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OAUTH 2.1

PARCEL DATA

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Mireye MCP gives your agent location data for US properties: geocode addresses, fetch public data fields, enrich parcels, search parcels by conditions, and compute distances and nearest candidates.

  • Per-user credentials: each call uses the actual user's token, never a shared bot.
  • Encrypted per-tenant vault: AES-256, resolved at request time, never in LLM context.
  • Scoped before every call: pre-call scope check, 90-day SIEM-exportable audit chain.
Mireye MCP
agent · Acme Q3
Run
Where exactly is 410 Pine Street, Austin, TX, and how precise is that match?
S
mireyemcp_mireye_geocode
96ms
Location agent
The address resolves to 30.2672, -97.7431 with a rooftop match, so the coordinate sits on the parcel itself.
Sources: Mireye geocode, 1 address
mireyemcp
1 address
18:29
Message Claude...

Tools your location data agent reaches for on Mireye, scoped per user.

CALL ANY TOOL
9 tools for location data in Mireye: geocode US addresses, fetch public data fields, enrich parcels, search parcels, compute proximity, ask cited location questions, and request new fields.
mireyemcp_mireye_ask
Mireye ask
Answer a location question with cited evidence for $0.10 per completed answer. Give coordinates or an address. Accept terms and reconnect to use licensed parcel evidence. Reuse a stable idempotency_key for retries; max_cost_usd is an optional dollar ceiling. Check geocode precision before treating an address answer as parcel-specific. Inspect parcel_grade and accuracy_type: range_interpolation is an estimate, not a rooftop match and can be about 2.9 km off in rural areas. State the precision_note before making parcel-specific claims.
Parameters
Name
Type
Required
Description
question
string
Required
Natural-language question about the coordinate.
address
string
Optional
No description.
idempotency_key
string
Optional
No description.
lat
string
Optional
No description.
lng
string
Optional
No description.
max_cost_usd
string
Optional
No description.
mireyemcp_mireye_enrich
Mireye enrich
mireyemcp_mireye_fetch
Fetch mireye
mireyemcp_mireye_field_request_status
Mireye field request status
mireyemcp_mireye_geocode
Mireye geocode
mireyemcp_mireye_list_fields
Mireye list fields
mireyemcp_mireye_proximity
Mireye proximity
mireyemcp_mireye_request_field
Mireye request field
mireyemcp_mireye_search
Search mireye
Build your Agent
Same auth pattern across LangChain, OpenAI, Anthropic, and Google ADK.
Python · LlamaIndex
import { ScalekitClient } from "@scalekit-sdk/node";
import { createReactAgent } from "@langchain/langgraph/prebuilt";

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);

// Mireye tools scoped to this user
const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["mireyemcp"], toolNames: [
    "mireyemcp_mireye_geocode",
    "mireyemcp_mireye_fetch",
    "mireyemcp_mireye_enrich"] },
  pageSize: 100,
});

const agent = createReactAgent({ llm, tools });
await agent.invoke({ messages: [{ role: "user", content: "Geocode 410 Pine Street, Austin, TX, fetch its public data fields, and enrich the parcel" }] });
import OpenAI from "openai";
import { ScalekitClient } from "@scalekit-sdk/node";

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);
const openai = new OpenAI();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["mireyemcp"] }, pageSize: 100,
});

const res = await openai.chat.completions.create({
  model: "gpt-5",
  messages: [{ role: "user", content: "Geocode 410 Pine Street, Austin, TX, fetch its public data fields, and enrich the parcel" }],
  tools,
});

// Execute the tool call with the user's vaulted Mireye OAuth token
await sk.tools.executeTool(res.choices[0].message.tool_calls[0], "user_123");
import Anthropic from "@anthropic-ai/sdk";
import { ScalekitClient } from "@scalekit-sdk/node";

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);
const anthropic = new Anthropic();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["mireyemcp"] }, pageSize: 100,
});

const msg = await anthropic.messages.create({
  model: "claude-sonnet-5",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Geocode 410 Pine Street, Austin, TX, fetch its public data fields, and enrich the parcel" }],
  tools,
});

// Tool call runs with the user's vaulted Mireye OAuth token
await sk.tools.executeTool(msg.content, "user_123");
import { Agent } from "@google/adk/agents";
import { ScalekitClient } from "@scalekit-sdk/node";

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["mireyemcp"] }, pageSize: 100,
});

const agent = new Agent({
  name: "location_agent",
  model: "gemini-2.5-pro",
  instruction: "Mireye tools scoped to this user",
  tools,
});

await agent.run("Geocode 410 Pine Street, Austin, TX, fetch its public data fields, and enrich the parcel");
Try these prompts
Copy any prompt into your agent. Each maps directly to a Mireye MCP tool. Click to copy, paste into your agent, done.
Addresses and fields
Copy the prompt
Copied
Geocode 410 Pine Street, Austin, TX and tell me how precise the match is.
Copy the prompt
Copied
Fetch the public data fields available at this coordinate.
Copy the prompt
Copied
Which fields can mireye_fetch return?
Parcels
Copy the prompt
Copied
Enrich this parcel with its attributes and polygon calculations.
Copy the prompt
Copied
Find parcels in Travis County, TX that match these conditions.
Copy the prompt
Copied
List the fields I can filter parcel searches by.
Questions and requests
Copy the prompt
Copied
Compute the nearest 3 candidates to this address.
Copy the prompt
Copied
Ask Mireye a location question about this parcel and cite the evidence.
Copy the prompt
Copied
Request a data field Mireye does not have yet, then check its status.
SEE HOW AUTH WORKS
Each user signs in to Mireye once, and Scalekit stores and refreshes their tokens. Credentials stay vaulted, every call is scope checked, and every action is logged.
1
Authorize
Your user connects
Mireye 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
Mireye 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
Mireye 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
Mireye 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
See the same per-user auth pattern across other search and analytics connectors.
GTM and RevOps Teams
Competitive intelligence briefing agent
Scans Gong calls for competitor mentions, matches each one to its Notion battlecard, and DMs every affected rep a single Slack digest per cycle. Every call runs as the PMM who owns the briefing, never a shared bot.
Test other agents
See the same per-user auth pattern across other search and analytics connectors.
GTM
Competitive intelligence briefing agent
Scan Gong calls for competitor mentions, match each one to its Notion battlecard, and DM every affected rep a single Slack digest.
Why Scalekit
Secure your agent's access. Connectors ship in minutes
01.
Shared tokens break per-user analytics
A shared Mireye OAuth token looks fine in a demo. In production every paid parcel question looks like one service account, and you cannot tell which user triggered it. Scalekit resolves the credential of the actual user who triggered the agent, never a shared bot.
// shared token
audit → bot_service_account

// scalekit
audit → user_abc ✓
02.
Authentication is not authorization
03.
Multi-tenancy is architectural
04.
Mireye today. Ten connectors 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 Mireye as the user or through a shared service account?
As the user. Each user signs in to Mireye once, and Scalekit stores and refreshes their tokens. Every geocode, parcel search, and paid question in your audit trail is attributed to that user, not a shared service account.
Where is the Mireye OAuth token stored?
In Scalekit's managed AES-256 token vault, namespaced per tenant. Refresh is automatic. Revocation is a single dashboard action. Credentials never appear in prompts, logs, or LLM context.
Can I limit what the agent does in Mireye?
Yes. Filter by tool name in listScopedTools to expose only what you want. Only 1 of the 9 tools writes: mireyemcp_mireye_request_field asks Mireye to build a new data field, and none of the tools is destructive. You can also leave out mireyemcp_mireye_ask, which costs $0.10 per completed answer. Scalekit also enforces scope checks before every API call.
What happens when a user revokes Mireye access?
The connection is invalidated on the next tool call. Subsequent requests for that user fail closed with a clear error. Other users in the tenant remain unaffected. The event is logged for audit.
Does Mireye cover locations outside the US?
The tools work on US locations. mireyemcp_mireye_geocode resolves US street addresses, mireyemcp_mireye_fetch takes a US coordinate or address, and mireyemcp_mireye_search requires a two-letter US state code.
Start in your coding agent
Up and running in one command
Install the Scalekit skill in your editor of choice. Connector, auth, tools, prompt, all wired up
Claude Code REPL
/plugin marketplace add scalekit-inc/claude-code-authstack
/plugin install agentkit@scalekit-auth-stack
Cursor Code REPL
# ~/.cursor/mcp.json
{
""mcpServers"": {
""mireyemcp"": {
""url"": ""https://mcp.scalekit.com/mireyemcp"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}
Codex Code REPL
# ~/.codex/config.toml
[mcp_servers.mireyemcp]
url = ""https://mcp.scalekit.com/mireyemcp""
auth_env = ""SCALEKIT_TOKEN""
Copilot Code REPL
# .vscode/mcp.json
{
""servers"": {
""mireyemcp"": {
""url"": ""https://mcp.scalekit.com/mireyemcp"",
""type"": ""http""
}
}
}