Bakery Data MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| query_transactionsC | Query POS transaction data. Supports filtering by date range, product code/name, payment method, and amount range. Returns transaction details. |
| query_productsB | Query product master data. Search by product code, name, department, price range, or tags. |
| query_departmentsB | Query department master data. Returns all departments or filter by ID/name. |
| sales_summaryC | Get sales summary statistics. Aggregate sales data by date range, product, department, or payment method. |
| top_productsC | Get top selling products by quantity or revenue. Supports filtering by date range and department. |
| execute_sqlA | Execute a custom SQL query on the database. Use with caution. Read-only queries recommended. |
| get_schemaB | Get the database schema information including table structures and column definitions. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 7 tools
Most tools have clearly distinct purposes, with execute_sql for custom queries, get_schema for metadata, and specific query tools for different data types (departments, products, transactions). However, sales_summary and top_products could potentially overlap in functionality, as both provide aggregated sales insights, which might cause minor confusion in tool selection.
The naming follows a consistent verb_noun pattern throughout, such as execute_sql, get_schema, query_departments, query_products, query_transactions, sales_summary, and top_products. There is a minor deviation with sales_summary and top_products using a noun-based naming style instead of a verb prefix, but overall the pattern is predictable and readable.
With 7 tools, the count is well-scoped for a bakery data server, covering essential operations like custom queries, schema inspection, data querying, and analytics. Each tool earns its place without feeling excessive or insufficient, aligning well with the server's purpose of data management and analysis.
The tool set provides comprehensive coverage for querying and analyzing bakery data, including schema access, master data queries, transaction details, and sales analytics. A minor gap exists in the lack of data modification tools (e.g., update or insert operations), but this is reasonable given the read-only nature implied by descriptions, and agents can work around this for most analytical workflows.