How do I use analytics-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@analytics-mcp Show top 5 products by revenue this month" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

analytics-mcp

by bhavya998

Overview Schema Related Servers Score Discussions

Python

Hybrid

analytics-mcp

Production-grade MCP server for enterprise sales analytics. 6 tools, 3 resources, 2 prompts — all with structured outputs — over a SQLite database. Connects to Claude, OpenCode, Cursor, and any MCP client.

Python FastMCP Tests License

What This Is

A Model Context Protocol (MCP) server that exposes an enterprise sales database to LLM clients. Instead of copy-pasting data into ChatGPT, connect this server and let the AI query, analyze, and visualize your data through structured tools.

Built with the latest MCP patterns (July 2026):

FastMCP 3.4 — high-level server framework
Structured outputs — Pydantic models define outputSchema for every tool
Tool annotations — readOnlyHint lets clients skip confirmations
In-memory client testing — no port flakiness, era-neutral
Resources — schema introspection, table details, report templates
Prompts — reusable analysis templates (sales analysis, customer segmentation)
Both transports — stdio (local) + HTTP (remote)

Related MCP server: agente-ecommerce-mcp-memoria

Quick Start

git clone https://github.com/bhavya998/analytics-mcp.git
cd analytics-mcp
uv sync

# Initialize database (200 customers, 25 products, 2000 orders)
uv run analytics-mcp init

# Start server (stdio for local MCP clients)
uv run analytics-mcp serve

# Or HTTP transport for remote access
uv run analytics-mcp serve --transport http --port 8000

Connect to Claude Desktop / OpenCode

Add to your MCP client config:

{
  "mcpServers": {
    "analytics": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/analytics-mcp", "analytics-mcp", "serve"]
    }
  }
}

Then ask: "Show me the top 5 products by revenue and analyze the monthly sales trend"

Tools

Tool	Description	Annotations
`query_database`	Run parameterized SELECT queries against the database	`readOnlyHint`
`get_revenue_report`	Revenue by category/region/tier/status/month with profit analysis	`readOnlyHint`
`get_customer_profile`	360-degree customer view: orders, LTV, favorite category, recent activity	`readOnlyHint`
`get_top_products`	Leaderboard by revenue or quantity, optional category filter	`readOnlyHint`
`analyze_sales_trend`	Time-series with period-over-period growth rates	`readOnlyHint`
`get_regional_performance`	Ranked regional performance with revenue, orders, customers, AOV	`readOnlyHint`

Resources

URI	Description
`schema://database`	Full database schema (all tables, columns, types, row counts)
`schema://tables/{table_name}`	Detailed table schema with sample rows
`report://templates`	Available report templates and usage examples

Prompts

Prompt	Description
`sales_analysis`	Comprehensive sales analysis with focus area (overall/category/region/customer)
`customer_segmentation`	Segment customers into Champions/At Risk/New/Dormant with retention strategies

Database Schema

customers (200 rows)
  id, name, email, company, region, tier, signup_date, lifetime_value

products (25 rows)
  id, name, category, price, cost, stock

orders (2000 rows)
  id, customer_id, employee_id, order_date, status, total

order_items (5000+ rows)
  id, order_id, product_id, quantity, unit_price

employees (15 rows)
  id, name, role, region, hire_date

Testing

make test         # 43 tests: database, tools, full MCP client integration
make lint         # ruff

Suite	Tests	Pattern
`test_database.py`	8	DB init, query validation, schema introspection
`test_tools.py`	17	Direct tool function calls (all 6 tools)
`test_server_client.py`	12	Full MCP protocol via in-memory Client

Tests use the modern in-memory Client(mcp) pattern — no ports, no subprocesses, era-neutral.

Tech Stack

Layer	Technology
MCP Framework	FastMCP 3.4 (Prefect)
Protocol	MCP 1.28 (Streamable HTTP + stdio)
Database	SQLite with seeded enterprise data
Validation	Pydantic v2 (structured tool outputs)
CLI	Typer + Rich
Testing	pytest + pytest-asyncio + FastMCP Client

Project Structure

analytics-mcp/
├── src/analytics_mcp/
│   ├── server.py         MCP server: 6 tools, 3 resources, 2 prompts
│   ├── database.py       SQLite setup + schema + seed data (2000+ orders)
│   ├── schemas.py        Pydantic models for structured outputs
│   └── cli.py            CLI (serve, init, inspect)
├── tests/                43 tests (database, tools, client integration)
├── data/                 SQLite database (auto-generated, gitignored)
├── Makefile
└── pyproject.toml

License

MIT

Available Tools

6 tools

analyze_sales_trendA

Read-only

Analyze sales trends over time with period-over-period growth rates.

Returns revenue, order count, and growth percentage for each period.

ParametersJSON Schema

Name	Required	Description	Default
`period_end`	No	End date YYYY-MM-DD	2025-06-30
`granularity`	No	Time grouping: 'month' or 'quarter'	month
`period_start`	No	Start date YYYY-MM-DD	2024-01-01

Output Schema

ParametersJSON Schema

Name	Required	Description
`trend`	Yes
`granularity`	Yes	Time grouping: 'month' or 'quarter'
`peak_period`	Yes	Best performing period
`overall_growth_pct`	Yes	First-to-last period growth

TDQS

A3.8/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, so the safety profile is known. The description adds useful behavioral context by detailing the return structure (revenue, order count, growth percentage), which helps the agent understand what to expect. However, it does not disclose limitations like date range handling or edge cases.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences with no extraneous information. Each sentence earns its place: first states purpose, second specifies outputs. Well-structured and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that an output schema exists and annotations cover read-only, the description is largely complete. It names the key outputs. However, it could mention that the tool compares periods or handles time ranges implicitly, but for a simple read tool, this is adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the parameters are already well-documented in the schema. The description does not add new semantic meaning beyond the schema; it only restates the tool's focus on growth. Baseline 3 is appropriate when schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly specifies the verb 'analyze' and the resource 'sales trends' with a focus on period-over-period growth rates. It lists specific output metrics (revenue, order count, growth percentage) and distinguishes from siblings like get_revenue_report or get_top_products by emphasizing trend analysis and growth rates.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. The description does not mention conditions, prerequisites, or explicitly state scenarios where another sibling tool might be more appropriate. The sibling list exists but is not leveraged in the description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_customer_profileA

Read-only

Get a 360-degree customer view: profile, order history, spending stats, favorite category.

Useful for account reviews, churn analysis, and upsell identification.

ParametersJSON Schema

Name	Required	Description	Default
`customer_id`	Yes	Customer ID to profile

Output Schema

ParametersJSON Schema

Name	Required	Description
`name`	Yes
`tier`	Yes
`email`	Yes
`region`	Yes
`company`	Yes
`customer_id`	Yes
`signup_date`	Yes
`total_orders`	Yes
`recent_orders`	Yes	Last 5 orders
`lifetime_value`	Yes
`avg_order_value`	Yes
`completed_orders`	Yes
`favorite_category`	Yes	Product category with most purchases

TDQS

A4.3/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, so no contradiction. The description adds valuable behavioral context by listing the types of data returned (profile, order history, spending stats, favorite category).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loads the main action, and contains no unnecessary words. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema (implied but not shown), the description does not need to detail return values. It covers purpose, usage, and core data aspects completely for a read-only customer profile tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with a clear description for customer_id. The tool description does not add additional parameter semantics, but the schema already handles it adequately, meeting the baseline for high coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves a comprehensive 360-degree customer view including profile, order history, spending stats, and favorite category. This distinct purpose from siblings like 'get_regional_performance' or 'analyze_sales_trend' is evident.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly mentions use cases: account reviews, churn analysis, and upsell identification. While it does not specify when not to use or name alternatives, the provided contexts strongly guide appropriate usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_regional_performanceA

Read-only

Get ranked sales performance by region: revenue, orders, customers, AOV.

Returns all 5 regions ranked by total revenue.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

Output Schema

ParametersJSON Schema

Name	Required	Description
`regions`	Yes
`total_revenue`	Yes	Sum across all regions

TDQS

A3.8/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint=true, so the description's mention of 'ranked' adds minor context. It does not discuss other behavioral traits like rate limits or data freshness, but it aligns with the read-only nature.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences: the first front-loads the action and key metrics, the second specifies the scope (all 5 regions, ranking by revenue). No redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a parameterless, read-only tool with an output schema, the description covers purpose and result structure. It could mention the default sort order explicitly, but the current detail is sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero parameters, and schema description coverage is 100%. The description compensates by explaining the tool's output (ranked performance across regions) without needing parameter details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get' and the resource 'ranked sales performance by region', listing specific metrics (revenue, orders, customers, AOV). It distinguishes from siblings like 'get_revenue_report' by specifying ranking across all five regions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives such as 'analyze_sales_trend' or 'get_revenue_report'. The description only states what it does, leaving the agent to infer usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_revenue_reportA

Read-only

Generate a revenue report grouped by a dimension within a date range.

Returns total revenue, order count, average order value, and estimated profit per segment.

ParametersJSON Schema

Name	Required	Description	Default
`group_by`	No	Dimension to group by: 'category', 'region', 'tier', 'status', or 'month'	category
`period_end`	No	End date YYYY-MM-DD (default: 2025-06-30)	2025-06-30
`period_start`	No	Start date YYYY-MM-DD (default: 2024-01-01)	2024-01-01

Output Schema

ParametersJSON Schema

Name	Required	Description
`group_by`	Yes	Dimension used for grouping
`segments`	Yes	Revenue breakdown by group
`period_end`	Yes	Report period end date
`grand_total`	Yes	Total revenue across all groups
`period_start`	Yes	Report period start date

TDQS

A4.3/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint=true, and the description adds the output metrics (total revenue, order count, avg order value, estimated profit), providing behavioral context beyond the annotation without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two concise sentences: first states the action and constraints, second states the output. No extraneous words, highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the existence of an output schema, the description appropriately complements it by listing the metrics. All parameters are documented, no required params, and the description covers the tool's purpose and output. Complete for the complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with each parameter having a description and default. The description adds no new information about parameters beyond what the schema provides, so baseline score of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool generates a revenue report grouped by a dimension within a date range, specifying the metrics returned. This distinguishes it from siblings like analyze_sales_trend (trend analysis) and get_regional_performance (region-specific).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implicitly defines use for revenue reports grouped by dimension but lacks explicit when-to-use or when-to-avoid guidance compared to siblings. While the purpose is clear, no alternatives or exclusions are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_top_productsA

Read-only

Get a leaderboard of top-performing products by revenue or quantity sold.

Optionally filter by category.

ParametersJSON Schema

Name	Required	Description	Default
`limit`	No	Number of top products to return
`metric`	No	Ranking metric: 'revenue' or 'quantity'	revenue
`category`	No	Filter by product category (optional)

Output Schema

ParametersJSON Schema

Name	Required	Description
`limit`	Yes
`metric`	Yes	Ranking metric: 'revenue' or 'quantity'
`products`	Yes

TDQS

A4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, so the description does not need to restate that. It adds context about ranking by metric and optional category filter, but does not disclose ordering direction or other behavioral details beyond what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences, front-loaded with the primary action, and no extraneous information. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 3 parameters fully described in the schema, an output schema, and annotations, the description covers the essential purpose and optional filtering. It could mention descending order or pagination, but the overall completeness is high given the existing structured data.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Parameter schema coverage is 100% with descriptions. The description adds value by linking the 'metric' parameter to 'revenue or quantity sold' and the 'category' parameter to optional filtering, which clarifies the semantics beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Get a leaderboard of top-performing products by revenue or quantity sold' – a specific verb and resource. It distinguishes from siblings like get_revenue_report and analyze_sales_trend by focusing on product ranking.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for retrieving top products but provides no explicit guidance on when to use this tool versus alternatives, nor any exclusion criteria or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

query_databaseA

Read-only

Run a read-only SQL query against the enterprise database.

Tables: customers, products, orders, order_items, employees.

Examples: SELECT * FROM customers WHERE tier = 'enterprise' LIMIT 10 SELECT region, COUNT(*) as customers FROM customers GROUP BY region SELECT p.name, SUM(oi.quantity) as sold FROM order_items oi JOIN products p ON oi.product_id = p.id GROUP BY p.name ORDER BY sold DESC LIMIT 5

ParametersJSON Schema

Name	Required	Description	Default
`sql`	Yes	SELECT SQL query to execute. Only SELECT statements allowed.

Output Schema

ParametersJSON Schema

Name	Required	Description
`rows`	Yes	Result rows as dicts
`columns`	Yes	Column names in result order
`row_count`	Yes	Number of rows returned

TDQS

A4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The annotation readOnlyHint=true already indicates the tool is read-only. The description adds no further behavioral details such as authentication requirements, rate limits, or consequences. It simply repeats the read-only nature without additional context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded with the core purpose. It efficiently provides table names and examples in a structured format without unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema and annotations, the description is sufficiently complete for a read-only query tool. It could be slightly improved by mentioning query timeout limits or that DDL statements are blocked.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema documents the 'sql' parameter with a description. The description adds value by listing available tables and providing examples, which helps the agent understand query patterns and constraints beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool runs read-only SQL queries against the enterprise database. It specifies the verb 'run a read-only SQL query' and the resource 'enterprise database', distinguishing it from sibling tools that focus on specific reports or analyses.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for ad-hoc SQL queries but does not explicitly state when to use this tool versus alternatives like 'get_revenue_report' or 'analyze_sales_trend'. It lacks guidance on when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

6 tool updatesv0.1.0Jul 10, 2026
- First observedanalyze_sales_trend
- First observedget_customer_profile
- First observedget_regional_performance
- First observedget_revenue_report
- First observedget_top_products
- First observedquery_database

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A3.9/5.0

Disambiguation4/5

Tools are mostly distinct: each targets a specific analytics aspect (trends, customer profile, regional, revenue report, top products, raw SQL). Some overlap in subject matter but purposes and outputs are clearly differentiated.

Naming Consistency3/5

Most tools use 'get_' prefix, but 'analyze_sales_trend' and 'query_database' break the pattern. The naming is readable but inconsistent across verbs.

Tool Count5/5

6 tools is well-scoped for an analytics server, covering common analytical functions without being excessive. Each tool earns its place.

Completeness4/5

Covers key analytics areas: trends, customers, regions, revenue, products. The query_database tool compensates for most gaps. Minor omission like employee analytics is noted but not critical.

Maintenance

ActivitySlowing

ResponsivenessSyncing

Resources

GitHub Repository

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