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 a read operation ('Get'), implying it is likely non-destructive, but does not address permissions, rate limits, error handling, or output format. This leaves significant gaps in understanding how the tool behaves beyond its basic function.

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

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

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is 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.

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 for a tool that retrieves schema information. It does not explain what the output looks like (e.g., a list of properties with types), potential errors, or dependencies, leaving the agent with insufficient context for effective use.

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

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

The input schema has 100% description coverage, with 'tableName' clearly documented. The description adds no additional parameter details beyond what the schema provides, such as examples or constraints. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.

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

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

The description clearly states the verb 'Get' and the resource 'property names and types from a table', making the purpose specific and understandable. However, it does not explicitly differentiate from sibling tools like 'list_tables' (which likely lists table names) or 'query_table' (which likely queries table data), leaving room for ambiguity in tool selection.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'list_tables' or 'query_table'. It lacks context such as prerequisites, typical use cases, or exclusions, leaving the agent to infer usage based on tool names alone.

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

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 lists tables but doesn't mention any behavioral traits such as pagination, rate limits, authentication requirements, or what happens if no tables exist. This leaves significant gaps in understanding how the tool behaves operationally.

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

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

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.

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

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

Given the lack of annotations and output schema, the description is incomplete for a tool that interacts with storage resources. It doesn't address behavioral aspects like return format, error handling, or operational constraints, which are important for an agent to use the tool effectively in real scenarios.

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

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

Schema description coverage is 100%, so the input schema already documents the optional 'prefix' parameter. The description doesn't add any additional meaning about parameters beyond what's in the schema, such as format examples or usage context. The baseline score of 3 reflects adequate but minimal value added.

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

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

The description clearly states the action ('List') and target resource ('all tables in the storage account'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'get_table_schema' or 'query_table', which prevents a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'get_table_schema' or 'query_table'. There's no mention of use cases, prerequisites, or exclusions, leaving the agent with minimal contextual direction.

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

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 excels at this by: 1) Warning about the default 5-item limit to protect context window, 2) Explaining that the full query executes to get total count despite the limit, 3) Providing extensive OData filter examples and supported operators, 4) Clarifying this is a query operation (not mutation). This goes well beyond what the input schema provides about behavioral characteristics.

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

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

The description is appropriately sized and front-loaded with the most critical information (warning and core purpose). Every sentence earns its place by providing essential guidance, examples, or operational details. The only minor issue is the extensive OData examples could be slightly condensed, but they serve an important educational purpose for this query tool.

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

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

Given 4 parameters, no annotations, and no output schema, the description does an excellent job of providing context. It covers the tool's purpose, behavioral constraints (limit warning), parameter usage (extensive filter examples), and distinguishes from siblings. The only gap is lack of information about return format or error handling, which would be helpful since there's no output schema.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds significant value beyond the schema by: 1) Providing concrete OData filter examples with syntax, 2) Listing all supported operators with explanations, 3) Clarifying the 'limit' parameter's purpose and default behavior. However, it doesn't explain the 'select' parameter's semantics beyond what the schema already states, keeping it from a perfect score.

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

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

The description clearly states the tool's purpose: 'Query data from an Azure Storage Table with optional filters.' This is a specific verb ('query') + resource ('Azure Storage Table') combination that distinguishes it from sibling tools like 'get_table_schema' (schema retrieval) and 'list_tables' (table enumeration). The description establishes this as a data querying tool with filtering capabilities.

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

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

The description provides clear context about when to use this tool: for querying table data with OData filters. It distinguishes from siblings by focusing on data retrieval rather than schema or table listing. However, it doesn't explicitly state when NOT to use this tool or mention specific alternatives beyond the sibling names. The warning about the default limit provides operational guidance but not comparative usage advice.

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