Provides access to bakery POS (Point of Sale) data stored in SQLite, enabling queries of transaction data, product information, sales analytics, and custom SQL execution against the database.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Bakery Data MCP Server show me the top 5 selling products from last 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.
An MCP (Model Context Protocol) server that provides access to bakery POS (Point of Sale) data stored in SQLite. This server enables Claude and other MCP clients to query transaction data, product information, and generate sales analytics.
This project imports bakery sales data from CSV files into a SQLite database and exposes it through an MCP server with powerful querying capabilities.
POS Transaction Journal (pos_journal_2023_2024.csv): Sales transactions from 2023-2024
Product Master (商品マスタ.csv): Product catalog with pricing and cost data
Product Master Extended (商品マスタ_タグ拡張版.csv): Product catalog with category tags
Department Master (部門マスタ.csv): Department/category definitions
Related MCP server: MCP Database Server
pip install mcpOr install in development mode:
pip install -e .Run the import script to create the database and load CSV data:
python import_data.pyThis will:
Create bakery_data.db SQLite database
Import all CSV files from the Data directory
Create indexes for better query performance
Display database statistics
Add the server to your Claude Desktop configuration file:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"bakery-data": {
"command": "python",
"args": [
"-m",
"bakery_data_mcp.server"
],
"cwd": "/absolute/path/to/bakery_data_mcp"
}
}
}Replace /absolute/path/to/bakery_data_mcp with the actual path to this project directory.
Restart Claude Desktop to load the new MCP server configuration.
The MCP server provides the following tools:
query_transactionsQuery POS transaction data with various filters.
Parameters:
start_date (optional): Start date (YYYY-MM-DD)
end_date (optional): End date (YYYY-MM-DD)
product_code (optional): Filter by product code
product_name (optional): Search product name (partial match)
payment_method (optional): Filter by payment method
min_amount / max_amount (optional): Amount range filter
limit (optional): Max results (default: 100)
query_productsQuery product master data.
Parameters:
plu_code (optional): Product PLU code
product_name (optional): Search product name (partial match)
department_id (optional): Filter by department
min_price / max_price (optional): Price range filter
tag (optional): Filter by product tag
include_tags (optional): Include tag data in results
limit (optional): Max results (default: 100)
query_departmentsQuery department master data.
Parameters:
department_id (optional): Department ID
department_name (optional): Search department name (partial match)
sales_summaryGet aggregated sales statistics.
Parameters:
start_date / end_date (optional): Date range
group_by (optional): Group by product, department, payment_method, date, or month
department_id (optional): Filter by department
limit (optional): Max results (default: 100)
top_productsGet top selling products.
Parameters:
start_date / end_date (optional): Date range
department_id (optional): Filter by department
metric (optional): Rank by quantity or revenue (default: revenue)
limit (optional): Number of top products (default: 10)
execute_sqlExecute custom SQL queries on the database.
Parameters:
query: SQL query to execute
params (optional): Query parameters for parameterized queries
⚠️ Use with caution: This allows arbitrary SQL execution. Use read-only queries when possible.
get_schemaGet database schema information including table structures and row counts.
Once configured, you can ask Claude questions like:
"What were the top 10 selling products in January 2024?"
"Show me all transactions paid with credit card over ¥1000"
"What's the total revenue by department for 2023?"
"Find all products tagged with '朝食向け' (breakfast)"
"What are the sales trends by month?"
departments: Department master data
department_id (PRIMARY KEY)
department_name
products: Product master data
plu_code (PRIMARY KEY)
department_id (FOREIGN KEY)
product_name
price
cost
cost_rate
products_extended: Product master with tags
Same as products plus:
tags (JSON array as text)
transactions: POS transaction journal
id (PRIMARY KEY, auto-increment)
transaction_number
datetime
product_code
product_name
unit_price
quantity
amount
payment_method
bakery_data_mcp/
├── Data/ # CSV data files
├── src/
│ └── bakery_data_mcp/
│ ├── __init__.py
│ └── server.py # MCP server implementation
├── schema.sql # Database schema
├── import_data.py # Data import script
├── pyproject.toml # Project configuration
├── bakery_data.db # SQLite database (generated)
└── README.mdFor testing, you can run the server directly:
python -m bakery_data_mcp.serverThe server communicates via stdio and expects MCP protocol messages.
MIT License
Execute a custom SQL query on the database. Use with caution. Read-only queries recommended.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | SQL query to execute. | |
| params | No | Parameters for the SQL query (for parameterized queries). Optional. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adds value by warning 'Use with caution' and recommending read-only queries, which hints at potential risks like data modification or performance issues. However, it doesn't detail specific behaviors such as permissions required, rate limits, or what happens with non-read-only queries, leaving gaps in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and front-loaded, consisting of two clear sentences that directly address the tool's function and a key caution. Every sentence earns its place without redundancy, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (executing arbitrary SQL queries) and the absence of both annotations and an output schema, the description is somewhat incomplete. It covers the basic purpose and a caution but lacks details on return values, error handling, or security implications, which are crucial for such a powerful tool. It's adequate but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, so the schema already documents both parameters ('query' and 'params') thoroughly. The description doesn't add any meaningful semantic details beyond what the schema provides, such as examples or constraints on query syntax. Thus, it meets the baseline but doesn't enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('execute') and resource ('custom SQL query on the database'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_schema' or 'query_transactions', which might also involve database queries, so it falls short of a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some guidance with 'Use with caution' and 'Read-only queries recommended', which implies cautionary context but doesn't specify when to use this tool versus alternatives like 'query_departments' or 'sales_summary'. It lacks explicit when/when-not rules or named alternatives, leaving usage somewhat ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the database schema information including table structures and column definitions.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states what the tool returns but doesn't disclose behavioral traits like whether it's read-only (implied by 'Get'), performance characteristics, error conditions, authentication needs, or format of returned information.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Get the database schema information') and adds specific details ('including table structures and column definitions') without any wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 0-parameter tool with no annotations and no output schema, the description provides basic purpose but lacks important context. It doesn't explain what format the schema information is returned in, whether it's comprehensive or filtered, or how it relates to the database context implied by sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0 parameters and 100% schema description coverage, the baseline is 4. The description appropriately doesn't discuss parameters since there are none, and the schema already fully documents the empty input structure.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get') and resources ('database schema information including table structures and column definitions'). It distinguishes from siblings like execute_sql (which executes queries) or query_* tools (which query specific data), but doesn't explicitly mention these distinctions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, timing considerations, or compare it to sibling tools like execute_sql for schema exploration versus data querying.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Query department master data. Returns all departments or filter by ID/name.
| Name | Required | Description | Default |
|---|---|---|---|
| department_id | No | Department ID. Optional. | |
| department_name | No | Department name to search (partial match). Optional. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool 'Returns all departments or filter by ID/name,' which implies read-only behavior and filtering capabilities. However, it lacks details on permissions, rate limits, pagination, error handling, or what 'master data' entails. For a query tool with zero annotation coverage, this is insufficient to fully inform the agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded: two sentences that directly state the tool's function and filtering options. There's no wasted verbiage, repetition, or unnecessary details. Every word earns its place, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (a query with filtering), no annotations, no output schema, and 100% schema coverage, the description is partially complete. It covers the basic purpose and parameters but lacks behavioral context and output details. It's adequate for a simple query tool but doesn't fully compensate for the missing annotations and output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds minimal value beyond the input schema. It states 'filter by ID/name,' which aligns with the schema's parameters (department_id and department_name). However, with 100% schema description coverage, the schema already documents these parameters well. The description doesn't provide additional context like format examples or usage tips, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Query department master data' specifies the verb (query) and resource (department master data). It distinguishes from siblings like query_products and query_transactions by focusing on departments. However, it doesn't explicitly differentiate from get_schema or execute_sql, which might also query data, so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose query_departments over execute_sql for department queries, or how it relates to siblings like get_schema. There's no context on prerequisites, exclusions, or typical use cases, leaving the agent with minimal direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Query product master data. Search by product code, name, department, price range, or tags.
| Name | Required | Description | Default |
|---|---|---|---|
| plu_code | No | Product PLU code. Optional. | |
| product_name | No | Product name to search (partial match). Optional. | |
| department_id | No | Department ID. Optional. | |
| min_price | No | Minimum price. Optional. | |
| max_price | No | Maximum price. Optional. | |
| tag | No | Search products by tag (uses extended product table). Optional. | |
| include_tags | No | Include tag information in results. Default: false. | |
| limit | No | Maximum number of results. Default: 100. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions search functionality but doesn't describe important behavioral aspects like whether this is a read-only operation, how results are returned (format, pagination), performance characteristics, or any limitations. The description is functional but lacks transparency about how the tool behaves beyond basic search.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - just two sentences that efficiently communicate the core functionality and search parameters. Every word earns its place with no wasted text. It's front-loaded with the main purpose and follows with specific search capabilities.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a query tool with 8 parameters, 100% schema coverage, but no annotations and no output schema, the description provides adequate basic information about what the tool does. However, it lacks important context about result format, limitations, or how this fits within the broader tool ecosystem. The description is complete enough to understand the tool's function but not complete enough for optimal agent usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 8 parameters thoroughly. The description adds minimal value by listing search criteria (product code, name, department, price range, tags) which aligns with some parameters, but doesn't provide additional semantic context beyond what's in the schema. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Query product master data' with specific search criteria (product code, name, department, price range, tags), which is a specific verb+resource combination. However, it doesn't explicitly distinguish this tool from sibling tools like 'query_departments' or 'query_transactions' in terms of data domain, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'top_products' or 'execute_sql'. It lists search criteria but doesn't indicate whether this is the primary product search tool or if there are specific scenarios where other tools might be more appropriate. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Query POS transaction data. Supports filtering by date range, product code/name, payment method, and amount range. Returns transaction details.
| Name | Required | Description | Default |
|---|---|---|---|
| start_date | No | Start date (YYYY-MM-DD format). Optional. | |
| end_date | No | End date (YYYY-MM-DD format). Optional. | |
| product_code | No | Product code to filter by. Optional. | |
| product_name | No | Product name to search (partial match). Optional. | |
| payment_method | No | Payment method (e.g., '現金', 'クレジット'). Optional. | |
| min_amount | No | Minimum transaction amount. Optional. | |
| max_amount | No | Maximum transaction amount. Optional. | |
| limit | No | Maximum number of results to return. Default: 100. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool 'Returns transaction details' but provides no information about permissions required, rate limits, pagination behavior (beyond the 'limit' parameter), error conditions, or what format the transaction details take. For a query tool with 8 parameters, this leaves significant behavioral aspects undocumented.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two sentences that efficiently convey the core functionality. The first sentence states the purpose and supported filters, while the second specifies the return type. There's no wasted text, though it could be slightly more structured by explicitly grouping related parameters or mentioning default behaviors.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a query tool with 8 optional parameters and no output schema, the description is moderately complete. It covers the basic purpose and filtering capabilities but lacks important context about the return format (what 'transaction details' includes), result ordering, error handling, and performance characteristics. With no annotations and no output schema, the agent has insufficient information about what to expect from this tool's behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description lists the filtering capabilities (date range, product code/name, payment method, amount range), which aligns with the 8 parameters in the schema. Since schema description coverage is 100%, the schema already documents all parameters thoroughly. The description adds minimal value beyond what's in the schema - it provides a high-level grouping of parameters but no additional semantic context about how filters combine or their precedence.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Query POS transaction data' specifies both the verb (query) and resource (POS transaction data). It distinguishes from siblings like 'query_products' or 'sales_summary' by focusing specifically on transaction data rather than products or aggregated sales. However, it doesn't explicitly contrast with 'execute_sql' which could also query transactions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'execute_sql' (which could query transactions directly), 'sales_summary' (which provides aggregated data), and 'query_products' (which focuses on products), there's no indication of when this filtered transaction query is preferable. The description only states what it does, not when to choose it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get sales summary statistics. Aggregate sales data by date range, product, department, or payment method.
| Name | Required | Description | Default |
|---|---|---|---|
| start_date | No | Start date (YYYY-MM-DD format). Optional. | |
| end_date | No | End date (YYYY-MM-DD format). Optional. | |
| group_by | No | How to group the results. Optional. | |
| department_id | No | Filter by department ID. Optional. | |
| limit | No | Maximum number of results. Default: 100. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a 'Get' operation implying read-only behavior, but doesn't mention important aspects like authentication requirements, rate limits, pagination behavior (beyond the limit parameter), error conditions, or what format the summary statistics are returned in. The description is minimal and leaves critical behavioral traits unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two sentences that efficiently convey the core functionality. The first sentence states the primary purpose, and the second elaborates on aggregation capabilities. There's no unnecessary verbiage, though it could be slightly more structured by separating different aspects of functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 5-parameter aggregation tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what 'summary statistics' actually means (totals, averages, counts?), doesn't mention how results are structured when grouped, and provides no information about response format or error handling. The description leaves too many contextual gaps for effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description adds marginal value by mentioning the aggregation dimensions (date range, product, department, payment method) which correspond to some parameters, but doesn't provide additional semantic context beyond what's in the schema descriptions. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Get sales summary statistics' with specific aggregation dimensions (date range, product, department, payment method). It uses a clear verb ('Get') and resource ('sales summary statistics'), but doesn't explicitly distinguish it from sibling tools like 'query_transactions' or 'top_products' which might overlap in sales data analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions aggregation capabilities but doesn't specify scenarios where this summary tool is preferred over raw data queries like 'query_transactions' or specialized tools like 'top_products'. No exclusions or prerequisites 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 selling products by quantity or revenue. Supports filtering by date range and department.
| Name | Required | Description | Default |
|---|---|---|---|
| start_date | No | Start date (YYYY-MM-DD format). Optional. | |
| end_date | No | End date (YYYY-MM-DD format). Optional. | |
| department_id | No | Filter by department ID. Optional. | |
| metric | No | Rank by quantity sold or total revenue. Default: revenue. | |
| limit | No | Number of top products to return. Default: 10. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions what the tool does but doesn't address important behavioral aspects: whether this is a read-only operation, potential rate limits, authentication requirements, error handling, or what format the results will be returned in. The description is functional but lacks operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two clear sentences. The first sentence states the core purpose, the second adds key capabilities. No wasted words, though it could be slightly more front-loaded with the most critical information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 5 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the output looks like (list format, fields returned), doesn't mention default behavior when parameters are omitted, and provides no context about performance characteristics or limitations. The description leaves too many operational questions unanswered.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description adds minimal value beyond the schema - it mentions filtering by date range and department, which the schema already covers. It doesn't provide additional context about parameter interactions or edge cases beyond what's in the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get top selling products by quantity or revenue' with specific verb+resource. It distinguishes itself from siblings like query_products or sales_summary by focusing on ranking products. However, it doesn't explicitly differentiate from all siblings (e.g., sales_summary might also provide ranking data).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like query_products, sales_summary, or execute_sql. It mentions filtering capabilities but doesn't specify scenarios where this tool is preferred over other data retrieval tools in the server.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
execute_sqlget_schemaquery_departmentsquery_productsquery_transactionssales_summarytop_productsMost 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.
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