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. The description reveals nothing about what the tool actually does behaviorally: whether it's a read operation, what information it returns, if it requires specific permissions, potential side effects, error conditions, or performance characteristics. 'describe_table' implies a read operation, but this isn't explicitly stated or detailed.

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

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

The description is extremely concise at just three words, which could be appropriate if it were more informative. However, this brevity results in under-specification rather than efficient communication. While it's front-loaded with the core action, the single phrase doesn't earn its place by providing sufficient value.

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 a tool with 1 parameter (0% schema coverage), no annotations, no output schema, and sibling tools that suggest database/query operations, the description is completely inadequate. It doesn't explain what the tool returns, how it differs from siblings, what 'describe' means in this context, or any behavioral characteristics. For a tool that presumably returns table metadata or schema information, this leaves critical gaps.

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

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

Schema description coverage is 0%, so the description must compensate for undocumented parameters. The description mentions 'by table name' which hints at the 'name' parameter, but adds no semantic meaning beyond what's obvious from the parameter name itself. It doesn't explain what constitutes a valid table name, format expectations, or how the parameter influences the tool's behavior.

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 'describe_table by table name' is essentially a tautology that restates the tool name with minimal additional information. It mentions the action ('describe_table') and the parameter ('by table name'), but doesn't specify what describing entails (e.g., returning schema, metadata, statistics) or what resource is being described. It doesn't distinguish this tool from its sibling 'list_table' or 'read_query' beyond the obvious name difference.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to use describe_table instead of list_table (which might list tables without details) or read_query (which might execute queries). There are no prerequisites, exclusions, or contextual cues about appropriate usage scenarios.

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 only states the action ('list table') without details on permissions, rate limits, pagination, or what 'current bitable' entails. This is inadequate for a tool with zero annotation coverage, as it lacks critical behavioral context.

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

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

The description is a single phrase ('list table for current bitable'), which is concise but potentially under-specified. It lacks structure or front-loading of key details, making it efficient but not optimally informative for an agent.

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

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

Given the tool's simplicity (0 parameters, no output schema), the description is incomplete. It doesn't explain what 'current bitable' means, how results are returned, or differentiate from siblings, leaving gaps in context despite low complexity.

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 tool has 0 parameters, and the schema description coverage is 100% (though empty). The description doesn't need to add parameter semantics, so it meets the baseline of 4 for tools with no parameters, as there's nothing to compensate for.

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 states the tool's purpose as 'list table for current bitable', which clearly indicates a listing operation on tables. However, it doesn't specify what 'current bitable' means or how this differs from sibling tools like 'describe_table' and 'read_query', making it somewhat vague about scope and differentiation.

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 'describe_table' or 'read_query'. It doesn't mention any prerequisites, context for 'current bitable', or exclusions, leaving the agent without usage instructions.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description reveals nothing about what this tool actually does behaviorally - whether it executes SQL queries, what permissions are required, whether it's read-only or has side effects, what happens with invalid SQL, or what format results are returned in. 'read_query by sql' is completely inadequate for a tool that presumably executes database queries.

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?

While technically concise with just three words, this is under-specification rather than effective conciseness. The description is so sparse that it fails to communicate essential information. Every word should earn its place, but here the words don't provide enough value to justify their inclusion - they create ambiguity rather than clarity.

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

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

For a SQL query execution tool with no annotations, no output schema, and a single undocumented parameter, the description is completely inadequate. It doesn't explain what the tool returns, what errors might occur, what database context it operates in, or any behavioral characteristics. This leaves the agent with insufficient information to use the tool effectively.

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

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

With 0% schema description coverage and one required parameter (sql), the description provides no meaningful information about the parameter. It doesn't explain what SQL syntax is expected, what database it runs against, whether it supports parameterized queries, or any constraints on the SQL. The description fails completely to compensate for the lack of schema documentation.

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 'read_query by sql' is a tautology that essentially restates the tool name with minimal additional information. It mentions 'read' and 'sql' but doesn't specify what resource is being read (tables? databases? specific data?), nor does it distinguish this tool from its siblings (describe_table, list_table). The purpose remains vague rather than specific.

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?

There is absolutely no guidance about when to use this tool versus the sibling tools (describe_table, list_table). The description provides no context about appropriate use cases, prerequisites, or alternatives. This leaves the agent with no information to make an informed selection between available tools.

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