signal_templates-create
Create a reusable research question template that can be applied to many companies in batch.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| __requestBody | Yes | Request body |
Create a reusable research question template that can be applied to many companies in batch.
| Name | Required | Description | Default |
|---|---|---|---|
| __requestBody | Yes | Request body |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=false and destructiveHint=false, but the description adds no additional behavioral details. It does not mention permissions, side effects, return behavior, or any constraints beyond the basic act of creation. No contradiction with annotations, but no extra value.
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?
A single, front-loaded sentence that clearly states the action and purpose. No fluff or unnecessary detail.
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 rich input schema and no output schema, the description sufficiently explains that this creates a reusable template for batch application, which is enough to select the tool. It could mention return values or prerequisites, but the purpose is clear and the schema fills in parameter details.
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 detailed descriptions for all nested properties (e.g., name, question, answerType, qualificationCriteria). The tool description adds no parameter-specific meaning, so the 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 clearly states the tool creates a reusable research question template for batch application, using the specific verb 'Create' and the resource 'reusable research question template.' It distinguishes itself from sibling CRUD tools (signal_templates-get/list/update/delete) and from signal creation tools like company_signals-create_batch.
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 implies the use case (creating a template for batch research questions) but does not explicitly state when to use this tool over alternatives such as company_signals-create_batch or signal_templates-update. It provides context but no exclusions or alternative comparisons.
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.