Import leads from CSV data. Provide raw CSV content and optional column mapping. If no mapping is provided, columns are auto-mapped based on header names. After import, use campaignstack_search_leads to verify imported leads, or campaignstack_add_leads_to_list to add them to a campaign.
| Name | Required | Description | Default |
|---|---|---|---|
| csvData | Yes | ||
| mapping | No | ||
| workspaceId | No | Defaults to the API key's workspace |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already flag that this is a non-read-only, non-idempotent write, and the description adds behavior beyond that: raw CSV content is required, mapping is optional, and auto-mapping uses header names. It also usefully signals a verification workflow after import. No contradiction with annotations.
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 tightly packed in three sentences, front-loads the core purpose, and every sentence adds value: input format, mapping behavior, and post-import next steps. There is no filler or repetition of schema details.
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?
For a moderate-complexity import tool with no output schema, the description provides enough to call it with just csvData and to know what to do next. The main remaining gap is the unclear mapping structure, but the tool can be used without a mapping, so the description is sufficiently complete for the primary use case.
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?
With schema_description_coverage at 33%, the description compensates partially by explaining csvData as 'raw CSV content' and mapping as 'optional column mapping' with auto-mapping based on header names. However, it does not define the exact key/value semantics of the mapping object, which remains ambiguous.
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 opens with a specific verb and resource: 'Import leads from CSV data.' It clearly identifies the input format (CSV) and distinguishes this tool from other import siblings like campaignstack_import_apollo_list and campaignstack_import_external_leads, though it does not explicitly name them.
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 gives clear follow-up actions ('After import, use campaignstack_search_leads to verify...') but does not state explicit when-to-use versus alternative import tools. Usage context is implied by 'CSV data' rather than directly contrasted with sibling tools.
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 share the same verb prefix (create_, list_, update_, get_) across closely related resources, so pairs like add_lead_to_external_list vs add_lead_to_sequence, create_signal_agent vs create_signal_watch, and approve_review vs approve_content_post can be confused. The descriptions are unusually detailed and cross-referenced, which mitigates but does not eliminate the ambiguity inherent in a 282-tool surface.
Virtually every tool follows the campaignstack_verb_noun snake_case pattern, which is highly predictable. Minor deviations exist: destructive operations mix remove_ and delete_ (remove_lead_list vs delete_campaign), AI generation uses both craft_ and generate_, and the seo_/search_console_ subdomains introduce a second prefix convention.
282 tools is an extreme mismatch by any reasonable standard, exceeding the 50+ threshold by more than 5x. Even for a full B2B outreach platform, this surface is far too large and would be better consolidated into higher-level operations or grouped sub-servers.
The tool surface is impressively comprehensive, covering campaigns, workflows, leads, content, ads, SEO, integrations, billing, and more with CRUD-level depth. Minor gaps remain: no single-ICP getter, no direct pause/delete for search watches, and no explicit delete for ad campaigns (only archive via update).
