MCP Server for OpenMetadata

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 states 'Get suggested entities' but doesn't describe the nature of the suggestions (e.g., based on partial input, popularity, or relevance), whether it's a read-only operation, potential rate limits, or authentication needs. For a tool with 4 parameters and no annotations, this leaves significant behavioral gaps.

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 a single, efficient sentence: 'Get suggested entities for auto-completion.' It's front-loaded with the core purpose and avoids unnecessary words. Every part of the sentence contributes to understanding the tool's intent, making it appropriately sized for its content.

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 complexity (4 parameters, 0% schema coverage, no annotations, no output schema), the description is incomplete. It doesn't explain what 'suggested entities' are, how they're generated, the format of the output, or the role of parameters. For a tool that likely returns structured data for auto-completion, more context is needed to guide effective use.

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 description mentions 'auto-completion' but doesn't explain any parameters. With 0% schema description coverage and 4 parameters (q, index, field, size), the schema provides only titles and types without meaning. The description fails to compensate by clarifying what 'q' represents (e.g., query string), what 'index' or 'field' refer to, or how 'size' affects results, leaving parameters largely undocumented.

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 'Get suggested entities for auto-completion' clearly states the action (get) and resource (suggested entities), with a specific use case (auto-completion). However, it doesn't distinguish this from sibling tools like 'search_entities' or 'search_field_query', which might also retrieve entities but with different purposes. The purpose is clear but lacks sibling differentiation.

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. It mentions 'auto-completion' as a use case, but doesn't specify scenarios, prerequisites, or exclusions. For example, it doesn't clarify if this is for real-time suggestions during typing or batch processing, leaving the agent to infer usage without explicit direction.

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