SF-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 the tool's function (fetching metadata), output (field definitions, types, constraints), and authentication requirements (user ID and password). However, it does not mention potential rate limits, error conditions, or data sensitivity, leaving some behavioral aspects uncovered.

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 and front-loaded with the core purpose, followed by detailed parameter explanations and return value. Every sentence adds value—no fluff or repetition—making it efficient and easy to parse for an AI agent.

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 (6 required parameters, no annotations, but with an output schema), the description is complete. It explains the tool's purpose, all parameters with examples, and the return type. The output schema handles return values, so the description appropriately focuses on usage and inputs without redundancy.

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 compensate fully. It provides clear semantics for all 6 parameters: 'instance' as company ID, 'entity' as OData entity to inspect with examples, 'data_center' as SAP code with examples, 'environment' as type with examples, and authentication details as required. This adds 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 with specific verbs ('Retrieve', 'fetches') and resources ('OData entity metadata/configuration', '$metadata document'). It distinguishes itself from siblings by focusing on entity metadata retrieval rather than operational data like employee profiles or permissions, making its scope explicit.

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 for inspecting entity fields and constraints, but does not explicitly state when to use this tool versus alternatives like 'list_entities' or 'query_odata'. It provides context for metadata retrieval but lacks explicit exclusions or comparisons with sibling tools.

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