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Create a new agent

create_agent

Create a new AI sales agent. Costs 1 credit. The agent is created in DRAFT status and does not start contacting anyone — you still need to attach it to a campaign and launch the campaign. Required: name + description. Company info (name/website/description) is required BEFORE generating a sales script, so you can either pass it here OR call set_agent_company later.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesAgent's display name (e.g. 'Sales agent for Acme')
descriptionYesA short description of the agent itself (1-2 sentences, what it does)

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses a real side effect (costs 1 credit), the DRAFT status, and importantly that the agent does not begin contacting anyone upon creation. This is strong behavioral context; only minor gaps remain, such as return value or failure behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but not bloated, and it front-loads the core purpose and credit cost before explaining workflow steps. Every sentence adds relevant context, though the company-info sentence is slightly convoluted and could be tightened.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description richly covers cost, draft behavior, required fields, and next steps, but it undermines itself by saying company info can be passed here when the schema does not allow it. This contradiction could cause an agent to construct an invalid call. The absence of any return-value guidance is also a minor gap since no output schema exists.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and both name and description already have clear parameter descriptions, so the baseline is 3. The description adds useful workflow context about required fields and company info, but it also says company info can be 'passed here' even though the schema has no such parameters and additionalProperties is false, which is misleading.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Create a new AI sales agent,' naming a specific verb and resource. It is clearly distinct from siblings like create_campaign and set_agent_company, and the 'new' qualifier separates it from update_agent.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit usage context: required fields, DRAFT status, the fact that the agent won't contact anyone until attached to a campaign and launched, and the alternative of calling set_agent_company later for company info. This tells the agent exactly when and how to use the tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.1/5.0
Disambiguation2/5

Despite consistently detailed descriptions, several tool pairs have unclear boundaries: `get_subscription_limits` and `get_agent_plan_limits` describe essentially the same agent-slot check, `crm_get_conversation` and `crm_communication_thread` both claim to return the full message thread, and `get_credit_usage_by_agent` vs `get_credit_usage_for_agent` differ only by preposition. At 149 tools, an agent will regularly misselect between these near-duplicates.

Naming Consistency4/5

The dominant pattern is verb_noun with domain prefixes (`crm_*`, `sdr_*`) and a consistent `preview_*` family that maps cleanly to destructive/expensive actions. Deviations are minor but real: CRM deletes use the inverted `delete_crm_*` form while other CRM ops use `crm_*`, and credit-usage tools mix `by_agent`/`for_agent` prepositions.

Tool Count1/5

149 tools is nearly three times the 50+ threshold the rubric treats as extreme, even though the platform genuinely spans agents, campaigns, audiences, CRM, SDR, billing, and connections. Many could be consolidated without losing capability — e.g. the 11 balance/credit-usage tools, the two LinkedIn-account listers, and the 15+ preview variants.

Completeness5/5

The surface is remarkably complete: full CRUD/lifecycle coverage for agents, campaigns, audiences, CRM leads/stages, and SDR searches, plus billing, analytics, and connection management. Destructive or costly operations all have preview/approval counterparts, so there are no dead ends. If anything the risk is over-coverage rather than gaps.

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