text_json_to_yamlConvert JSON data to YAML format. Use when generating human-readable config files from JSON data structures.
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | JSON object to convert | |
| indent | No | Number of spaces for indentation |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint: true, so the safety profile is covered. The description adds the use case of human-readable config files but does not disclose additional behavioral traits such as return format, handling of edge cases, or limitations. With annotations providing the safety context, the description adds only modest value.
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 two sentences, front-loaded with the core action and followed by a use case. Every word earns its place with no fluff or redundancy.
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 simple two-parameter converter with readOnlyHint annotation and no output schema, the description is largely sufficient. It states the purpose and usage context. However, it could be slightly more complete by mentioning that the output is a YAML string, though this is implied by the tool name and conversion nature.
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%, meaning both `data` and `indent` parameters are already described in the input schema. The description does not add any parameter-specific semantics beyond what the schema provides, so it meets the baseline but does not exceed it.
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 function: 'Convert JSON data to YAML format.' This specific verb+resource combination distinguishes it from sibling tools like text_yaml_to_json and other text_* converters. The additional context about generating human-readable config files reinforces its purpose.
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 a clear use case: 'Use when generating human-readable config files from JSON data structures.' This gives context for when to use the tool, though it does not explicitly mention alternatives or when not to use it, missing the full guidance of an explicit exclusion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The tools are grouped into clear categories (dev, lookup, security, text, transform), which helps with disambiguation, but within categories there is some overlap. For example, lookup_ssl and lookup_ssl_cert_expiry both handle SSL certificates, and dev_url_encode/dev_url_decode are closely related but distinct. Most tools have unique purposes, but a few could be confused without careful reading of descriptions.
The naming follows a consistent snake_case pattern with a clear prefix structure (dev_, lookup_, security_, text_, transform_), which aids in organization. However, there are minor deviations like dev_cron_describe using 'describe' while others use verbs like 'generate' or 'convert', and some tools have longer names that break the verb_noun pattern slightly. Overall, the naming is predictable and readable.
With 49 tools, the count is excessive for a utility server, making it overwhelming and likely to cause confusion or inefficiency. While the tools cover many use cases, a more focused set of 15-25 tools would be more manageable and better scoped. The high number suggests feature bloat rather than a coherent, minimal surface.
The tool set is highly complete for its utility and development support domain, covering a wide range of operations from data transformation and security to lookups and text processing. There are no obvious gaps; each category provides comprehensive coverage, such as full text encoding/decoding, security functions, and various lookup capabilities, ensuring agents can handle diverse tasks without dead ends.