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netcafe-tables

by com.ainetcafe

Two tables → what differs (the VLOOKUP job, no amounts needed)

diff_tables
Read-onlyIdempotent

Matches rows across two CSVs on a key column and reports three things: keys only in A, keys only in B, and keys in both whose other columns disagree — naming the exact column and both values. Unlike reconcile_ledger this needs no amount column, so it also fits name lists, inventory counts, permission tables, and any "these two exports should match" check.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesColumn that identifies a row, e.g. id.
url_aNoLink to the first CSV.
url_bNoLink to the second CSV.
text_aNoOr the first CSV content directly.
text_bNoOr the second CSV content directly.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark the tool as read-only and idempotent. The description adds valuable behavioral detail beyond annotations: it reports three categories (keys only in A, only in B, and mismatched rows) and names the exact differing column and both values. No contradiction with annotations.

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?

Two sentences: the first states the core function and outputs, the second provides usage context and a sibling contrast. No redundancy, no filler, and it front-loads the most important information.

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

Completeness5/5

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

With an output schema present, annotations, and a detailed description covering purpose, behavior, and use cases, the tool is fully specified. The description covers all necessary decision-making context for an agent, leaving no obvious gaps.

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

Parameters3/5

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

Schema coverage is 100%, so each parameter already has a description. The tool description adds minimal parameter-specific detail beyond restating the key column concept and the two CSV sources. Baseline of 3 is appropriate since schemas carry the full burden.

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 uses specific language: 'Matches rows across two CSVs on a key column and reports three things' – a clear verb, resource, and exact outputs. It explicitly distinguishes itself from the sibling tool 'reconcile_ledger' by noting it needs no amount column, making its purpose unmistakable.

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

Usage Guidelines5/5

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

The description explicitly contrasts with reconcile_ledger ('Unlike reconcile_ledger this needs no amount column') and lists fitting use cases (name lists, inventory counts, permission tables). This tells the agent exactly when to choose this tool over alternatives.

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

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