MCP App Store
by KeenEthics

Overview

Cargo is GTM infrastructure for AI assistants. Connect this MCP server and Claude can operate your Cargo workspace: discover and run connector actions across 130+ integrations, execute tools and agents, query your CRM and revenue data models, and read the playbooks and context your team already wrote. Sign in with OAuth — no API key to paste. Your session binds to one workspace, so Claude sees your integrations, your models, and your credits. What you can do from chat: • Search the action catalog and see credit cost before anything runs • Enrich people and companies, look up CRM records, and call native platform ops • Run a single action or a batch across many records (with cost sampling before large runs) • Query workspace models with SQL-style selects, joins, and aggregates • Read workspace context — ICP definitions, playbooks, rubrics — before guessing how your team works Ask in plain English. Claude finds the right action, confirms cost when it matters, and brings results back into the conversation. Best for RevOps, sales ops, and GTM engineers who already live in Claude and want the research-to-action loop without leaving the chat. Building multi-step workflows, warehouse-wide SQL, or workspace-as-code deploys stays in the Cargo CLI — this connector is the runtime for in-conversation work. Learn more: https://www.getcargo.ai/mcp

Tools

autocomplete_action

ChatGPT
Resolve autocomplete options for a connector action (HubSpot object types, Slack channels, Metabase questions, …). Pass slug from search_actions.autocompletes. Resolve params to concrete values first — do not pass $this expression paths. Put chosen values in execute_action data, not action.config.

browse_context

ChatGPT
List the files and folders at a path in the workspace context. Pass depth -1 with no path for the full tree in one call. Navigates structure without reading any content.

describe_model

ChatGPT
Return a model's columns, dataset slug, and unquoted dataset.model reference without querying rows.

execute_action

ChatGPT
Run one action on a single input. Creates a run and consumes credits. If the result is outcome executing, poll with get_run. Do not loop this over a list — use execute_action_batch.

execute_action_batch

ChatGPT
Run one action over many records in a single batch. Cheaper and observable than looping execute_action. If the result is outcome executing, poll with get_batch.

get_action_schema

ChatGPT
Resolve the input and output JSON schemas of a connector, native, tool, or agent action without executing it. Pass the action object from search_actions. For connector fields that need a picked id (list, object type, campaign), call autocomplete_action.

get_batch

ChatGPT
Get one batch by uuid. If execute_action_batch returned outcome "executing", call this until the batch has finished. A finished batch includes one page of the CSV in data.text. Pass offset and limit to read further pages (limit default 100, max 100). If truncated is true, call again with the next offset. totalRows is the total when this page reaches the end of the file. Read data.text. Do not fetch the URL. Does not return the raw batch record or internal storage keys.

get_run

ChatGPT
Get one run by uuid. Returns the same shape as execute_action: the last node output, run uuid, and credits used. If execute_action returned outcome "executing", call this until the run has finished. Does not return computed configs, execution traces, or other debug payloads.

get_usage

ChatGPT
Credit usage for the last 7 days, grouped by integration. Check this before running paid actions.

list_models

ChatGPT
List data models in this workspace. Use describe_model then query_models.

list_runs

ChatGPT
List recent ad-hoc action runs in this workspace. Does not include play or tool workflow runs.

map_context

ChatGPT
List every indexed file in the workspace context (path, title, summary, folder) without reading any file body. The context is the workspace's knowledge base — playbooks, ICP definitions, scoring rubrics, internal docs. Start here to discover what exists, then search_context or read_context for the parts the task needs. This surface is read-only; write through the workspace's context capability.

query_models

ChatGPT
Run one read-only SQL SELECT over this workspace's models. Reference a model as dataset.model — take the exact reference from describe_model. Joins, aggregates and CTEs are allowed; anything other than SELECT is refused. Aggregate rather than paging: the row cap is small.

read_context

ChatGPT
Read a UTF-8 file from the workspace context. Reads with no endLine return only the first 250 lines and set truncated; pass a 1-indexed inclusive startLine/endLine range to page through the rest or target a section. The response always carries totalLines.

search_actions

ChatGPT
Search actions you can run: integration catalog (connector), Cargo native, workspace tools, and agents. Each result includes autocompletes when input fields need a picked id. Use the returned action with execute_action or execute_action_batch, get_action_schema, and autocomplete_action. Never fan out execute_action record-by-record — use execute_action_batch.

search_context

ChatGPT
Search context file contents and return matching lines (path, line number, snippet), never whole files. At most 5 matches per file, so results spread across files. Use it to locate a term, then read_context a tight range around the hits.

whoami

ChatGPT
Return the signed-in user, the workspace this MCP session is bound to, and a plan/credits snapshot.

autocomplete_action

Claude

browse_context

Claude

describe_model

Claude

execute_action

Claude

execute_action_batch

Claude

get_action_schema

Claude

get_batch

Claude

get_run

Claude

get_usage

Claude

list_models

Claude

list_runs

Claude

map_context

Claude

query_models

Claude

read_context

Claude

search_actions

Claude

search_context

Claude

whoami

Claude

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