Page through companies that match a saved company list filter.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of companies to return (1–100, default 25) | |
| listId | Yes | The unique identifier of the company list | |
| offset | No | Number of companies to skip for pagination (default 0) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safe read-only nature is covered. The description adds the pagination behavior and the dependency on a saved list filter, but does not disclose details like result ordering, empty results, or error behavior. This is minimal additional value over annotations.
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 a single sentence that directly states the tool's function without filler or repetition. It is front-loaded and easy to scan.
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 simple paginated read tool with well-described parameters and safe annotations, the description is nearly complete. It could mention what happens when no companies match or how results are ordered, but these are not essential for correct invocation. The lack of an output schema is partially offset by the clear statement that it returns companies.
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 input schema provides 100% coverage with clear descriptions for limit, listId, and offset, so the description does not need to add parameter details. The term 'page through' reinforces the role of limit/offset, but adds no new semantics beyond the schema.
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 uses a specific verb ('Page through') and identifies the exact resource ('companies that match a saved company list filter'), clearly distinguishing it from related tools like company_lists-get (list metadata) and company_lists-list.
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 implies the tool is for paginating through companies in a saved list, but it does not explicitly state when to use it versus alternatives like company_lists-search, company_lists-get, or contact_lists-get_contacts. No exclusions or conditions are mentioned.
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.
Many tools have overlapping purposes, such as signals-firmographics vs companies-enrich_firmographics, findEmail vs contacts-enrich_work_email, and monitors vs signal_subscriptions vs market_signals. While descriptions add some context, an agent could easily select the wrong tool due to the high similarity in function.
Most tools use a resource_subresource-action pattern, but there are inconsistent separators: underscores within some names, hyphens in others (e.g., scoring-assignment-bulk-create), and several camelCase exceptions (findEmail, findEmailBatchGet, getContactResearchByExternalID). This mixed convention makes the tool set feel unpredictable.
With 119 tools, this server vastly exceeds the typical well-scoped range. Even for a comprehensive B2B data platform, the sheer number creates cognitive overload and increases the risk of incorrect tool selection.
The server covers an extensive range of operations: enrichment, lists, contacts, signals, subscriptions, monitors, and scoring. Nearly every resource has create, read, update, and delete or lifecycle equivalents, leaving very few practical gaps for the intended use case.
