Panintelligence Dashboard Integration Service

This Model Context Protocol (MCP) utility server establishes a robust bridge to the Panintelligence PI Reporting Platform's Application Programming Interface. It furnishes Claude and other compliant assistants with authenticated, granular access to platform artifacts, specifically focusing on data containers (categories) and visual representations of data (charts).

Operational Workflow with PI via MCP

Once the service is initialized and the endpoint is configured, typical interactions proceed as follows.

Establishing Initial Linkage:

Execute these preliminary steps to confirm operational readiness:

Confirm the PI API MCP server process is active
Configure the base endpoint address to http://localhost:8224/pi/api/v2
Invoke the 'authenticate' function to receive authorization instructions
Validate the connection health using the status checker tool
Fetch a small sample set of two distinct visualizations from the dashboard

Deep Dive Chart Examination:

Assuming visualization ID 450 holds necessary structural information, employ this sequence:

Fetch the configuration schema (metadata) associated with visualization ID 450
Deconstruct and retrieve the raw JSON payload for visualization ID 450
Determine which visualization IDs are referenced within any associated 'claims' metadata
Acquire the complete JSON payload for all discovered referenced visualizations
Synthesize the aggregated data to derive meaningful, actionable business interpretations

Example Result Snippet:

Deployment Instructions

Automated Deployment via Smithery

For rapid deployment to Claude Desktop environments using Smithery:

npx -y @smithery/cli install @mingzilla/pi-api-mcp-server --client claude

Deployment - Docker Containerization (Preferred Method)

No direct MCP Server setup adjustments are required on the host.
MCP client configuration file modification:

{
  "mcpServers": {
    "pi-api": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "API_URL=http://localhost:8224/pi/api/v2",
        "-e",
        "PI_API_KEY=XXXXXXXX",
        "mingzilla/pi-api-mcp-server"
      ],
      "disabled": false,
      "autoApprove": [
        "keep-session-alive",
        "check-connection",
        "authenticate",
        "list-categories",
        "get-category",
        "list-charts",
        "get-chart",
        "export-chart",
        "get-filterable-attributes",
        "export-chart"
      ]
    }
  }
}

Crucial Point: If the --api-url argument is omitted during initial launch, the system necessitates the use of the set-api-url utility before any operational calls can proceed, allowing for dynamic endpoint configuration.

Configuration File Path Reference

Locate the configuration file for your Claude Desktop installation at:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Linux: Consult auxiliary tools like Cline for path resolution of the MCP configuration.

Available Operational Tools

Schema Inspection

get-filterable-attributes: Ascertains which characteristics are permissible for data set refinement by inspecting a representative entity structure. Determine the fields available for filtering criteria on visualization data entities

Session and Endpoint Control

check-connection: Verifies the validity of the configured endpoint address and current authorization status.
set-api-url: Establishes or overrides the foundational Uniform Resource Locator for all subsequent API transactions. Assign the endpoint address: http://localhost:8224/pi/api/v2

Credential Management

authenticate: Provides instructions and guidance on approved methods for credential exchange.
authenticate-with-credentials: Emergency fallback: Authenticate using explicit username and password credentials.
keep-session-alive: Confirms active token validity or performs a refresh cycle (essential for token-based access).
logout: Revokes the current authorization token and terminates the active session.
set-organization: Designates the specific organizational unit ID for request scoping.

Data Groupings (Categories)

list-categories: Retrieves a comprehensive list of data groupings, supporting optional filtering parameters.
get-category: Fetches the detailed definition for a specified grouping ID.
create-category: Provisions a novel data grouping structure.
update-category: Modifies the properties of an existing data grouping.
delete-category: Removes a specified data grouping.
list-category-objects: Enumerates all underlying data items associated with a particular grouping.

Visualizations (Charts)

list-charts: Returns a catalog of all available data visualizations, supporting filtering.
get-chart: Retrieves the complete definition and data structure for a visualization ID.
delete-chart: Eradicates a specified visualization.
export-chart: Generates an output file of the visualization in various stipulated formats.

Access Endpoints (Resources)

auth://status: Queries the current authorization state.
categories://list: Endpoint to list all known categories.
categories://{id}: Direct access to a specific category record by its identifier.
categories://{categoryId}/objects: Endpoint for viewing content items within a category.
charts://list: Endpoint for listing all charts.
charts://{id}: Direct retrieval of a chart's definition.
charts://{id}/export/{format}: Endpoint for exporting a chart in a defined file type.

Illustrative Prompts for AI Interaction

These examples demonstrate suitable input queries for Claude post-service establishment:

Endpoint Configuration

Instruct the set-api-url utility to establish the Panintelligence API nexus at http://localhost:8224/pi/api/v2

Authorization Sequence

Guide me through the necessary steps to successfully authorize access to the PI API.

I possess an active session token. Please utilize the keep-session-alive tool, supplying this token: [YOUR_TOKEN_HERE]

Verify the integrity and operability of my current connection channel to the PI API.

Category Operations

Provide a complete manifest of all defined groupings within the dashboard.

Fetch the comprehensive configuration details for grouping identified as 123.

Visualization Operations

Generate a list of all visualizations present in the analytics environment.

Initiate the creation of a PDF export file for the visualization labeled 456.

Filtering Utilization

Query the get-filterable-attributes tool for visualization entities to ascertain usable fields for restriction parameters.

List all visualizations whose descriptive summary includes the term "revenue", employing the available filtering capabilities.

Analytical Prompts

Perform an analytical review of the organizational data groupings present in the system.

Execute a comparative analysis contrasting the datasets represented by visualizations 123 and 456.

Demonstrate the effective application of filtering mechanisms when interacting with this API interface.

Software Development Lifecycle

Local Runtime Execution

Reminder: The './start.sh' script can be used to launch the development server.

# Repository Cloning (Use SSH or HTTPS)
git clone git@github.com:mingzilla/pi-api-mcp-server.git
cd pi-api-mcp-server

# Dependency Installation
npm install
./dependencies.sh # Installs system-wide dependencies required for MCP client connection via "@mingzilla/pi-api-mcp-server"

# Code Compilation
npm run build

# Server Launch
npm start

NPM Package Deployment

# Global Installation
npm install -g @mingzilla/pi-api-mcp-server

# Execution via NPX with parameters
npx @mingzilla/pi-api-mcp-server --api-url "http://localhost:8224/pi/api/v2" --auth-token "XXXXXXXX"

MCP Client Configuration Integration

Integration Guide for Claude for Desktop:

Node.js Implementation Path

Follow the steps outlined in the "Local Runtime Execution" section.
Ensure ./dependencies.sh has been successfully executed for prerequisite installations.
Adopt the following configuration block (Note: "@mingzilla/pi-api-mcp-server" refers to the module installed locally):

{
  "mcpServers": {
    "pi-api": {
      "command": "npx",
      "args": [
        "-y",
        "@mingzilla/pi-api-mcp-server",
        "--api-url",
        "http://localhost:8224/pi/api/v2",
        "--auth-token",
        "XXXXXXXX"
      ],
      "autoApprove": [
        "keep-session-alive",
        "check-connection",
        "authenticate",
        "list-categories",
        "get-category",
        "list-charts",
        "get-chart",
        "export-chart",
        "get-filterable-attributes",
        "export-chart"
      ]
    }
  }
}

Local Development Configuration

Launch the service utilizing ./start.sh
Update the client configuration to point to the compiled output file path (build/index.js)

./start.sh

{
  "mcpServers": {
    "pi-api": {
      "command": "node",
      "args": [
        "/home/mingzilla/dev/tool-mcp-pi-api-server/build/index.js",
        "--api-url",
        "http://localhost:8224/pi/api/v2",
        "--auth-token",
        "XXXXXXXX"
      ],
      "autoApprove": [
        "keep-session-alive",
        "check-connection",
        "authenticate",
        "list-categories",
        "get-category",
        "list-charts",
        "get-chart",
        "export-chart",
        "get-filterable-attributes",
        "export-chart"
      ]
    }
  }
}

Project Development Progression

Code modification -> Initiate local server -> Test against the local server using the direct path to index.js
Update documentation (readme.md) -> Modify the mcpServers configuration blocks: Docker, Node, and NPX definitions.
Execute ./publish.sh to deploy to NPM.
Run ./dockerBuild.sh then ./dockerPublish.sh (ensure version in package.json is synchronized) -> Verify Docker configuration functionality.
Commit and push all changes to the central GitHub repository.

Software License

MIT License

Developer

Ming Huang (mingzilla)

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panintelligence-dashboard-connector

Author

mingzilla

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