Connect an Instantly.ai account by providing an API key. Validates the key against the Instantly API and stores the integration. Returns success: true when the key is valid and the integration is connected. Returns success: false (with integrationId still created) when the key fails validation.
| Name | Required | Description | Default |
|---|---|---|---|
| apiKey | Yes | ||
| workspaceId | No | Workspace ID (required for user keys; workspace keys are bound) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / workspaceId / descriptionPrevious value: -"Workspace ID (defaults to the bound workspace)"New value: +"Workspace ID (required for user keys; workspace keys are bound)"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint=false, destructiveHint=false, idempotentHint=true), the description discloses validation behavior, persistence of the integration, and the important partial-failure semantics where integrationId is still created even on failed validation. This adds meaningful runtime context not evident from annotations alone.
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 structured in three sentences: what the tool does, what it validates and stores, and what the return semantic is. No sentence is wasted, and the most important action verb is front-loaded.
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 two-parameter integration connector with no output schema, the description sufficiently explains the inputs, the validation flow, the persistence side effect, and the success/failure return semantics. The surprising behavior of creating an integrationId even on failure is explicitly called out, which is exactly the kind of context an agent needs.
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?
The description adds functional meaning for apiKey by explaining that it is validated against the Instantly API, going beyond the schema's type/minLength constraints. The workspaceId parameter is not described in the description, but the schema already provides a useful explanation for it, so the combined coverage is adequate.
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 states a specific action ('Connect an Instantly.ai account'), names the exact resource type, and clarifies the mechanism (API key validation and storage). It also distinguishes itself from sibling connector tools like campaignstack_connect_apollo and campaignstack_connect_hubspot through the explicit 'Instantly.ai' reference.
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 clearly implies this tool is for connecting an Instantly.ai account using an API key, which provides a clear context for when to use it versus other connector tools. However, it does not explicitly name alternatives or state when not to use this tool, so it falls short of fully explicit routing guidance.
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.
The set is enormous and generally well-differentiated through detailed cross-referenced descriptions, but several clusters blur together: archive/delete/remove have inconsistent permanence semantics (delete_campaign vs remove_signal_watch vs archive_campaign), create_connection_watch_agent explicitly overlaps with set_account_watcher, and the parallel draft-checkup and playbook-proposal flows (run_draft_checkup/get_draft_checkup/accept_draft_checkup vs propose_playbook_change/get_playbook_proposal/decide_playbook_proposal) present near-identical decision pipelines.
Nearly every tool follows the campaignstack_<verb>_<noun> convention with disciplined get/list pairing and consistent verb choices (create/update/delete/pause/resume). Minor deviations like campaignstack_priority_enrich (adverb+verb) and campaignstack_whoami break the strict verb_noun pattern but are isolated and do not hinder navigation.
223 tools is an extreme surface for any MCP server. Even though each tool maps to a distinct API operation and the underlying platform is broad, the scale far exceeds the 50+ threshold for an extreme mismatch and will overwhelm agents with selection overhead.
The surface is exhaustive for the LinkedIn outreach domain: full campaign/workflow/lead-list lifecycles, ICP and persona management, content scheduling and approvals, inbox and messaging, enrichment and integrations, signal watches and exclusions, review queues, playbook versioning, workspace admin, billing, and notifications. Minor gaps like a missing delete_lead or delete_company are explained by shared-data semantics, so no critical dead ends remain.