companies-enrich_tech_stack
Enrich a company with its tech stack
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| __requestBody | Yes | Request body |
Enrich a company with its tech stack
| Name | Required | Description | Default |
|---|---|---|---|
| __requestBody | Yes | Request body |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds no behavioral context beyond the annotations. It does not disclose caching behavior, webhook delivery, auto-resolution of technology names, or the distinction between sync and async endpoints. The parameter descriptions in the schema carry that burden, but the tool description itself is silent.
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 with no filler, achieving maximum conciseness. It is front-loaded with the core action and resource, and every word is meaningful.
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?
Given the tool has five parameters, a nested request body, and no output schema, the one-sentence description is minimal but not misleading. The schema provides detailed parameter info, and annotations cover read/write hints, but the description omits high-level context such as sync/async behavior or mutual exclusivity of category and technology.
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 schema fully documents parameters like domain, category, technology, webhookUrl, and forceRefresh. The description adds no parameter-level meaning, but the baseline of 3 is appropriate given that the schema handles semantics.
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 clear verb ('Enrich') and resource ('a company') with a specific scope ('its tech stack'), which distinguishes it from sibling enrichment tools like firmsgraphics or funding. However, it lacks detail about the optional parameters (category, technology) that define the type of tech stack enrichment.
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 provides no guidance on when to use this tool versus alternatives. While the sibling list includes other enrichment tools (e.g., companies-enrich_firmographics), the description does not mention any differentiators, prerequisites, or scenarios where this tool is preferred.
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.