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ai-netcafe

by com.ainetcafe

Two tables → what does not match (the VLOOKUP job), with the arithmetic proof

reconcile_ledger
Read-onlyIdempotent

Reconciles two sets of records — your books against a bank, platform, or supplier statement. Matches rows on a key column, compares an amount column, and returns three lists: only in A, only in B, and same key but different amount. Amounts are compared in integer cents, so 0.1 + 0.2 never invents a phantom difference for someone to chase. The response also proves the result: the listed differences are re-added and must equal the gap between the two totals, checked in code. Use for month-end close, platform payouts vs orders, or any "these two numbers should agree and do not" problem. This is the job people do by hand with VLOOKUP or a groupby and then cannot prove they got right.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesColumn name to match rows on, e.g. order_id.
url_aNoLink to side A (e.g. your books).
url_bNoLink to side B (e.g. the statement).
amountYesNumeric column to compare, e.g. amount.
text_aNoOr the CSV content of side A directly.
text_bNoOr the CSV content of side B directly.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.6/5.0
Behavior5/5

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

The description goes well beyond the readOnly/read-only annotations by explaining the integer-cent precision ('0.1 + 0.2 never invents a phantom difference') and the built-in arithmetic proof ('the listed differences are re-added and must equal the gap'). These are valuable behavioral details not present in annotations.

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?

The description is front-loaded with a clear purpose and then provides useful details. The final sentence about VLOOKUP, while informative, is slightly rhetorical and could be seen as non-essential, but overall the structure is clean and purposeful.

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?

Given the tool's moderate complexity (6 params, output schema present, rich annotations), the description fully covers the behavioral contract: what inputs do, how matching works, what the output looks like (three lists), and the verification step. No significant gaps remain.

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?

Input schema covers all six parameters with descriptions (100% coverage), so the baseline is 3. The description adds semantic meaning by explaining that the 'key' parameter is for row matching and 'amount' is compared in integer cents, plus clarifies the relationship between url/text inputs for each side.

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 tool's function: 'Reconciles two sets of records' and details the matching and comparison logic, returning three specific lists. The title also frames it as an alternative to VLOOKUP, distinguishing it from sibling tools like diff_tables or merge_tables.

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

Usage Guidelines4/5

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

The description provides explicit when-to-use scenarios: 'Use for month-end close, platform payouts vs orders, or any "these two numbers should agree and do not" problem.' It does not explicitly mention alternative tools or when-not-to-use, but the VLOOKUP comparison implies the manual alternative.

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

A3.6/5.0
Disambiguation3/5

Many tools are clearly distinct, but there are several overlapping groups: PDF extraction (extract_invoices, extract_statement, extract_tables, pdf_to_markdown), table comparison (diff_tables vs reconcile_ledger), and model pricing (list_models vs model_costs). Descriptions help clarify boundaries, but an agent could misselect without careful reading.

Naming Consistency3/5

All names use lowercase snake_case, but the verb-noun pattern is inconsistent. Most tools are verb-first (build_app, clean_table, fetch_page), but several are noun-first (jwt_decode, regex_test, web_search), noun-only (ai_visibility, model_costs), bare verbs (recall, remember), or a full phrase (what_can_you_do). This mixed convention is still readable but not predictable.

Tool Count2/5

With 34 tools, this server exceeds the 25-tool threshold for 'too many'. While the breadth covers many utility domains, the count is heavy and some tools could be consolidated or removed. A more focused set would reduce cognitive load and misselection risk.

Completeness3/5

The utility set covers web, PDF, CSV, model, task, and dev tooling well, but there are notable gaps in resource lifecycles. Apps have build/list/get but no update/delete, and memories support remember/recall but no forget. These missing operations could create dead ends for agents.

Resources