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

Annotations already declare the tool as read-only and open-world, so the behavioral burden is lower. The description clarifies that DDL is returned for exact table names, but does not add details like pagination limits, error handling for missing tables, or performance traits beyond what annotations signal.

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—two short sentences that immediately convey purpose and usage context. There is no wasted wording, and every sentence adds 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 the tool's simplicity, strong annotations, and complete schema, the description is largely complete. It could optionally mention that the output is raw DDL (though implicit), but lack of output schema does not diminish clarity given the straightforward fetch operation.

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

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

Schema description coverage is 100%, so the schema already thoroughly documents all three parameters. The description adds no extra semantics beyond what the schema provides, so a 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 tool fetches 'full stored DDL for one or more tables by exact name', using specific verbs and resources. It also distinguishes itself from sibling tool 'search_schema' by indicating when to use this tool after that one.

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 explicit guidance on when to use this tool ('after search_schema when you need every column') and references the user naming a table. However, it does not explicitly state when not to use it or mention any alternatives like 'list_tables'.

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?

Annotations already declare readOnlyHint=true and openWorldHint=true, covering safety and scope. The description adds beyond annotations by stating that results are grouped by database and include index configuration. No behavioral traits (like performance impact or pagination) are discussed, but for a read-only inventory tool this is adequate.

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?

Two sentences with zero wasted words. The first sentence defines the core functionality, and the second provides usage guidance. It is front-loaded and every part earns its place.

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 simple list tool with no output schema, the description gives a sufficient mental model: inventory, grouping by database, and inclusion of index configuration. Could be slightly more explicit about the return structure (e.g., whether it's flat or nested), but fine given the tool's simplicity and the presence of sibling context.

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 coverage is 100% with clear descriptions for both optional parameters (database filter and namespace override). The description does not elaborate further on parameter semantics beyond what the schema provides, so it meets the baseline expected when schema does the heavy lifting.

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 provides an 'Inventory of every table in the schema index, grouped by database, plus the index configuration.' It uses specific verbs and nouns, and distinguishes itself from sibling tools like search_schema by indicating when to use it (when search_schema returns empty).

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 explicitly says 'Use it to orient yourself, or when search_schema comes back empty,' providing clear usage context. It does not mention when to avoid this tool or use get_table_schema, but the positive guidance is strong and helps an agent decide between siblings.

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?

Annotations already provide readOnlyHint=true and openWorldHint=true, so the description's disclosure burden is lower. The description adds context about the Pinecone vector store and the first-step pipeline role, which is valuable beyond annotations.

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?

Two sentences, no wasted words. The first sentence defines the tool, the second provides usage instruction. Front-loaded and efficient.

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?

No output schema, but the description indicates what is returned (relevant tables, columns, types, relationships). For a semantic search tool, this is sufficient. The tool is moderately complex (5 params, no nested objects) and the description covers its role adequately.

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 coverage is 100%, so all 5 parameters are documented in the schema. The description reinforces the 'question' parameter by saying 'pass the user question verbatim' but does not add new semantics beyond what the schema provides for other parameters. Baseline 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 it performs semantic search over the database schema stored in Pinecone, explicitly for text-to-SQL requests. It distinguishes from siblings by positioning itself as the first step and mentioning retrieval of tables, columns, types, and relationships.

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 explicitly says 'Call this FIRST for any text-to-SQL request', providing clear context for when to use. It doesn't explicitly exclude alternatives like get_table_schema or list_tables, but the 'first step' instruction implies the workflow.

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