Return a company's firmographic profile (headcount, size, type, founded, HQ) plus a Saber-derived refined category and sell-to segments, with cited evidence.
| Name | Required | Description | Default |
|---|---|---|---|
| __requestBody | Yes | Request body |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=false and openWorldHint=true, but the description says only 'Return', which suggests a simple read operation. It does not disclose async behavior, possible credit consumption, force refresh implications, or that it may create a research job. The 'Saber-derived' phrase adds some context but not behavioral transparency.
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, well-structured sentence that front-loads the action ('Return') and concisely lists the key output contents. No wasted words.
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 lacks important context for a tool with async/webhook support and no output schema. It does not explain the 'Saber-derived' methodology, how sell-to segments are represented, or that the operation may be non-blocking. Given the complexity, the description is too minimal to fully prepare an agent.
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%, with the required 'domain' parameter fully described. The tool description does not add parameter-specific semantics beyond the schema; it focuses on the output fields rather than the input parameters. Baseline 3 applies because the schema handles parameter documentation.
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 ('Return') and clearly identifies the resource (company's firmographic profile). It lists the included fields (headcount, size, type, founded, HQ) and adds unique differentiators (Saber-derived category, sell-to segments), distinguishing it from sibling signals tools.
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?
There is no explicit guidance on when to use this tool versus alternatives. It does not mention when not to use it, prerequisites, or reference sibling tools like signals-funding or signals-tech. Usage is only implied by the tool name and description.
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.
