CockroachDB MCP Server

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

No annotations are provided, so the description carries the full burden. It describes the tool as providing detailed metadata, which implies a read-only operation, but does not explicitly state behavioral traits like whether it requires specific permissions, has rate limits, or what happens if the table doesn't exist. The description adds some context about interpreting structures but lacks comprehensive behavioral disclosure.

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 core purpose in the first sentence. The additional sentences and Args/Returns sections add necessary context without redundancy. However, the formatting with bullet points could be slightly more streamlined, but overall it is efficient and well-structured.

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 moderate complexity (2 parameters, no annotations, no output schema), the description is somewhat complete but has gaps. It explains the purpose and parameters but lacks details on behavioral traits, error handling, and output specifics. Without annotations or output schema, more context on what 'Table details' includes would be beneficial for full completeness.

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 schema description coverage is 0%, so the description must compensate. It adds meaning beyond the schema by explaining that table_name is 'Name of the table' and db_schema is 'Schema name (default: "public")', which clarifies their roles. However, it does not provide details on format constraints or examples, leaving some gaps in parameter understanding.

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 with specific verbs ('provide detailed schema information, column definitions, data types, and other metadata') and resource ('for a specified table'). It distinguishes from siblings like analyze_schema (which might analyze broader schema patterns) or list_tables (which just lists names) by focusing on detailed metadata for a single table.

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 implies usage context ('allows the AI to accurately interpret table structures and formulate precise queries or data manipulation commands'), suggesting it should be used when understanding table schema is needed. However, it does not explicitly state when to use this tool versus alternatives like analyze_schema or get_table_relationships, nor does it provide exclusions or prerequisites.

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