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Clean a table against a stated schema (paid, $0.02 USDC)

clean_table
Read-onlyIdempotent

Normalise a delimited table to the fields you name: dates to ISO, money to numbers, text collapsed, optional deduplication. Anything that cannot be read with certainty is returned as an exception with a reason, never guessed. Every delivered cell is re-checked against its source token before the response is built. Deterministic. PAID: $0.02 USDC on Base mainnet, settled by Circle Gateway via x402 v2; call without payment to receive the PaymentRequired terms.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
csvYesThe delimited table, including its header row (1–48000 bytes, at most 2000 data rows).
fieldsYesThe agreed schema: which source column becomes which output field, and of what type.
dateOrderNoYour system's date order. The file's own proof always wins over this.
dedupeKeyNoOutput field to deduplicate on. Omit for no deduplication.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Despite having useful annotations (readOnlyHint, idempotentHint), the description adds substantial behavior beyond them: deterministic behavior, a never-guess policy, exception-with-reason returns, per-cell re-checking, and the exact payment flow including the PaymentRequired response when unpaid. It enriches the annotations without contradicting them.

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?

Core behavior is front-loaded in the first sentence, followed by short, high-signal statements about exceptions, verification, determinism, and payment. No sentence is filler; even the payment sentence adds the x402/PaymentRequired flow rather than merely repeating the title's price.

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?

The description is comprehensive for a tool with no output schema: it explains outcome categories, exception semantics, idempotence, and payment prerequisites. The main gap is that the exact response envelope is implied ('exceptions are returned', 'delivered cells') rather than explicitly enumerated, but the agent has enough to invoke and handle the result correctly.

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 already 100%, so the baseline is 3; the description earns more by adding meaning absent from the schema: dates become ISO, money becomes numbers, text collapses, and deduplication is optional. The transformation semantics are explicitly stated beyond the raw property definitions.

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 opens with a specific verb and resource — 'Normalise a delimited table' — and enumerates concrete transformations: dates to ISO, money to numbers, text collapsed, optional deduplication. It is unmistakably distinct from the only sibling (mm_status) and adds paid service context rather than restating the tool name.

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 context for use is clear: use it when a delimited table needs normalization against a stated schema, with exceptions surfaced rather than guessed. It does not explicitly name an alternative condition or a when-not-to-use, but the sibling set is minimal and no confusing alternative exists, so this is strong but not exhaustive.

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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