Era Context MCP connector for AI agents

CALL ANY TOOL

List accounts, search and bulk-update transactions, manage tags, transfers, and spending categories. Same toolkit, every framework, no auth plumbing.

eracontextmcp_accounts_list

List financial accounts

List all financial accounts available to the authorized user including connected institutions and manual accounts.

Parameters

Name

Type

Required

Description

status

string

Optional

Filter by account status: active, archived

eracontextmcp_transactions_search

Search transactions

eracontextmcp_transactions_update

Bulk update transactions

eracontextmcp_transfers_manage

Manage transfer links

eracontextmcp_tags_manage

Manage transaction tags

eracontextmcp_categories_manage

Manage spending categories

Build your Agent

Drop the toolkit in, point it at the user, and your agent can query and edit Era Context transactions from the first run.

import { ScalekitClient } from "@scalekit-sdk/node";
import { DynamicStructuredTool } from "@langchain/core/tools";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { z } from "zod";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["eracontextmcp"], toolNames: ["eracontextmcp_accounts_list", "eracontextmcp_transactions_search", "eracontextmcp_transactions_update"] },
  pageSize: 100,
});

const lcTools = tools.map((t) => new DynamicStructuredTool({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  schema: z.object({}).passthrough(),
  func: async (args) => {
    const { data } = await sk.tools.executeTool({
      toolName: t.tool.definition.name,
      identifier: "user_123",
      params: args,
    });
    return JSON.stringify(data);
  },
}));

const agent = createReactAgent({ llm, tools: lcTools });

import { ScalekitClient } from "@scalekit-sdk/node";
import OpenAI from "openai";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const openai = new OpenAI();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["eracontextmcp"], toolNames: ["eracontextmcp_accounts_list", "eracontextmcp_transactions_search", "eracontextmcp_transactions_update"] },
  pageSize: 100,
});

const llmTools = tools.map((t) => ({
  type: "function",
  function: {
    name: t.tool.definition.name,
    description: t.tool.definition.description,
    parameters: t.tool.definition.input_schema,
  },
}));

const resp = await openai.responses.create({
  model: "gpt-4o", input: prompt, tools: llmTools,
});

import { ScalekitClient } from "@scalekit-sdk/node";
import Anthropic from "@anthropic-ai/sdk";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const anthropic = new Anthropic();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["eracontextmcp"], toolNames: ["eracontextmcp_accounts_list", "eracontextmcp_transactions_search", "eracontextmcp_transactions_update"] },
  pageSize: 100,
});

const llmTools = tools.map((t) => ({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  input_schema: t.tool.definition.input_schema,
}));

const msg = await anthropic.messages.create({
  model: "claude-sonnet-4-6", max_tokens: 1024,
  tools: llmTools,
  messages: [{ role: "user", content: prompt }],
});

import { Agent } from "@google/adk/agents";
import {
  MCPToolset, StreamableHTTPConnectionParams,
} from "@google/adk/tools/mcp";

const toolset = new MCPToolset({
  connectionParams: new StreamableHTTPConnectionParams({
    url: "https://mcp.scalekit.com/eracontextmcp",
    headers: { Authorization: `Bearer ${userScopedToken}` },
  }),
});

const agent = new Agent({
  name: "agent", model: "gemini-2.0-flash",
  tools: await toolset.getTools(),
});

Try these prompts

Paste any prompt into your agent to start managing Era Context finances from your workflows.

Search & recall

Copy the prompt

Copied

Find all uncategorized transactions from last month.

Copy the prompt

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Show transactions over $[amount] tagged as [tag].

Copy the prompt

Copied

List all spending categories and their total spend YTD.

Bulk actions

Copy the prompt

Copied

Recategorize all [merchant] transactions as [category].

Copy the prompt

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Mark all transactions from [date] as reviewed.

Copy the prompt

Copied

Merge duplicate [category A] and [category B] categories.

SEE HOW AUTH WORKS

Users authorize Era Context once. Their credentials stay vaulted, every call is checked, and every action is logged.

1

Authorize

Your user connects

Era Context MCP

once. We tie it to their identity and the meetings they approved — no shared bot account, no org-wide access

Who:

user ‘A’

when:

Once per user

access:

Limited to user

2

Store

Their

Era Context MCP

token lives in a vault scoped to them. User A's meetings are never reachable by an agent acting for user B, even on the same connection

vault:

encrypted

scope:

per-user

tokens:

auto-refreshed

3

Resolve

When your agent calls a

Era Context MCP

tool, we fetch the right token server-side. It never touches your agent, never appears in the LLM context, never shows up in your logs

speed:

~40ms

check:

before every call

seen by:

nobody

4

Audit

Every

Era Context MCP

tool call is logged — who triggered it, which meeting was fetched, what came back. 90 days of history, tied to the user who authorized it

history:

90 days

export:

SIEM-ready

logged:

every call

Test other agents

Same per-user auth pattern across other finance and accounting connectors.

No items found.

Why Scalekit

Secure your agent's access. Connectors ship in minutes

Other connector libraries treat auth as a demo afterthought. Scalekit starts with user identity, scope enforcement, and audit.

01.

Shared tokens break per-user analytics

A shared token looks fine in a demo. In production every call looks like a service account. Scalekit resolves the real user credential so attribution, audit, and scope stay accurate.

// shared token
 audit → bot_service_account
 user_filter → broken

 // scalekit
 audit → user_abc
 scope → enforced ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

Era Context MCP today. Others tomorrow.

“Our agents act across Salesforce, Gong, Google Drive, and more, on behalf of every customer. Scalekit behind the scenes meant we can keep adding tools without ever rebuilding how credentials or tool calling work.”

Venu Madhav Kattagoni

Head of Engineering / Von

FAQs

Frequently Asked Questions

Does the agent access Era Context as the user or a shared account?

As the user. Each account authorizes once and Scalekit resolves their credential at request time. All edits are attributed to that user.

Where is the Era Context OAuth token stored?

In Scalekit's AES-256 vault, namespaced per tenant. Refresh is automatic. Tokens never appear in prompts or LLM context.

Can I prevent the agent from bulk-updating transactions?

Yes. Use listScopedTools to allow read-only operations — search and list — without granting the transactions_update tool.

What happens when a user revokes Era Context access?

The connection is invalidated on the next tool call. Subsequent requests fail closed. Other users remain unaffected.

Is this safe for production finance workflows?

Yes. Per-user credential isolation, scope enforcement before every call, and a 90-day audit trail are built in. Every edit executes as the authorizing user.

Era Context MCP

Tools your agent reaches for on Era Context MCP, scoped per user.