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convert_data

Data Converter — Convert a data file between formats (JSON, NDJSON/JSONL, CSV, TSV, XML, YAML, TOML, INI). Upload the data file and pick a target format; the result comes back as a downloadable file (so it chains in workflows). Inline text is also accepted via a JSON-body 'input' string instead of a file. [category: convert]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toYesTarget format. Must differ from the source format.
fileYesThe data file to convert (max 5MB).
fromNoSource format. Optional — inferred from the file extension when omitted. 'ndjson' (aka jsonl) is newline-delimited JSON; 'tsv' is tab-separated values.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Adds useful behavioral facts beyond annotations: the result is returned as a downloadable file rather than an inline body, and inline text can substitute for a file upload. This is consistent with readOnlyHint=false and destructiveHint=false, since conversion creates an output artifact without destroying the input.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with purpose and format list, then usage and output behavior. Minor redundancy exists: the 'Data Converter' prefix repeats the title/category, and 'upload the data file and pick a target format' restates required parameters, so it is not maximally tight.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 3-param tool with no output schema, the description adequately covers accepted inputs, output delivery as a downloadable file, and workflow chaining. Error cases and the 5MB limit are not explained in the description, but the size limit is in the schema and error behavior is not essential for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds meaningful semantics by introducing an undocumented inline 'input' string alternative and confirming that 'from' is inferred when omitted. The only caveat is that 'input' is not present in the schema, which could confuse strict validation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (convert), resource (data file), and enumerates the supported formats, which distinguishes it from document/media converters in the sibling list. It does not explicitly name or exclude overlapping siblings like convert_file or convert_text, so it falls just short of full differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives clear operational guidance: upload a file, pick a target format, or pass inline text, and notes the downloadable-file behavior for workflow chaining. It does not state when to prefer this tool over convert_file/convert_text/convert_batch, nor any when-not-to-use conditions, so usage is implied rather than explicit.

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

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TDQS

B3.2/5.0
Disambiguation2/5

Multiple tool pairs are near-identical: octopus_mkdir/octopus_make_folder and octopus_move/octopus_move_file are literal duplicates, analyze_hash/generate_hash both compute hashes, convert_word_to_pdf overlaps convert_document, and photo_compress/photo_compress_to_size plus pdf_thumbnails/pdf_to_images have fuzzy boundaries. The descriptions are detailed and cross-reference each other helpfully, but at 144 tools an agent will regularly misselect.

Naming Consistency3/5

The dominant {category}_{verb}_{object} snake_case pattern (pdf_*, photo_*, convert_*, analyze_*, media_*) is largely consistent and predictable. However, outliers like chatwithyourpdf and describe_image break the category-prefix convention, and the octopus namespace mixes bare verbs (read, write, mkdir) with verb_noun forms (make_folder, move_file, search_meta) inconsistently.

Tool Count2/5

144 tools is an extreme count for any MCP server. The broad scope (PDF, photo, video, audio, conversion, analysis, generation, file storage, web, e-sign) justifies some volume, but the count is inflated by batch and inspect variants (pdf_to_excel + batch + inspect), duplicate tools, and overlapping converters. An agent faces an overwhelming selection surface.

Completeness4/5

Per-domain coverage is remarkably deep: PDF spans merge/split/compress/protect/unlock/metadata/OCR/watermark and bidirectional conversion; photo covers editing, format conversion, face handling, OCR, and collage; file storage has full CRUD plus search. Minor gaps exist (no audio transcription, no video metadata editing, no deletion of PDF pages is actually covered via pdf_delete_pages) but the surface has no dead ends for its declared domains.

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