AWS MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Every tool has a clearly distinct purpose with no ambiguity. The DynamoDB tools are precisely scoped to specific operations (batch_execute, batch_get, describe_ttl, etc.), and S3 tools are similarly well-defined (bucket_create, object_read, etc.). There is no overlap where an agent would struggle to choose between tools.
Naming Consistency5/5
All tool names follow a consistent pattern of service_operation format (e.g., dynamodb_item_get, s3_object_upload). The naming is uniformly snake_case with clear verbs (get, put, delete, list, create, update) that match the operations precisely, making it highly predictable and readable.
Tool Count4/5
With 23 tools, the count is slightly high but reasonable for covering two major AWS services (DynamoDB and S3). It provides comprehensive operations for each service, though it might feel heavy for a single server. The scope is well-defined, and each tool serves a distinct function, justifying its inclusion.
Completeness5/5
The tool set offers complete CRUD/lifecycle coverage for both DynamoDB and S3 domains. For DynamoDB, it includes table management (create, describe, list, update, delete) and item operations (get, put, update, delete, query, scan, batch operations, TTL). For S3, it covers bucket management (create, list, delete) and object operations (upload, read, list, delete). No obvious gaps exist for core workflows.
Average 2.9/5 across 23 of 23 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
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- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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{
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  "maintainers": [
    "your-github-username"
  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It mentions 'execute' but does not clarify if this includes mutations, requires specific permissions, has atomicity guarantees, or handles errors. This leaves significant gaps for a tool that likely involves database 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, efficient sentence with no wasted words. It is front-loaded and directly states the tool's function, making it highly concise and well-structured.
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 batch database operations, no annotations, and no output schema, the description is insufficient. It lacks details on behavior, error handling, return values, and usage context, making it incomplete 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.
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 both parameters ('statements' and 'parameters') adequately. The description adds no additional meaning beyond what the schema provides, such as syntax examples or constraints, resulting in a baseline score.
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 ('execute') and resource ('multiple PartiQL statements in a batch'), making the purpose understandable. However, it does not explicitly differentiate from siblings like 'dynamodb_item_batch_write' or 'dynamodb_item_update', which could also involve batch operations, leaving some ambiguity.
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 does not specify if this is for read-only queries, write operations, or mixed batches, nor does it mention prerequisites or compare to sibling tools like 'dynamodb_item_batch_write'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. While 'batch get' implies a read operation, the description doesn't mention important behavioral aspects like rate limits, error handling for partial failures, response format, or whether this operation is eventually consistent by default. This leaves significant gaps for an agent trying to use the tool effectively.
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 communicates the core functionality without any wasted words. It's appropriately sized for a tool with a clear primary function and good schema documentation.
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 batch operation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the tool returns, how errors are handled, performance characteristics, or limitations. Given the complexity implied by batch operations and the lack of structured metadata, the description should provide more complete context 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?
With 100% schema description coverage, the schema already documents the single 'request_items' parameter thoroughly. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain the structure of the keys, provide examples, or clarify how multiple tables are handled. 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('batch get') and resource ('multiple items from DynamoDB tables'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'dynamodb_item_get' (single-item retrieval) or 'dynamodb_item_query' (query-based retrieval), which would have earned 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.
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 multiple DynamoDB retrieval tools available (item_get, query, scan), the agent receives no help in selecting batch_get over these other options, nor any information about prerequisites or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 retrieves TTL settings, implying a read-only operation, but doesn't specify required permissions, error conditions (e.g., if the table doesn't exist), rate limits, or the format of returned data. This leaves significant gaps for an agent to understand the tool's 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 is front-loaded with the core action and resource, making it easy to parse. Every part of the sentence earns its place by conveying essential information.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what TTL settings are returned (e.g., status, attribute name), potential errors, or dependencies. For a tool that retrieves configuration data, more context about the output and operational constraints would help an agent use it effectively.
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 'table_name' clearly documented. The description adds no additional semantic context beyond what the schema provides, such as table naming conventions or validation rules. With high schema coverage, the baseline score of 3 is appropriate as 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 ('Get') and resource ('TTL settings for a table'), making the purpose immediately understandable. It distinguishes itself from siblings like 'dynamodb_update_ttl' by focusing on retrieval rather than modification. However, it doesn't explicitly contrast with 'dynamodb_table_describe', which might provide broader table metadata.
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 prerequisites (e.g., table must exist), when not to use it, or how it differs from related tools like 'dynamodb_table_describe' that might also include TTL information. Usage is implied by the name but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. It states the tool performs 'batch write operations' which implies mutation, but lacks details on permissions, rate limits, error handling, or atomicity of operations. For a mutation tool with zero annotation coverage, this is a significant gap 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core functionality without unnecessary words. It directly communicates the tool's purpose in a compact form, making it easy to parse and understand 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 mutation tool with no annotations and no output schema, the description is incomplete. It doesn't address critical aspects like what the tool returns, error conditions, or performance implications of batch operations. Given the complexity and lack of structured data, 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 all parameters thoroughly. The description adds no additional meaning beyond what's in the schema, such as explaining the structure of 'items' or when 'key_attributes' is required. 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 tool performs 'batch write operations (put/delete) for DynamoDB items,' specifying both the action (batch write) and resource (DynamoDB items). It distinguishes from read-only siblings like dynamodb_item_get or dynamodb_item_query by mentioning write operations, but doesn't explicitly differentiate from dynamodb_batch_execute or other write 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?
No guidance is provided on when to use this tool versus alternatives like dynamodb_item_put, dynamodb_item_delete, or dynamodb_batch_execute. The description mentions 'batch' but doesn't clarify thresholds or scenarios where batch operations are preferred over individual ones, leaving usage context implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. While 'Delete' implies a destructive mutation, the description doesn't specify whether this operation is irreversible, what permissions are required, if there are rate limits, or what happens on success/failure (e.g., error handling). This leaves significant gaps for a destructive operation.
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. It's front-loaded with the core action and resource, 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive tool with no annotations and no output schema, the description is insufficient. It lacks critical context such as the irreversible nature of deletion, required permissions, error conditions, or what the tool returns (e.g., success confirmation or error details). This leaves the agent under-informed about risks and outcomes.
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%, with both parameters ('table_name' and 'key') well-documented in the schema. The description doesn't add any semantic details beyond what the schema provides, such as examples of key structures or table naming conventions, but the schema adequately covers the basics.
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 ('Delete') and resource ('an item from a DynamoDB table'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'dynamodb_item_batch_write' or 'dynamodb_table_delete', which could also perform deletion operations in different contexts.
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 about when to use this tool versus alternatives. The description doesn't mention sibling tools like 'dynamodb_item_batch_write' for batch deletions or 'dynamodb_table_delete' for table-level deletions, nor does it specify prerequisites such as needing the item's key or appropriate permissions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 insight. It states it's a read operation ('Get'), but doesn't cover error handling, permissions needed, performance characteristics, or what happens if the item doesn't exist. For a database operation with no annotation coverage, this is insufficient.
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 gets straight to the point with zero wasted words. It's appropriately sized for a simple retrieval operation.
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 operation with no annotations and no output schema, the description is inadequate. It doesn't explain what format the returned item will be in, what happens on errors, or any constraints like required IAM permissions. Given the complexity of DynamoDB operations, more context is needed.
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 thoroughly. The description doesn't add any additional meaning about the parameters beyond what's in the schema, meeting the baseline for high schema coverage.
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 ('Get') and resource ('an item from a DynamoDB table'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like dynamodb_item_query or dynamodb_item_scan, which also retrieve items but with different approaches.
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 that this is for retrieving a single item by its exact key, as opposed to querying or scanning for multiple items, which are available as sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 action ('Put') but doesn't mention critical behaviors like whether this overwrites existing items, requires specific permissions, has rate limits, or returns any output. For a mutation tool, this leaves significant gaps in understanding its effects.
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, direct sentence with zero wasted words, making it highly concise and front-loaded. It efficiently conveys the core purpose without unnecessary elaboration, which is ideal for quick comprehension.
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 tool's complexity as a mutation operation with no annotations and no output schema, the description is insufficient. It doesn't explain what happens on success or failure, whether items are overwritten, or any error conditions. For a DynamoDB write tool, this leaves the agent under-informed about critical operational aspects.
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 clear documentation for 'table_name' and 'item'. The description doesn't add any semantic details beyond what the schema provides, such as item format examples or constraints. This meets the baseline for high schema coverage but doesn't enhance parameter understanding.
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 ('Put') and target ('an item into a DynamoDB table'), which is specific and unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'dynamodb_item_update' or 'dynamodb_item_batch_write', which reduces clarity about when to choose this specific tool over 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?
The description provides no guidance on when to use this tool versus alternatives like 'dynamodb_item_update' for modifications or 'dynamodb_item_batch_write' for multiple items. It lacks context about prerequisites, such as table existence, or exclusions, leaving the agent to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 it's a query operation, implying it's read-only and non-destructive, but doesn't cover important aspects like performance implications (e.g., query vs. scan efficiency), pagination handling, error conditions, or authentication requirements. 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 extremely concise—a single, direct sentence: 'Query items in a DynamoDB table.' It's front-loaded with the core action and resource, with zero wasted words. This makes it easy to parse quickly, though it sacrifices detail for brevity.
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 queries, the lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., items, metadata, pagination tokens), error handling, or how it differs from similar tools like 'scan'. For a tool with three required parameters and no structured behavioral hints, more context is needed to use it effectively.
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 clear documentation for each parameter (table_name, key_condition, expression_values). The description adds no additional semantic context beyond what's in the schema—it doesn't explain what a 'key condition expression' entails, how to format it, or provide examples. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, but the description doesn't enhance parameter understanding.
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 'Query items in a DynamoDB table' clearly states the action (query) and resource (items in a DynamoDB table), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'dynamodb_item_scan' or 'dynamodb_item_get', which are also read operations on DynamoDB items, leaving some ambiguity about when to choose this specific query method.
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 that 'dynamodb_item_query' is for querying items based on primary key conditions, unlike 'dynamodb_item_scan' for full table scans or 'dynamodb_item_get' for retrieving a single item by exact key. There are no prerequisites, exclusions, or context provided for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 mentions scanning but fails to describe key behaviors: whether it's read-only or mutative, potential performance impacts (e.g., scanning entire tables), rate limits, or authentication needs. For a tool with no annotation coverage, this leaves significant gaps in understanding its operational traits.
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 front-loads the core action ('Scan items') without unnecessary words. It's appropriately sized for the tool's complexity, with zero waste, making it highly concise and well-structured.
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 tool's complexity (3 parameters, nested objects, no output schema, and no annotations), the description is inadequate. It lacks details on return values, error handling, or behavioral nuances like scanning limitations. Without annotations or output schema, the description should provide more context to be complete, but it falls short.
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 67%, with parameters like 'table_name' well-documented but 'filter_expression' and 'expression_attributes' only partially described. The description adds no additional parameter semantics beyond the schema, such as examples or usage tips. Given the moderate coverage, the baseline of 3 is appropriate as the description doesn't compensate for gaps.
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 ('Scan') and resource ('items in a DynamoDB table'), making the purpose immediately understandable. It distinguishes from siblings like 'dynamodb_item_query' by specifying the scan operation, though it doesn't explicitly contrast them. The description is specific but lacks explicit sibling differentiation, warranting a 4.
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 'dynamodb_item_query' or 'dynamodb_item_get', nor does it mention prerequisites or context for scanning. It simply states what the tool does without indicating appropriate scenarios, resulting in minimal usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. While 'Update' implies a mutation, it lacks details on permissions needed, whether it's idempotent, error handling (e.g., if the item doesn't exist), or side effects. This is inadequate for a mutation tool with zero annotation coverage.
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 waste. It is front-loaded and directly communicates the core functionality without 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?
Given the complexity of a DynamoDB update operation, no annotations, and no output schema, the description is insufficient. It should address behavioral aspects like atomic updates, conditional writes, or return values to help an agent use it correctly.
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%, with clear descriptions for 'table_name', 'key', and 'item'. The description does not add any semantic details beyond what the schema provides, such as explaining how 'key' identifies the item or what 'item' should contain. 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 ('Update') and resource ('an item in a DynamoDB table'), making the purpose immediately understandable. However, it does not differentiate this tool from its sibling 'dynamodb_item_put' or 'dynamodb_table_update', which could cause confusion about when to use each.
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 'dynamodb_item_put' (for creating/overwriting items) or 'dynamodb_table_update' (for modifying table settings). Without such context, an agent might misuse the tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 'Create' which implies a write/mutation operation, but doesn't cover critical aspects like whether this is idempotent, what permissions are required, potential costs, rate limits, or what happens on failure (e.g., if the table already exists). For a cloud resource creation tool, this is a significant gap 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states the core purpose without any wasted words. It's appropriately sized for a tool with a clear name and well-documented schema, 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of creating a DynamoDB table (a cloud resource with cost and permission implications), no annotations, and no output schema, the description is insufficient. It doesn't address behavioral aspects, error handling, or what the tool returns upon success/failure. The agent would need to guess about important operational details.
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%, with all three parameters clearly documented in the input schema. The description adds no additional parameter information beyond what's in the schema (e.g., no examples, format details, or constraints). According to the rules, when schema coverage is high (>80%), the baseline score is 3 even without param info in the description.
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 ('Create') and resource ('a new DynamoDB table'), making the purpose immediately understandable. It distinguishes from siblings like 'dynamodb_table_delete' or 'dynamodb_table_update' by specifying creation. However, it doesn't explicitly differentiate from 's3_bucket_create' or other resource creation tools, which keeps it from 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.
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 prerequisites (e.g., needing proper AWS permissions), when to choose this over updating an existing table, or how it relates to sibling tools like 'dynamodb_table_list' for checking existing tables. This leaves the agent with minimal context for decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. 'Delete a DynamoDB table' implies a destructive, irreversible operation, but it fails to specify critical details: whether authentication is required, if there are rate limits, what happens to the data (permanent loss), or if there's a confirmation step. For a high-risk mutation tool, this is a significant gap 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.
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 front-loaded with the core action ('Delete') and resource ('DynamoDB table'), making it immediately scannable. Every word earns its place in conveying the essential purpose.
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 this is a destructive mutation tool with no annotations and no output schema, the description is incomplete. It doesn't address the high-risk nature of the operation, expected outcomes, error conditions, or how it differs from similar tools like 'dynamodb_item_delete'. For a tool that permanently destroys resources, more context is needed to use it safely and correctly.
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 'table_name' clearly documented. The description adds no additional parameter information beyond what's in the schema. According to scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description.
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 ('Delete') and resource ('a DynamoDB table'), making the purpose immediately understandable. It distinguishes this tool from sibling tools like 'dynamodb_item_delete' by specifying table-level deletion rather than item-level. However, it doesn't explicitly contrast with 'dynamodb_table_update' or 'dynamodb_table_describe', which would have made it 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.
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 prerequisites (e.g., table must exist), consequences (e.g., data loss), or when to choose 'dynamodb_table_delete' over 'dynamodb_item_delete' for removing data. The agent must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 'Get details,' which implies a read-only operation, but doesn't clarify aspects like required permissions, rate limits, error handling, or what specific details are returned (e.g., table schema, status, metrics). For a tool with no annotations, 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, direct sentence with zero waste—'Get details about a DynamoDB table'—making it highly concise and front-loaded. Every word contributes to understanding the tool's purpose without 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?
Given the complexity of DynamoDB operations and the lack of annotations and output schema, the description is insufficiently complete. It doesn't explain what details are returned (e.g., table attributes, throughput settings), potential errors, or dependencies. For a tool that retrieves metadata in a cloud service context, more context is needed to guide 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?
The input schema has 100% description coverage, with the single parameter 'table_name' clearly documented. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
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 ('Get details about') and resource ('a DynamoDB table'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'dynamodb_table_list' or 'dynamodb_describe_ttl', but the verb 'describe' is specific enough to imply retrieving metadata rather than listing or modifying.
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 siblings like 'dynamodb_table_list' (for listing tables) or 'dynamodb_describe_ttl' (for TTL-specific details), nor does it specify prerequisites such as needing an existing table. Usage is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 insight. It states 'Update' which implies a mutation operation, but doesn't disclose critical details like required permissions, whether updates are atomic/reversible, potential downtime during schema changes, or error conditions. For a table-level update tool with zero annotation coverage, this is inadequate.
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 maximally concise with a single, clear sentence that states the core functionality. There's no wasted verbiage or unnecessary elaboration. The structure is front-loaded with the essential information.
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 table mutation tool with no annotations and no output schema, the description is insufficiently complete. It doesn't address what happens during the update, what the response contains, potential side effects on existing data, or how this differs from item-level updates. The agent lacks crucial context for safe and 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%, providing complete parameter documentation in the structured schema. The description adds no additional parameter semantics beyond what's already in the schema - it doesn't explain the relationship between 'table_name' and 'attribute_definitions', provide examples of attribute definitions, or clarify what 'updated' means in this context. 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 verb 'Update' and the resource 'DynamoDB table', making the purpose immediately understandable. It distinguishes from siblings like 'dynamodb_table_create' and 'dynamodb_table_delete' by specifying the update action. However, it doesn't specify what aspects of the table are updated beyond what's implied by parameters.
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 (e.g., table must exist), when to choose this over 'dynamodb_item_update' for data modifications, or any limitations compared to sibling tools like 'dynamodb_table_describe' for metadata retrieval.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. 'Update' implies a mutation operation, but the description doesn't specify whether this requires specific permissions, if changes are reversible, potential impacts on existing data, or error conditions. For a mutation tool with zero annotation coverage, this leaves critical behavioral traits undocumented.
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. Every word earns its place, with no redundancy or fluff.
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 mutation tool (updating TTL settings in DynamoDB) with no annotations and no output schema, the description is insufficient. It doesn't explain what TTL settings are, the implications of enabling/disabling TTL, expected return values, or error handling. For a tool that modifies database behavior, more context is needed for safe and 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%, with clear descriptions for all three parameters. The description adds no additional parameter semantics beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.
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 ('Update') and resource ('TTL settings for a table'), making the purpose immediately understandable. It distinguishes from siblings like 'dynamodb_describe_ttl' (read vs. write) and 'dynamodb_table_update' (general vs. TTL-specific), though it doesn't explicitly name these alternatives. The description is specific but could be more precise about what TTL settings are being modified.
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 prerequisites (e.g., table must exist), when to use 'dynamodb_table_update' for other settings, or when 'dynamodb_describe_ttl' might be needed first. Without such context, an agent must infer usage from the tool name and schema alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 action ('create') but doesn't cover critical aspects like required AWS permissions, potential costs, region-specific constraints, or error handling. For a mutation 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 with no wasted 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 this is a mutation tool (creating resources) with no annotations and no output schema, the description is incomplete. It doesn't address behavioral aspects like permissions, costs, or what happens on success/failure, which are crucial for safe and effective use in an AWS context.
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%, with the single parameter 'bucket_name' well-documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema, such as naming conventions or constraints, so it meets the baseline for high schema coverage without extra value.
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 'Create a new S3 bucket' clearly states the action (create) and resource (S3 bucket), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'dynamodb_table_create' or explain what distinguishes an S3 bucket from other AWS resources, keeping it from 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.
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 prerequisites (e.g., AWS permissions, region considerations), when not to use it (e.g., for existing buckets), or refer to sibling tools like 's3_bucket_list' for checking existing buckets first.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. 'Delete an S3 bucket' implies a destructive mutation but doesn't specify critical details: whether this operation is irreversible, what happens to bucket contents, required permissions, error conditions, or rate limits. For a destructive operation with zero annotation coverage, this is a significant gap.
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 action and resource, making it immediately understandable despite its brevity.
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 destructive deletion tool with no annotations and no output schema, the description is inadequate. It doesn't explain what happens to bucket contents, whether deletion is permanent, what permissions are required, or what the response looks like. Given the high-stakes nature of bucket deletion, more context is needed.
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%, with the single parameter 'bucket_name' fully documented in the schema. The description doesn't add any parameter semantics beyond what the schema already provides, so it meets the baseline for high schema coverage without adding value.
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 ('Delete') and target resource ('an S3 bucket'), providing specific verb+resource pairing. However, it doesn't distinguish this tool from sibling tools like 's3_object_delete' or 'dynamodb_table_delete', which reduces its differentiation value.
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 's3_object_delete' or 'dynamodb_table_delete'. There's no mention of prerequisites (e.g., bucket must be empty), consequences, or appropriate contexts for bucket deletion versus other deletion operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. It states 'Delete' which implies a destructive mutation, but doesn't disclose critical behavioral traits like whether deletion is permanent, requires specific IAM permissions, has rate limits, or what happens on success/failure. For a destructive operation with zero annotation coverage, this is a significant gap.
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 front-loaded with the core action and resource, making it easy to parse quickly. Every word earns its place.
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 this is a destructive mutation tool with no annotations and no output schema, the description is incomplete. It doesn't cover behavioral aspects like permanence, permissions, error handling, or return values. For a tool that permanently deletes data, more context is needed to use it safely and effectively.
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%, with both parameters ('bucket_name', 'object_key') clearly documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline of 3 where 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 ('Delete') and resource ('an object from S3'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 's3_bucket_delete' or 'dynamodb_item_delete', which would require mentioning it targets individual objects rather than buckets or database items.
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 prerequisites (e.g., needing appropriate permissions), when not to use it (e.g., for batch deletions), or refer to related tools like 's3_object_list' for finding objects to delete.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 ('List objects') but lacks critical details: it does not mention pagination, rate limits, permissions required (e.g., S3:ListBucket), return format (e.g., object keys, sizes), or error conditions (e.g., bucket not found). This leaves significant gaps for an agent to invoke it correctly.
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, direct sentence ('List objects in an S3 bucket') with zero wasted words. It is front-loaded and efficiently conveys the core purpose without unnecessary elaboration, making it highly concise and well-structured.
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 tool's complexity (listing objects in cloud storage), lack of annotations, and no output schema, the description is incomplete. It omits behavioral traits (e.g., pagination, permissions), usage context, and return value details, which are essential for an agent to operate effectively in a real-world S3 environment.
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 'bucket_name' clearly documented. The description adds no additional parameter semantics beyond what the schema provides (e.g., bucket naming rules, region implications). Since schema coverage is high, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract.
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 'List objects in an S3 bucket' clearly states the verb ('List') and resource ('objects in an S3 bucket'), making the purpose immediately understandable. However, it does not differentiate from sibling tools like 's3_bucket_list' (which lists buckets, not objects) or specify scope (e.g., all objects vs. filtered), preventing 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.
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 does not mention sibling tools like 's3_object_read' (for reading specific objects) or 's3_object_delete' (for deletion), nor does it specify prerequisites (e.g., bucket existence) or use cases (e.g., inventory checks).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 it reads content but doesn't disclose authentication requirements, rate limits, error handling, return format, or whether it streams or loads entire objects into memory. For a read operation with zero annotation coverage, this is inadequate.
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 front-loaded with the core action and resource, making it immediately scannable and appropriately sized for a simple tool.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'content' means (e.g., binary data, text), how large objects are handled, or what the return value looks like. For a read operation with no structured safety or output information, more context is needed.
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%, with both parameters clearly documented in the schema. The description doesn't add any additional meaning beyond what's in the schema (e.g., format examples, constraints), so it meets the baseline of 3 where 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 ('Read') and resource ('object's content from S3'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like s3_object_list or s3_object_delete, but the verb 'Read' versus 'list' or 'delete' provides implicit 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. The description doesn't mention prerequisites, error conditions, or comparison to sibling tools like s3_object_list (for metadata) or s3_object_upload (for writing).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 only states the basic action. It doesn't disclose critical behaviors like authentication requirements, file size limits, overwrite policies, error conditions, or response format. For a write operation with potential side effects, this is a significant gap 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence with zero wasted words. It's front-loaded with the core action and resource, making it immediately scannable and efficient. Every word earns its place.
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 write operation with no annotations and no output schema, the description is incomplete. It doesn't address mutation implications, success/failure responses, or operational constraints. Given the complexity of S3 uploads (e.g., permissions, encoding), more context is needed for safe and 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 fully documents all three parameters. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain format expectations for object_key or constraints on file_content). 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 action ('Upload') and resource ('an object to S3'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 's3_object_delete' or 's3_object_read' beyond the obvious verb difference, missing an opportunity to clarify its specific role in the S3 toolset.
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 mention prerequisites like bucket existence, compare to batch operations, or indicate if it's for single-file uploads only. This leaves the agent to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. 'List all DynamoDB tables' implies a read-only operation, but it doesn't disclose behavioral traits like whether it requires specific IAM permissions, if it's paginated (likely for many tables), if it includes deleted tables, rate limits, or what the return format looks like. For a tool with zero annotation coverage, this is a significant gap.
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 waste: 'List all DynamoDB tables'. It's front-loaded and efficiently communicates the core purpose without unnecessary words. Every word earns its place.
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 (listing all tables in a cloud database service), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what information is returned (e.g., table names, ARNs, status), whether results are filtered or paginated, or any AWS-specific context like region scoping. For a tool in this context, more detail is needed.
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% description coverage, so no parameters need documentation. The description doesn't add parameter information, which is appropriate here. A baseline of 4 is given since there are no parameters to explain, and the description doesn't create confusion by mentioning nonexistent parameters.
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 'List all DynamoDB tables' clearly states the verb ('List') and resource ('DynamoDB tables'), making the purpose immediately understandable. It distinguishes from siblings like dynamodb_table_describe (which describes a specific table) and dynamodb_table_create/delete/update (which modify tables). However, it doesn't explicitly mention that this lists tables across all regions or accounts, which could be implied but isn't specified.
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 when to use dynamodb_table_list versus dynamodb_item_query or dynamodb_item_scan for data retrieval, or versus dynamodb_table_describe for table metadata. There's no context about prerequisites, permissions needed, or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. 'List all S3 buckets' implies a read-only operation but doesn't disclose behavioral traits like whether it requires specific permissions, returns paginated results, includes metadata, or has rate limits. For a tool with zero annotation coverage, this is inadequate.
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 for a simple tool and front-loads the core functionality immediately.
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 tool's simplicity (0 parameters, no output schema) but lack of annotations, the description is incomplete. It doesn't address what the list returns (e.g., bucket names, ARNs, creation dates), potential errors, or behavioral context needed for an AI agent to use it effectively without annotations.
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 tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the absence of inputs. The description doesn't need to add parameter details, and it correctly implies no filtering or options are available, earning a baseline score of 4 for this dimension.
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 ('List') and resource ('all S3 buckets'), making the purpose immediately understandable. However, it doesn't distinguish this tool from its sibling 's3_object_list' which lists objects within a bucket rather than buckets themselves, so it misses full sibling differentiation.
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 's3_object_list' or 'dynamodb_table_list'. It doesn't mention prerequisites, authentication needs, or any context for selection among the many sibling tools provided.
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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