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csvbox-mcp-server

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Generate CSVBox Sheet JSON

generate_sheet_json

Turn natural-language importer descriptions into complete CSVBox sheet JSON, including columns, destinations, and security settings, with validation. Requires an Anthropic or OpenAI API key.

Instructions

Generate a COMPLETE CSVBox sheet (title, sheet_columns, destinations, webhooks, security_settings, steps) from a natural-language prompt using a configured LLM. Does NOT call the CSVBox API. Returns { sheet, source, validation }. Requires ANTHROPIC_API_KEY or OPENAI_API_KEY; without one it returns an error pointing to the create_csvbox_sheet MCP prompt. Input: { prompt }.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesNatural-language description of the importer, e.g. "Create employee importer with columns name, email, salary, joining date; destination as testapi; allow only xlsx files".
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses several key behaviors: no API call, key requirements, error fallback, and the return shape { sheet, source, validation }. However, it does not describe whether the call is synchronous, how long it may take, token/rate-limit considerations, or the meaning of the returned 'source' and 'validation' fields — moderate disclosure but not rich.

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?

Three sentences, all load-bearing: the exclusions ('Does NOT call...') and key requirement ('Requires ANTHROPIC_API_KEY...') are front-loaded before the input spec. The use of ALL-CAPS for emphasis is a minor stylistic distraction, but overall it is compact with no wasted sentences.

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?

For a single-input-parameter tool with no output schema and no annotations, the description covers the essentials: what it returns, its prerequisites, a common failure mode, and what the prompt parameter expects. The only notable gap is the absence of a minimal output/JSON example structure, which would help the agent gauge the result format.

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?

The single parameter prompt is fully documented in the schema (100% coverage, with a concrete example). The description adds only 'natural-language prompt' context, which marginally reinforces but does not extend the schema. Baseline 3 is appropriate where the schema already carries the parameter meaning.

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

Purpose4/5

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

The description uses a specific verb+resource ('Generate a COMPLETE CSVBox sheet') and enumerates the exact fields produced (title, sheet_columns, destinations, webhooks, security_settings, steps). It distinguishes itself from the API-calling siblings by explicitly stating 'Does NOT call the CSVBox API,' which is the key differentiator among the sibling list.

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 states the enabling condition (ANTHROPIC_API_KEY or OPENAI_API_KEY required) and the failure behavior when absent (returns an error pointing to create_csvbox_sheet). It clearly implies this tool generates JSON only and is the predecessor to an API-calling tool, though it doesn't name an explicit 'when not to use' sibling or a direct comparison to create_importer_from_prompt.

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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