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

With no annotations, the description carries the behavioral burden. It discloses a non-mutating 'show' operation and specifies the exact output fields, making the tool's behavior predictable. It does not discuss error conditions or permissions, but for a simple introspection tool this is sufficient.

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 short sentences with no filler. The core purpose and output are front-loaded, and the usage guidance is added as a separate sentence. Every word 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 single-parameter tool with an output schema, the description covers what the tool does, what it returns, and when to use it. No critical information is missing for an agent to invoke it correctly.

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 clarify the one parameter. It does so by identifying table_name as the table whose columns are shown. Since the parameter title is already 'Table Name' and the description ties it to the action, the semantic gap is mostly filled.

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 a specific action and resource: 'Show the columns of one table' with explicit output details (name, type, nullable). This clearly distinguishes it from siblings like list_tables and run_query, and the 'one table' scope separates it from broader schema tools.

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 gives explicit context for when to use the tool: 'Use before writing SQL.' It does not mention alternatives or exclusions relative to get_schema, but the clear use case is enough for an agent to decide when it applies.

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 behavioral burden. It discloses that the tool returns all tables and columns in a single call and emphasizes a performance trait. It does not explicitly state side effects, but 'get' and the output schema imply a read-only, well-defined response.

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 one tightly written sentence with no filler. It front-loads the core promise and immediately follows with the comparative value proposition, making it easy to scan.

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 zero-parameter, bulk-read tool with an output schema present, the description covers everything an agent needs: scope, speed advantage, and differentiation from siblings. No required inputs or return-shape details are missing.

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?

There are zero parameters, so the schema's 100% coverage vacuously documents all inputs. The description appropriately adds no parameter details because none are needed.

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 a specific verb ('Get'), a specific resource ('ALL tables and their columns'), and a scope ('in one call'), which clearly distinguishes it from list_tables and describe_table. An agent can understand exactly what this tool does and how it differs from siblings.

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 positions this tool as faster than 'list_tables + describe_table', giving an agent a clear reason to prefer it over those siblings. It doesn't enumerate exclusion cases, but the zero-parameter, bulk-read nature makes the guidance sufficient.

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 must carry the behavioral burden. 'List' and 'Call this first' imply a safe, read-only discovery operation, and the description clarifies that both tables and views are included. However, it does not disclose details such as whether system schemas are included or how results are ordered, though the output schema covers return value structure.

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 short sentences with no filler. The first sentence states the action and resource, and the second provides a clear usage directive. Every word 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 zero-parameter discovery tool with an output schema and clear sibling context, the description is fully adequate. It tells the agent what the tool returns, when to call it, and how it fits into the workflow.

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 takes zero parameters, so the description does not need to explain parameter meaning. With no parameters to document, the baseline of 4 applies because there is nothing the schema requires the description to supplement.

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 uses a specific verb-resource pair, 'List all tables and views in the database,' which clearly identifies the operation's scope. The phrase 'Call this first' also positions it as a discovery/entry-point tool, distinguishing it from siblings like describe_table, get_schema, and run_query.

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?

'Call this first to see what data exists' gives explicit guidance on when to invoke the tool. It does not explicitly state when not to use it or mention alternatives, but for a simple discovery tool the primary usage context is clear.

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?

The description discloses key behaviors: read-only enforcement, rejection of writes and multiple statements, return limit of 100 rows, and error handling advice. Although annotations are not provided, the description covers significant behavioral traits beyond the schema, but it doesn't detail failure modes or side effects (e.g., no mention of rate limits or resource usage). With no annotations, this is adequate but not exhaustive.

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 compact and efficient, using bullets to front-load the most critical constraints (one query, read-only, row limit) and then providing helpful error-handling guidance. Every sentence adds value without fluff.

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 (one parameter) and that an output schema exists, the description covers the essentials: what kind of query to provide, constraints, and error handling. It doesn't describe the output structure, but the output schema likely covers that. It is complete enough for an agent to call it correctly, though it could mention pagination for more than 100 rows.

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 only one 'sql' parameter and 0% schema coverage, the description must compensate. It explicitly states the parameter should be a single PostgreSQL SELECT query, and clarifies that it's read-only and rejects multiple statements or risky functions, adding semantic meaning beyond the bare schema field name.

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: 'Run ONE read-only PostgreSQL SELECT query' with a specific verb and resource. It distinguishes from siblings by implying it executes queries, while siblings like list_tables and describe_table are for metadata inspection, though it doesn't explicitly name them.

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: it is for one read-only SELECT query, and mentions that writes are rejected. It doesn't explicitly state when to use this vs. siblings (e.g., for data retrieval vs. metadata), but the read-only nature and rejection of multiple statements imply appropriate usage.

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