Export a saved company list to CSV, optionally including selected firmographic and signal columns.
| Name | Required | Description | Default |
|---|---|---|---|
| listId | Yes | The unique identifier of the company list | |
| __requestBody | No | Request body |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=false, destructiveHint=false, and openWorldHint=true. The description adds no context about side effects, asynchronous behavior, file handling, or authentication requirements. For an export operation that may create a file or return a download link, the lack of behavioral detail leaves significant ambiguity for the agent.
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, front-loaded sentence that directly states the action, resource, and optional parameters. It contains no filler or redundant information, earning full marks for conciseness.
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?
There is no output schema, so the description should explain what the agent should expect in return. 'Export to CSV' implies a CSV file, but it does not clarify whether the tool returns the file content, a download URL, or an async job ID. Given the moderate complexity (nested request body) and annotations indicating open-world behavior, the description is adequate but not fully complete.
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 description coverage is 100%, so the input schema already documents listId and the request body fields with clear descriptions. The tool description merely summarizes 'firmographic and signal columns' without adding any details beyond what the schema provides. Baseline of 3 applies.
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 a specific verb ('Export') and resource ('saved company list') with a clear output format ('CSV'), distinguishing it from siblings like company_lists-get or count_preview. It also mentions optional column selection, making its functionality precise.
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 'Export a saved company list to CSV' clearly implies the use case of obtaining a CSV export, but it does not explicitly compare with alternatives such as company_lists-get_companies (which likely returns structured JSON) or state when not to use the tool. No exclusion criteria or alternative suggestions are provided, leaving usage to be inferred.
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.
