text_csv_to_jsonParse CSV text into a JSON array of objects. Use when converting spreadsheet or tabular data into structured JSON for processing or storage.
| Name | Required | Description | Default |
|---|---|---|---|
| csv | Yes | CSV data as a string | |
| delimiter | No | Column delimiter character | , |
| has_header | No | Whether the first row contains column headers |
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 include readOnlyHint: true, which aligns with the non-mutating 'Parse' action. The description adds the output format ('JSON array of objects'), but does not disclose edge-case behaviors like error handling or encoding assumptions. It provides some value beyond the annotation but not rich context.
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?
Two concise sentences: the first states the core function, the second gives the use case. Every word earns its place, with no fluff or repetition of schema details.
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?
The tool is simple with 3 well-documented parameters and no output schema. The description covers the output format ('JSON array of objects') and the typical use case, making it complete enough for an AI agent to invoke correctly. It doesn't need to repeat parameter details already in the schema.
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%, so the baseline is 3. The description does not add parameter-level details beyond what the schema already provides (csv, delimiter, has_header). It only reinforces the general CSV-to-JSON concept.
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 ('Parse CSV text') and the output ('a JSON array of objects'), distinguishing it from sibling tools like text_xml_to_json and text_yaml_to_json. The verb 'Parse' plus resource 'CSV' makes the purpose unambiguous.
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 converting spreadsheet or tabular data into structured JSON for processing or storage.' It lacks explicit when-not-to-use or named alternatives, but the context is sufficiently clear for selecting this tool over similar ones.
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.