Thinkific MCP Server

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

With no annotations provided, the description carries full burden but offers minimal behavioral information. It indicates a read operation ('Returns') but doesn't disclose authentication requirements, rate limits, error behavior (e.g., what happens if email doesn't exist), or response format. For a GraphQL query tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.

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 extremely concise (7 words) and front-loaded with all essential information. Every word earns its place: verb, resource, lookup method, and technology context. There's zero redundancy or unnecessary elaboration.

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 GraphQL query tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what a 'User' object contains, how errors are handled, authentication requirements, or performance characteristics. The agent lacks critical context needed to use this tool effectively despite its simple parameter structure.

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 100% with a single well-documented 'email' parameter, so the baseline is 3. The description adds no additional parameter semantics beyond what's in the schema (it merely restates 'by email address'), providing no format examples, validation rules, or edge case handling beyond the schema's email format specification.

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 action ('Returns') and resource ('a User'), specifying the lookup mechanism ('by email address') and technology context ('GraphQL'). It distinguishes from generic 'get_user' by specifying the email-based query approach, though it doesn't explicitly differentiate from other user retrieval tools like 'gql_user' or 'search_users'.

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 no guidance on when to use this tool versus alternatives like 'gql_user', 'get_user', or 'search_users'. It doesn't mention prerequisites, error conditions, or typical use cases. The agent must infer usage from the name and description alone without explicit context.

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