Hardened Google Workspace MCP

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 effectively describes what the tool does (shows table structure, dimensions, cell content), how to interpret the output (e.g., 'dimensions: 2x3'), and its role in debugging workflows. However, it lacks details on potential errors, performance characteristics, or authentication needs, though these are somewhat implied by the 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 well-structured with clear sections (e.g., 'USE THIS IMMEDIATELY WHEN:', 'WHAT THIS SHOWS YOU:', 'HOW TO READ THE OUTPUT:'), making it easy to scan. It is appropriately sized for a debugging tool, though some redundancy exists (e.g., repeating parameter info in 'Args' could be streamlined). Every sentence adds value, but minor trimming could improve efficiency.

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 complexity (debugging tables), lack of annotations, and 0% schema coverage, the description is highly complete. It covers purpose, usage guidelines, behavioral context, parameter semantics, output interpretation, and integration with workflows. The presence of an output schema (Returns: str with JSON structure) further reduces the need for return value explanations, making this description comprehensive and self-sufficient.

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%, but the description compensates by explaining all three parameters in the 'Args' section: user_google_email, document_id, and table_index (with clarification that 0 = first table). It adds meaning beyond the bare schema by specifying the table_index default and usage, though it doesn't detail format constraints or examples for the string parameters.

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 explicitly states the tool's purpose as an 'ESSENTIAL DEBUGGING TOOL' for when 'tables don't work as expected,' clearly distinguishing it from sibling tools like create_table_with_data or populate_existing_table. It specifies the exact function: to show table structure, dimensions, cell positions, current content, and insertion indices, making the verb+resource combination specific and unambiguous.

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 usage scenarios with bullet points (e.g., 'Table population put data in wrong cells,' 'Need to understand existing table structure'), workflow integration steps (e.g., 'After creating table → Use this to verify structure'), and references to sibling tools ('Planning to use populate_existing_table'). It clearly delineates when to use this tool versus alternatives, offering comprehensive guidance.

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