Skip to main content
Glama

netcafe-tables

by com.ainetcafe

csv to chart

csv_to_chart

CSV (first column = labels, second = values) → chart PNG in one call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
csvNo
urlNo
typeNo

Schema Changelog

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

  1. Changed3 schema fields changed
    • addedInput schema / properties / csv
      Added value: +{}
    • addedInput schema / properties / type
      Added value: +{}
    • addedInput schema / properties / url
      Added value: +{}
  2. First observed

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals expected input ordering and output format, but omits details about chart type selection, handling of malformed data, or authentication/rate limits. Adds some behavioral context beyond schema but remains thin.

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

Conciseness5/5

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

The description is a single concise sentence that delivers the core input-output contract without waste. It front-loads the key transformation and format details.

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

Completeness2/5

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

The tool has 3 parameters, no output schema, and no annotations. The one-sentence description covers the high-level transformation but leaves parameter semantics, chart types, error behavior, and alternative input methods unexplained. Given the sibling set and complexity, this is insufficient.

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

Parameters1/5

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

Schema description coverage is 0%, and the description does not explain any of the three parameters (csv, url, type). It does not clarify what each parameter means, which is especially problematic given the ambiguous empty schema types and additionalProperties allowed.

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

Purpose5/5

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

The description clearly states the transformation: CSV with specified column roles becomes a chart PNG in one call. It distinguishes from siblings like csv_to_json and csv_to_md_table by naming the output format (chart PNG) and input structure.

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?

Usage context is implied: use when you need to convert CSV data into a chart image. However, no explicit when-not-to-use guidance or alternative tool names are provided, leaving the decision to inference from sibling naming.

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

All netcafe-tables toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation4/5

Most tools have clearly distinct functions (conversions, cleaning, xlsx I/O, reconciliation), but diff_tables, reconcile_ledger, and match_transactions all involve comparing or matching records, which could cause initial confusion. However, each has a specific use case—generic column diff, amount-focused reconciliation, and keyless fuzzy matching—and the descriptions provide enough detail to disambiguate them.

Naming Consistency4/5

The conversion tools follow a consistent 'csv_to_*' or 'json_to_csv' pattern, while operation tools use a verb_noun style (e.g., clean_table, merge_tables, reconcile_ledger). This dual pattern is predictable by function type, but 'what_can_you_do' breaks convention as a question-like meta-tool, so the naming is mostly consistent with a minor deviation.

Tool Count5/5

With 15 tools, the server sits at the upper edge of the well-scoped range, but each tool earns its place in the CSV/spreadsheet domain—covering conversions, cleaning, merging, diffing, reconciliation, matching, and Excel I/O. The count feels appropriate for the server's broad yet focused scope.

Completeness5/5

The toolset covers the full lifecycle of table manipulation: reading (read_xlsx), encoding repair, cleaning and transforming (clean_table), merging (merge_tables), comparing (diff_tables, reconcile_ledger), matching without keys (match_transactions), entity deduplication, and output to various formats (JSON, MD, chart, QBO, XLSX). No obvious gaps are evident for common tasks.

Resources