ATC MCP CONNECTOR
ATC gives Claude and ChatGPT read-only tools for account fit, technology adoption, developer activity, active initiatives, and stakeholder context.
Try a query below, inspect the evidence returned to the model, or connect the server to your own AI workspace.
Read-only · OAuth-authenticated · 50 free credits/month · no credit card
Browser demo
This public page uses a fixed scenario so you can audit the shape of the answer without registration or access to nonpublic data.
Is this account a fit? Give me the strongest supporting evidence, the strongest counterargument, and what would falsify the thesis.
Wells Fargo is a plausible Snowflake fit where the recorded ATC thesis centers on transaction-risk decisioning: risk teams scaling across ATM, mobile, and branch channels may need fresher model outputs without slowing ingestion, scoring, BI, or downstream analytics.
The public-source-only baseline does not prove an active data-platform project, a confirmed buyer, or the technical pain. The thesis should be treated as a research path to validate.
Query path
The AI selected an ATC account-research capability.
ATC resolved the company and product context.
ATC returned structured account, stack, initiative, and evidence records.
The AI synthesized those records into the answer shown above.
ATC supplies the account intelligence. Claude or ChatGPT produces the final prose.
Boundaries
The product is useful only when the distinction between source evidence, interpretation, and generated prose stays visible.
A dated technology-adoption signal, developer event, organization change, public initiative, or other underlying account evidence.
ATC's interpretation of how those observations relate to the seller's product, likely problem, buying path, or next action.
The final prose, meeting brief, account plan, or outreach wording produced by the user's AI model.
An ATC conclusion is a research thesis, not ground truth. The supporting and conflicting evidence should remain visible so the user can evaluate it.
MCP capabilities
This is a focused subset of the public pricing catalog. Internal MCP method names are omitted here because this page only uses verified public capability labels.
Fast fit assessment scoring one account against your product
Every product in the account's stack that helps or hinders your deal
Strategic initiatives at the account relevant to your product
Recent tech adoptions, initiatives, and developer activity for one account
Open-source and community developer activity inside one account
Ranked list of accounts prioritized for your product
Evidence of how an account uses specific products
Complete outreach playbook for one account: fit scores, top business problems, signal-backed selling angles, talk track, next best actions, and a full stakeholder path with named contacts and personalized message hooks
Security
The connector lets the model request scoped ATC resources. It does not turn ATC into a middleware layer for your ordinary prompt stream.
Install
Create a free ATC account, add the remote MCP server, authenticate, and enable ATC in a conversation.
https://mcp.atc-analytics.com/mcpOpen Customize -> Connectors.
Add a custom connector.
Paste the ATC MCP URL.
Authenticate with ATC.
Enable ATC in the conversation.
Open Apps & Connectors or Connectors.
Enable developer mode or custom apps if available.
Create a connector using the ATC MCP URL.
Authenticate with ATC.
Select ATC in a new conversation.
Custom-app availability and admin approval can vary by plan and workspace.
Pricing
The free plan supports up to 50 one-credit research calls, or approximately five full Account Attack Plans, depending on the workflow.
Limitations
Why
Our team came from technology-ecosystem research for institutional investors. The work involved combining many weak or conflicting signals into a defensible view of what was happening inside a market or company.
When we began looking at LLM-based GTM workflows, the prose generation was not the hard part. The hard part was giving the model enough structured account and product context to produce something less generic. We built ATC to expose that research layer as tools an LLM can call.
Feedback
We would particularly value feedback on the MCP tool boundaries, whether the evidence is sufficient to audit a recommendation, and how much installation friction is acceptable for an authenticated remote MCP server.