atlassian-browser-mcp

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

Annotations indicate readOnlyHint=true, which the description aligns with by describing a retrieval operation. The description adds valuable behavioral context beyond annotations: it explains platform-specific API differences (Cloud vs Server/DC), auto-resolution behavior for context_id, and filtering capabilities (contains, return_limit). However, it doesn't mention rate limits or authentication requirements, which could be relevant for a Jira API tool.

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 efficiently structured: it opens with the core purpose, lists supported field types, explains platform differences, and summarizes the return format. Every sentence adds value—no redundancy or fluff. The parameter explanations in the Args section are clear and directly relevant to usage.

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 (platform-specific behavior, 7 parameters) and the presence of both comprehensive input schema (100% coverage) and output schema (implied by 'Returns' statement), the description is complete. It covers purpose, usage guidelines, behavioral nuances, and return format without needing to duplicate schema details. The context signals indicate all necessary information is provided.

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 100% schema description coverage, the input schema already documents all 7 parameters thoroughly. The description adds minimal parameter semantics beyond the schema—it mentions platform-specific requirements for project_key and issue_type, but most parameter details (like field_id format, contains filtering behavior) are already in the schema. This meets the baseline for high schema coverage.

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 specific action ('Get allowed option values') and resource ('for a custom field'), distinguishing it from sibling tools like jira_search_fields (which finds field IDs) and jira_get_issue (which retrieves issue data). It explicitly lists the field types supported (select, multi-select, radio, checkbox, cascading select), making the purpose highly specific and differentiated.

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 versus alternatives, noting that 'Use jira_search_fields to find field IDs' in the input schema. It also details platform-specific requirements: Cloud uses Field Context Option API with auto-resolution, while Server/DC requires project_key and issue_type parameters. This clearly defines the context and prerequisites for proper usage.

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