Check what this dataset actually covers before relying on it. Returns per-state record counts AND the government source behind each state, which matters: some states are full business registries with officer records, while others (California, Illinois) hold only federal SAM.gov registrants with no officer data at all. Read this before asking who runs a company in a state you have not used before.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: returns per-state counts and source, and highlights the important caveat that some states (California, Illinois) only hold SAM.gov registrants with no officer data. This goes beyond a simple 'returns stats' statement.
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, front-loaded with the main purpose, and each sentence adds value: purpose, output details, and usage warning. No fluff or repetition.
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 no-param tool with no output schema, the description explains the purpose, the returned data (per-state counts and source), and the practical implication for data reliability. It could optionally mention whether all states are included or only used ones, but overall it is complete for the tool's simplicity.
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 tool has 0 parameters, and the schema is empty with 100% coverage. Baseline for 0 params is 4. The description doesn't need to explain parameter semantics, and it doesn't, focusing instead on the output context.
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 states the tool checks dataset coverage by returning per-state record counts and government source. It distinguishes itself from sibling tools like private_search and private_officer_search by focusing on dataset-level statistics rather than individual records.
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 explicitly instructs when to use the tool: 'Read this before asking who runs a company in a state you have not used before.' It also warns about states with no officer data. It doesn't mention exclusions or alternatives but gives strong contextual guidance.
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.
Each tool targets a distinct query type: name search, identity resolution, attribute-based browsing, executive vs officer lookup, entity records, and aggregate statistics. Descriptions explicitly call out when to use one over another (e.g., private_search vs private_resolve vs private_browse).
All tool names share the 'private_' prefix and follow snake_case. While some use verbs (browse, search, resolve) and others use nouns (entity, geography, stats), the pattern is highly predictable and the domain is uniformly private company data.
11 tools is right-sized for a business registry server: enough to cover various lookup and analytical needs without redundancy. Each tool has a clear purpose and earns its place.
The surface covers the full lifecycle of interacting with company records: discover (search, browse, resolve), detail (entity), people (officer, ceo), signals (owner_operated), aggregations (age, geography, type), and dataset awareness (stats). No obvious gaps for the stated scope.