Parse CSV/TSV/semicolon/pipe-delimited data into rows. Returns column headers, row/column counts, delimiter used, column consistency check, and a JSON preview of the first rows. Handles quoted fields and escaped quotes.
| Name | Required | Description | Default |
|---|---|---|---|
| csv | Yes | The delimited data to parse | |
| delimiter | No | Delimiter: auto, comma, tab, semicolon, or pipe | auto |
| has_header | No | Whether the first row is a header row |
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?
With no annotations, the description carries the behavioral disclosure burden and does so well: it explains what the tool returns and that it handles quoted fields and escaped quotes. It does not mention potential limitations such as malformed input handling, size limits, or exact preview size, so it is strong but not exhaustive.
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 three concise sentences, each adding distinct value: main purpose, concrete outputs, and edge-case handling. It is front-loaded with the tool's core action and contains no filler.
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 parsing tool with no output schema, the description usefully lists the return components and special-case behavior. It is complete enough for an agent to select and call the tool, though 'column consistency check' could be more specific and no error behavior is mentioned.
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 schema already documents all three parameters. The description reinforces which delimiters are supported and the fact that the first row may be a header, but it does not add meaningful parameter-level detail beyond what the schema provides.
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 states a specific action ('Parse') and the exact resource ('CSV/TSV/semicolon/pipe-delimited data'), and clearly distinguishes the tool from siblings like json_inspect or text_diff by describing what it returns: headers, counts, delimiter, consistency check, and preview. The purpose is immediately understandable and 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 clearly implies the tool is for converting delimited text into structured rows, which is distinct from all listed siblings. It does not explicitly name an alternative or state when not to use it, so the guidance is clear but lacks explicit exclusions or alternative routing.
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.
Each tool targets a distinct operation—encoding, color conversion, parsing, hashing, JWT validation, Markdown rendering, regex testing, SemVer operations, diffing, URL analysis, and UUID generation. The four SemVer tools are related but cleanly separated by action (bump vs compare vs max vs satisfies), and descriptions clarify their boundaries.
Tools overwhelmingly follow an object_verb snake_case convention (base64_encode, csv_parse, regex_test, semver_bump). Semver_max and semver_satisfies deviate slightly from the imperative verb pattern, but the overall naming is predictable and searchable.
At 16 tools, the server is slightly above the ideal 3–15 tool range but each utility earns its place for a general-purpose developer toolbox. No tools feel redundant, and the count remains manageable because the names and domains are highly scannable.
The toolkit covers a solid breadth of common developer utilities: encodings, common formats (JSON, CSV, Markdown), hashing/JWT, regex, SemVer, cron, URL, UUIDs, and diffing. It lacks some fringe converters such as YAML/XML parsing or HTML escape/unescape, but these are minor gaps that agents can work around rather than dead ends.