, ccompany, dns, jobs, flight, email, phone, tax...) into a single MCP surface. Even focusing on one domain forces the agent to load an enormous unrelated tool list; this should be split into many smaller domain-specific servers.
Per-domain coverage is impressively thorough: weather spans current/forecast/hourly/historical/normals/marine/route/air-quality, fx covers rates/convert/historical/volatility/correlation/strenth, and company includes lookup/enrichment/networks/timeline/peer-comparison plus six buyer-tuned signals with profile-introspection tools. Minor gaps like flight being historical-only and smtp probes skipping major email providers are documented scope decisions rather than dead ends.