Geographic distribution of private businesses. Group by city, zip, county, or state. Shows where businesses are concentrated.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Top N locations (default 30) | |
| state | No | Filter by state | |
| group_by | No | Group by: city, zip, county, state | city |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. The description tells us it produces a 'geographic distribution' and 'shows where businesses are concentrated,' implying it aggregates/counts. However, it doesn't disclose what 'distribution' means exactly (counts, percentages), whether filtering applies to results or just which businesses are included, or the return format. For a read-only analytical tool this is borderline adequate but lacks specifics.
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?
Two sentences, zero waste. The first sentence states the resource and grouping dimensions; the second clarifies the analytical value. Every word 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?
This tool has zero annotations, no output schema, and a compact 3-parameter schema. Given it's an analytical aggregation tool, the description should clarify what the output looks like and how group_by+state interact. It's functional but doesn't fully specify behavior—e.g., whether limit applies per group or total, or how results are ordered (top N implies descending by concentration).
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 100%, so all three parameters are documented in the input schema (group_by with its four enum-like values, state filter, limit defaulting to 30). The description adds the grouping dimension by naming the values, but doesn't add meaning beyond what the schema already provides. 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 states the tool shows geographic distribution and grouping options (city, zip, county, state), with a clear verb-resource combination. It doesn't explicitly distinguish from sibling tools like private_type_breakdown, but the geographic dimension is clear.
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?
No explicit guidance on when to use this tool vs. alternatives. The description implies it's for geographic concentration analysis, but doesn't name alternative tools or exclude cases. The text mentions it shows 'where businesses are concentrated' which gives some context but no exclusions.
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 distinct purposes: search vs resolve vs browse vs officer vs entity records are well-separated. However, private_browse and private_search overlap in intent (both find companies by criteria/name), and the description explicitly cross-references private_list which doesn't exist as a tool, adding confusion. private_ceo_search and private_officer_search are distinguished mainly by title scope, which is reasonable but could be misselected.
Tools consistently use the private_ prefix with snake_case verb_noun names (private_browse, private_search, private_resolve, private_ceo_search, private_officer_search). The convention is uniform and predictable. Minor deviation: private_entity is a noun-only tool name rather than verb_noun, but all others follow the pattern well.
11 tools is well-scoped for a company data server. Each tool covers a distinct data-access pattern (browse, search, resolve, officer find, entity record, aggregates), and none feel redundant or ornamental.
The surface covers identification (search/resolve), full records (private_entity), officer/executive lookups, people-to-company mapping, and data-availability introspection (private_stats). Minor gaps: there's no dedicated tool for fetching physical addresses or contact info beyond the entity record, and no filtered officer search by state/industry combining criteria with private_browse. But core lifecycle needs are covered.