Free CSV validation and repair, up to 20 data lines, no storage. Annual browser licence available.
Scored across 1 tool
There is only one tool, so there is no possibility of selecting the wrong tool or confusing overlapping purposes. The tool name and description clearly identify its single function.
The sole tool name repair_csv follows a clear verb_noun snake_case convention. With only one tool there are no inconsistent naming patterns to create confusion.
A single focused repair tool is coherent for this narrow utility, but the surface is thin by the rubric's standards for 1-2 tool servers. It may be sufficient for the core task but leaves no room for complementary operations such as validation-only or schema inspection.
The tool covers the central domain operation of validating and repairing CSV data, including normalization and formula escaping. Minor gaps exist because there is no separate validation-only or diagnostic tool, but agents can likely work around this using the repair operation.
Free CSV validation and repair: up to 20 data lines, no storage. Normalizes dates and amounts and escapes spreadsheet formulas. Use synthetic/redacted data. Full local browser tool and annual licence: https://api.rohnelt.dev/csv-repair/?ref=mcp-csv
| Name | Required | Description | Default |
|---|---|---|---|
| csv | Yes | CSV header plus at most 20 physical data lines. Use synthetic or redacted data. | |
| order | No | auto | |
| dedupe | No | ||
| decimal | No | . |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive, so the bar is lower. The description still adds substantive behavior: a hard 20-physical-line cap, no storage of inputs, automatic date/amount normalization, and spreadsheet-formula escaping — all things the annotations do not say. It stops short of describing the response or what happens when validation fails.
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 functional content is tightly front-loaded in the first two sentences, but the trailing 'Full local browser tool and annual licence: <URL>' is promotional rather than operational and does not help an agent decide or invoke. Roughly a quarter of the description does not earn its place.
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?
With four parameters, rich annotations, and no output schema, the agent still does not learn what the tool returns — a repaired CSV string, a validation report, or an error list — nor how partial repairs are communicated. Given the tool's validation focus, that return-shape gap is the main omission.
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 only 25%, with just 'csv' documented. The mention of normalizing dates and amounts loosely signals what 'order' and 'decimal' govern, and the enums are largely self-explanatory, but 'dedupe' is never addressed and no defaults or interactions are explained in the description.
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 uses specific verbs and resources — it validates and repairs CSV, normalizes dates and amounts, and escapes spreadsheet formulas. That is much more than restating the name. With no siblings to distinguish from, the only thing keeping it off a 5 is that the paywall/licence sentence dilutes the otherwise crisp purpose statement.
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?
'Use synthetic/redacted data' plus 'up to 20 data lines, no storage' gives a real constraint on when and how to call it. However, there is no statement of what kinds of CSV problems this is appropriate for versus doing nothing, and no explicit exclusions beyond the size limit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
repair_csvFree CSV and SRT diagnostics with deterministic $3 one-file repairs.
Validate and clean CSV before import. Find duplicates, missing values and invalid dates with row-level reports. Apply only the cleanup you request; files are not retained. Run view_csv_demo free without a key. Custom CSV: EUR 9 for 100 operations, valid 90 days, no subscription. Setup: https://check.orvel.dev/docs/
Inspect, filter, sort and edit CSV text with four free tools. No account or API key required.
Convert and clean tables: CSV, JSON, dedupe, stats. Free.
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