SF-MCP

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. It mentions authentication requirements ('auth_user_id' and 'auth_password' are required) and pagination defaults ('default 100, max 1000'), which adds useful context. However, it doesn't cover important aspects like rate limits, error handling, or response format details, leaving gaps in behavioral understanding.

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 a clear purpose statement, usage guidelines, and detailed parameter documentation. While comprehensive, it's appropriately sized for a complex tool with many parameters. The information is front-loaded with the most important details first, though the parameter section is lengthy but necessary.

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 (12 parameters, no annotations, but with output schema), the description does a good job covering purpose, usage, and parameters. The presence of an output schema means return values don't need explanation. However, some behavioral aspects like error conditions or performance characteristics are missing, preventing a perfect score.

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, the description fully compensates by providing detailed explanations for all 12 parameters. Each parameter is clearly documented with examples (e.g., "entity: OData entity to query (e.g., 'User', 'EmpJob', 'Position')"), default values, and constraints, adding significant value beyond the bare schema.

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 as 'Query any OData entity with flexible filtering, sorting, and field selection.' It specifies the verb ('query'), resource ('any OData entity'), and scope ('flexible filtering, sorting, and field selection'), and explicitly distinguishes it from specialized sibling tools for common queries like employee profiles.

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 vs alternatives: 'Use other specialized tools for common queries (employee profiles, etc.).' It clearly positions this as the most flexible tool for general queries while directing users to specialized tools for specific use cases, which helps the agent make informed decisions.

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