text_xml_to_jsonConvert XML documents to JSON. Use when parsing XML API responses, feeds, or config files into a structured JSON format for easier processing.
| Name | Required | Description | Default |
|---|---|---|---|
| xml | Yes | XML string to convert |
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?
The annotation readOnlyHint: true already indicates the operation is non-mutating. The description adds no extra behavioral context beyond restating the conversion purpose; it does not disclose how invalid XML is handled, potential data loss, or output structure. This meets the baseline but lacks additional 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, with the first sentence front-loading the core function and the second adding usage context. It contains no redundant phrasing or unnecessary detail, making it appropriately sized and well-structured.
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 single-parameter conversion tool with a read-only annotation and no output schema, the description provides essential purpose and usage guidance. It does not specify the exact JSON output format or error behaviors, but these are not critical for a straightforward conversion tool, so it is almost complete.
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 input schema covers the only parameter 'xml' with the description 'XML string to convert', achieving 100% coverage. The tool's description adds no further parameter-level semantics beyond what the schema already provides, so the baseline score of 3 applies.
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 with a specific verb ('Convert') and resource ('XML documents to JSON'). It distinguishes itself from sibling converters like text_csv_to_json and text_yaml_to_json by naming the input format, and adds concrete use cases ('XML API responses, feeds, or config files').
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 explicitly specifies when to use the tool ('Use when parsing XML API responses, feeds, or config files'), providing clear context. However, it does not mention when not to use it or name alternative tools, so it slightly falls short of the highest tier.
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.