axom_mcp_transform

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

The description adds valuable context beyond annotations by detailing supported formats, transformation rules, template support, and chain operations. Annotations already indicate it's read-only, non-destructive, and idempotent, but the description clarifies what 'transformation' entails (e.g., field mapping, filtering, sorting) and mentions advanced features like Jinja2 templates and chaining, which enhance behavioral understanding without contradicting annotations.

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 appropriately sized, using bullet points and clear sections ('Supported Formats', 'Transformation Rules', 'Template Support', 'Chain Support') to organize information efficiently. Each sentence adds value without redundancy, making it easy to scan and understand the tool's capabilities quickly.

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 parameters, multiple formats and rules) and rich annotations, the description is largely complete. It covers key aspects like formats, rules, templates, and chaining. However, without an output schema, it does not describe return values or error handling, which could be helpful for a transformation tool. The annotations provide safety context, but output details are missing.

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 6 parameters thoroughly. The description adds some semantic context by listing formats and rules that correspond to parameters like 'input_format', 'output_format', and 'rules', but does not provide additional syntax, examples, or details beyond what the schema specifies. 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 tool's purpose with specific verbs ('Transform data between formats and structures') and lists the supported formats and transformation rules. It distinguishes itself from sibling tools like 'analyze', 'discover', 'exec', and 'memory' by focusing specifically on data transformation rather than analysis, discovery, execution, or memory operations.

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 through the listing of supported formats and transformation rules, suggesting this tool should be used for data format conversion and restructuring tasks. However, it does not explicitly state when to use this tool versus alternatives like 'axom_mcp_analyze' for data analysis or provide specific exclusions or prerequisites for usage.

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