What can you do with this server?

This DynamoDB Read-Only MCP server enables LLMs like Claude to query AWS DynamoDB data using natural language requests. You can: * Table Management: * List all available DynamoDB tables (list-tables) * Retrieve detailed information about a specific table (describe-table) * Data Access: * Scan all or part of a table's data with filtering options (scan-table) * Query data matching specific conditions, with support for indexes (query-table, paginate-query-table) * Retrieve a specific item by key (get-item) * Count items in a table, with optional filtering (count-items) * Resources: * Access metadata for all tables (dynamodb-tables-info) * Retrieve schema information for a specific table (dynamodb-table-schema) * Assistance: * Get help with writing DynamoDB queries (dynamodb-query-help)

Which integrations are available for this server?

Uses environment variables for AWS credential configuration, allowing secure connection to DynamoDB services without hardcoding sensitive information. Hosts the source code repository, providing version control and enabling users to clone, contribute to, or customize the DynamoDB MCP implementation. Supports configuration specifically for macOS systems, providing platform-specific file paths for Claude Desktop integration. Distributes the MCP server through npm, enabling easy installation and execution of the DynamoDB connection service. Leverages Zod library for parameter validation when processing requests to DynamoDB, ensuring properly formatted queries and parameters.

How do I use DynamoDB Read-Only 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., "@DynamoDB Read-Only MCP show me the last 10 orders from the Orders table" 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.

de en es ja ko ru zh

DynamoDB Read-Only MCP

by jjikky

Overview Schema Related Servers Score Discussions

JavaScript

Remote

DynamoDB Read-Only MCP

npm version

A server that utilizes the Model Context Protocol (MCP) to query AWS DynamoDB databases. This server allows LLMs like Claude to query DynamoDB data through natural language requests.

Features

This MCP server provides the following features:

Table Management Tools:
- list-tables: View a list of all DynamoDB tables
- describe-table: View detailed information about a specific table
Data Query Tools:
- scan-table: Scan all or part of a table's data
- query-table: Search for data that matches specific conditions in a table
- paginate-query-table: Retrieve data across multiple pages that matches specific conditions
- get-item: Retrieve an item with a specific key
- count-items: Calculate the number of items in a table
Resources:
- dynamodb-tables-info: A resource that provides metadata for all tables
- dynamodb-table-schema: A resource that provides schema information for a specific table
Prompts:
- dynamodb-query-help: A help prompt for writing DynamoDB queries

Related MCP server: Azure Cosmos DB MCP Server

Installation and Execution

You can run it without installation using the Run with NPX method below.

Installing via Smithery

To install DynamoDB Read-Only Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @jjikky/dynamo-readonly-mcp --client claude

Installation

Clone the repository:

git clone https://github.com/jjikky/dynamo-readonly-mcp.git
cd dynamo-readonly-mcp

Install the required packages:
```
npm install
```

Create a .env file and set up your AWS credentials:

AWS_ACCESS_KEY_ID=your_access_key
AWS_SECRET_ACCESS_KEY=your_secret_key
AWS_REGION=your_region

Build and Run

npm run build
npm start

Connect to Claude Desktop

To use this MCP server with Claude Desktop, you need to modify the Claude Desktop configuration file.

Open the Claude Desktop configuration file:
- macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
- Windows: %APPDATA%\Claude\claude_desktop_config.json

Add the server configuration as follows:

{
  "mcpServers": {
    "dynamodb-readonly": {
      "command": "node",
      "args": ["/absolute-path/dynamo-readonly-mcp/dist/index.js"],
      "env": {
        "AWS_ACCESS_KEY_ID": "your_access_key",
        "AWS_SECRET_ACCESS_KEY": "your_secret_key",
        "AWS_REGION": "your_region"
      }
    }
  }
}

Restart Claude Desktop.

Run with NPX

You can also run this server using npx without a global installation:

{
  "mcpServers": {
    "dynamodb-readonly": {
      "command": "npx",
      "args": ["-y", "dynamo-readonly-mcp"],
      "env": {
        "AWS_ACCESS_KEY_ID": "your_access_key",
        "AWS_SECRET_ACCESS_KEY": "your_secret_key",
        "AWS_REGION": "your_region"
      }
    }
  }
}

Usage Examples

You can ask Claude questions like:

"Can you tell me what tables are in DynamoDB?"
"Explain the structure of the Users table"
"Find the number of users in the 'Users' table where groupId is '0lxp4paxk7'"

Architecture

This MCP server consists of the following layered structure:

Client Interface (Claude Desktop) - Interaction between user and LLM
MCP Protocol Layer - Provides standardized message exchange method
DynamoDB Server - Implements functions that interact with DynamoDB
AWS SDK - Communicates with AWS DynamoDB service

Key Operation Mechanisms

1. Initialization and Connection

When the server starts, the following process occurs:

async function main() {
  const transport = new StdioServerTransport();
  await server.connect(transport);
  console.error('DynamoDB read-only MCP server is running...');
}

StdioServerTransport sets up a communication channel through standard input/output.
server.connect(transport) connects to Claude Desktop through the MCP protocol.
During connection, the server sends information about supported tools, resources, and prompts to the client.

2. Tool Request Processing

When a user asks Claude something like "Show me the list of DynamoDB tables":

Claude analyzes this request and calls the list-tables tool.
This request is sent to the server through the MCP protocol.
The server executes the corresponding tool handler:

server.tool('list-tables', 'Gets a list of all DynamoDB tables', {}, async () => {
  try {
    const tables = await listTables();
    return {
      content: [{ type: 'text', text: JSON.stringify(tables, null, 2) }],
    };
  } catch (error) {
    return { isError: true, content: [{ type: 'text', text: `Error: ${error.message}` }] };
  }
});

The result is returned to Claude through the MCP protocol.
Claude processes this result into natural language and presents it to the user.

3. Specific Parameter Handling

When a user requests "Tell me the structure of the Users table":

Claude determines that this request should use the describe-table tool.
Claude configures the parameter as { tableName: "Users" }.
This information is sent to the MCP server:

server.tool(
  'describe-table',
  'Gets detailed information about a DynamoDB table',
  {
    tableName: z.string().describe('Name of the table to get detailed information for'),
  },
  async ({ tableName }) => {
    // Query table information using the tableName parameter
    const tableInfo = await describeTable(tableName);
    // Return results
  }
);

Here, z.string() uses the Zod library to validate parameters.

4. Resource Handling

Resources are another MCP feature that provides read-only data:

server.resource('dynamodb-tables-info', 'DynamoDB table information', async () => {
  // Create and return resource data
  const tables = await listTables();
  const tablesInfo = await Promise.all(/* Query table information */);

  return {
    contents: [
      {
        uri: 'dynamodb://tables-info',
        text: JSON.stringify(tablesInfo, null, 2),
        mimeType: 'application/json',
      },
    ],
  };
});

Claude accesses resources and uses them as context information.

5. Prompt Handling

The MCP server can provide prompt templates for specific tasks:

server.prompt(
  'dynamodb-query-help',
  'A prompt that helps write DynamoDB queries',
  {
    tableName: z.string().describe('Table name to query'),
    queryType: z.enum(['basic', 'advanced']).default('basic'),
  },
  async ({ tableName, queryType }) => {
    // Generate prompt content
    return {
      messages: [
        {
          role: 'user',
          content: { type: 'text', text: helpContent },
        },
      ],
    };
  }
);

This prompt is used when a user requests "Show me how to write queries for the Users table."

Data Flow Summary

User makes a request to Claude in natural language
Claude analyzes the request and selects the appropriate MCP tool/resource/prompt
MCP client sends the request to the server in a standardized format
Server processes the request and calls the AWS DynamoDB API
DynamoDB returns results
Server converts results to MCP format and sends them to the client
Claude processes the results into natural language and presents them to the user

License

This project is licensed under the MIT License - see the LICENSE file for details.

Available Tools

7 tools

count-itemsB

Count items in a DynamoDB table

ParametersJSON Schema

Name	Required	Description
`expressionAttributeValues`	No	Filter expression attribute values (optional)
`filterExpression`	No	Filter expression (optional)
`tableName`	Yes	Table name

TDQS

B3.1/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It states what the tool does but doesn't mention performance characteristics (e.g., whether it's optimized for counting vs. full scans), error handling, or any constraints like rate limits or permissions required for DynamoDB operations.

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 a single, clear sentence with zero wasted words. It's appropriately sized and front-loaded with the essential information, making it highly efficient.

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

Completeness3/5

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

For a tool with 3 parameters, 100% schema coverage, and no output schema, the description is minimally adequate. It states the core purpose but lacks context about when to use it versus siblings, behavioral details, or output format, leaving gaps in completeness.

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 schema already documents all three parameters thoroughly. The description adds no additional meaning about parameters beyond implying filtering is possible, which is already covered in the schema. 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.

Purpose4/5

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

The description clearly states the action ('Count') and resource ('items in a DynamoDB table'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'scan-table' or 'query-table' which might also return counts, so it misses the top score.

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?

The description provides no guidance on when to use this tool versus alternatives. With siblings like 'scan-table' and 'query-table' that might offer similar functionality, there's no indication of when this specific counting tool is preferred or what its limitations are.

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

describe-tableC

Get detailed information about a DynamoDB table

ParametersJSON Schema

Name	Required	Description	Default
`tableName`	Yes	Name of the table to get details for

TDQS

C2.9/5.0

Behavior2/5

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 states the action ('Get detailed information') but doesn't reveal critical traits like whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or what 'detailed information' includes (e.g., schema, throughput, status). This leaves significant gaps for safe and effective tool invocation.

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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized for a simple tool and front-loaded with the core action, making it easy to parse quickly.

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

Completeness2/5

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

Given the complexity of DynamoDB operations and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'detailed information' entails (e.g., table properties, metrics), potential side effects, or error handling. For a tool interacting with a database system, more context is needed to ensure reliable use, especially with multiple sibling tools available.

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?

The input schema has 100% description coverage, with the single parameter 'tableName' clearly documented as 'Name of the table to get details for'. The description doesn't add any semantic context beyond this, such as format constraints or examples. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, though no extra value is provided.

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

Purpose4/5

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

The description clearly states the verb ('Get') and resource ('detailed information about a DynamoDB table'), making the purpose understandable. However, it doesn't differentiate from siblings like 'list-tables' (which lists table names) or 'get-item' (which retrieves specific items), leaving some ambiguity about what 'detailed information' specifically entails compared to other table-related tools.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention scenarios like needing metadata vs. data retrieval, or how it differs from siblings such as 'list-tables' for table enumeration or 'get-item' for item-level details. Without such context, an agent might struggle to choose appropriately among the six sibling tools.

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

get-itemC

Get an item from a DynamoDB table based on a specific key

ParametersJSON Schema

Name	Required	Description	Default
`key`	Yes	Item key (JSON format)
`tableName`	Yes	Table name

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden but offers minimal behavioral context. It states the action but doesn't disclose permissions needed, error conditions, rate limits, or what happens if the key doesn't exist. For a database read operation, this leaves significant gaps in understanding its behavior.

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 a single, efficient sentence with zero wasted words. It's appropriately sized and front-loaded with the core purpose, making it easy to parse quickly.

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

Completeness2/5

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

For a database read tool with no annotations and no output schema, the description is insufficient. It doesn't explain return values, error handling, or operational constraints. Given the complexity of DynamoDB operations and lack of structured metadata, more context is needed for effective use.

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 schema already documents both parameters fully. The description adds no additional meaning about parameter usage, constraints, or examples beyond what's in the schema. Baseline 3 is appropriate when the schema does all the work.

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

Purpose4/5

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

The description clearly states the verb ('Get') and resource ('an item from a DynamoDB table') with the specific mechanism ('based on a specific key'). It distinguishes from siblings like 'scan-table' or 'query-table' by emphasizing key-based retrieval, but doesn't explicitly contrast with all alternatives like 'paginate-query-table'.

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 is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, when-not scenarios, or compare with sibling tools like 'query-table' for more complex retrievals or 'scan-table' for full table scans.

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

list-tablesB

Get a list of all DynamoDB tables

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

B3.1/5.0

Behavior2/5

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 states what the tool does but doesn't cover critical aspects like whether it's read-only, requires authentication, has rate limits, or what the output format looks like (e.g., pagination, error handling). This leaves significant gaps for an agent.

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 a single, clear sentence with no wasted words, making it front-loaded and easy to parse. It efficiently conveys the core action without unnecessary elaboration, earning full marks for conciseness.

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

Completeness2/5

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

Given the complexity of DynamoDB operations and the lack of annotations and output schema, the description is incomplete. It doesn't explain behavioral traits, return values, or usage context, which are essential for an agent to operate this tool effectively in a real-world scenario.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate here. A baseline of 4 is applied as it adequately handles the lack of parameters without redundancy.

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

Purpose4/5

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

The description clearly states the verb ('Get') and resource ('list of all DynamoDB tables'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'describe-table' or 'scan-table' that also involve table operations, missing explicit distinction.

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 is provided on when to use this tool versus alternatives. For example, it doesn't specify if this is for metadata listing versus data retrieval (like 'scan-table') or if there are prerequisites such as permissions. The description lacks context for selection among siblings.

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

paginate-query-tableC

Paginate query results

ParametersJSON Schema

Name	Required	Description
`expressionAttributeValues`	Yes	Filter expression attribute values (JSON format)
`keyConditionExpression`	Yes	Key condition expression (e.g: 'PK = :pk')
`projectionExpression`	No	Projection expression (optional)
`tableName`	Yes	Table name

TDQS

C2.7/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden but offers minimal behavioral context. It mentions 'paginate' which implies handling large result sets, but doesn't disclose pagination mechanics (e.g., token-based, limit parameters), rate limits, authentication needs, or what constitutes a 'query' in this context. The description doesn't contradict annotations since none exist.

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 extremely concise at just three words, with zero wasted language. It's front-loaded with the core functionality ('paginate query results') and contains no unnecessary elaboration.

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

Completeness2/5

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

For a pagination tool with 4 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what 'paginate' means operationally, how results are returned, error conditions, or relationship to the 'query-table' sibling. The minimal description leaves too many contextual gaps.

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%, providing good documentation for all 4 parameters. The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline for high schema coverage without compensating value.

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

Purpose3/5

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

The description 'Paginate query results' states a general purpose but lacks specificity about what resource is being paginated (DynamoDB table query results) and how it differs from the sibling 'query-table' tool. It uses a clear verb ('paginate') but doesn't distinguish from alternatives.

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 is provided on when to use this tool versus the 'query-table' sibling or other query/scan alternatives. The description implies pagination functionality but doesn't specify prerequisites, when pagination is needed, or when other tools might be more appropriate.

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

query-tableC

Query items from a DynamoDB table based on conditions

ParametersJSON Schema

Name	Required	Description
`expressionAttributeValues`	Yes	Filter expression attribute values (JSON format)
`filterExpression`	No	Filter expression (optional)
`indexName`	No	Name of the index to use (optional)
`keyConditionExpression`	Yes	Key condition expression (e.g: 'PK = :pk')
`limit`	No	Maximum number of items to return
`projectionExpression`	No	Projection expression (optional)
`tableName`	Yes	Name of the table to query

TDQS

C2.9/5.0

Behavior2/5

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 states the tool queries items but doesn't mention whether it's read-only, if it has side effects, rate limits, authentication needs, or what the return format looks like. For a database query tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse quickly.

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

Completeness2/5

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

Given the complexity of a DynamoDB query tool with 7 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain return values, error conditions, or behavioral constraints, leaving the agent with insufficient context to use the tool effectively beyond basic parameter passing.

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?

The schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description adds no additional parameter semantics beyond implying 'conditions' relate to the filter and key condition expressions. This meets the baseline of 3 since the schema does the heavy lifting, but the description doesn't compensate with extra context.

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

Purpose4/5

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

The description clearly states the action ('Query items') and resource ('from a DynamoDB table') with the purpose ('based on conditions'), making it easy to understand what the tool does. It distinguishes from siblings like 'scan-table' by specifying querying rather than scanning, though it doesn't explicitly differentiate from 'paginate-query-table' which might be a related operation.

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?

The description provides no guidance on when to use this tool versus alternatives like 'scan-table' or 'paginate-query-table'. It mentions conditions but doesn't specify that this is for key-based queries in DynamoDB, leaving the agent to infer usage from the tool name and parameters alone.

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

scan-tableC

Scan items from a DynamoDB table

ParametersJSON Schema

Name	Required	Description
`expressionAttributeValues`	No	Filter expression attribute values (JSON format)
`filterExpression`	No	Filter expression (e.g: 'age > :minAge')
`limit`	No	Maximum number of items to return (default: 20)
`projectionExpression`	No	Projection expression (e.g: "id")
`tableName`	Yes	Name of the table to scan

TDQS

C2.9/5.0

Behavior2/5

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. 'Scan' implies a read operation, but it doesn't specify whether this is paginated, has performance implications for large tables, requires specific permissions, or what the output format looks like. For a DynamoDB operation with potential complexity, this leaves significant gaps in understanding tool behavior.

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 extremely concise - a single sentence that directly states the tool's purpose without any unnecessary words. It's perfectly front-loaded and every word earns its place, making it efficient for an agent to parse.

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

Completeness2/5

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

For a DynamoDB scanning tool with 5 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what 'scanning' means in DynamoDB context (sequential reads vs. query efficiency), doesn't mention performance considerations for large tables, and provides no information about return format or pagination behavior.

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 all parameters are documented in the schema itself. The description doesn't add any meaningful parameter semantics beyond what's already in the schema - it doesn't explain relationships between parameters like how 'filterExpression' works with 'expressionAttributeValues' or typical scanning patterns. Baseline 3 is appropriate when the schema does the heavy lifting.

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

Purpose4/5

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

The description clearly states the action ('scan') and resource ('items from a DynamoDB table'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'query-table' or 'paginate-query-table' which might also retrieve data from DynamoDB tables, missing an opportunity for sibling distinction.

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?

The description provides no guidance on when to use this tool versus alternatives like 'query-table' or 'get-item'. It doesn't mention typical use cases for scanning (e.g., full table reads vs. indexed queries) or any prerequisites, leaving the agent to infer usage from context alone.

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

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TDQS

B3.4/5.0

Disambiguation5/5

Every tool has a clearly distinct purpose with no ambiguity: count-items focuses on counting, describe-table on metadata, get-item on single retrieval, list-tables on table enumeration, paginate-query-table on pagination, query-table on conditional queries, and scan-table on full scans. The descriptions clearly differentiate each tool's function, making misselection unlikely.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with hyphens (e.g., count-items, describe-table, get-item). There are no deviations in style or convention, making the naming predictable and easy to understand across the entire set.

Tool Count5/5

With 7 tools, the count is well-scoped for a read-only DynamoDB server, covering essential operations like listing, describing, retrieving, querying, scanning, and counting. Each tool earns its place without redundancy, fitting within the typical 3-15 range for focused server purposes.

Completeness4/5

The tool set provides comprehensive read-only coverage for DynamoDB, including table listing, metadata retrieval, item access, queries, scans, and pagination. A minor gap exists in not supporting batch operations like batch-get-item, but agents can work around this with existing tools, and the core read workflows are well-covered.

Maintenance

ActivityInactive

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