Convert tab-separated values into a JSON array of row objects. Pure CPU, instant.
Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {
"tsv": "<tsv>"
},
"name": "tsv_to_json"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| tsv | Yes | TSV text to convert |
Changes observed during successful MCP inspections.
Input schema / properties / tsvAdded value: +{
+ "description": "TSV text to convert",
+ "type": "string"
+}Input schema / requiredAdded value: +[
+ "tsv"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, and 'Pure CPU, instant' usefully discloses that there is no network call, no side effects and no meaningful latency. It omits important behavioral detail: whether the first row is treated as a header, how ragged rows or embedded tabs/newlines are handled, and whether values are coerced to numbers or kept as strings.
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?
Two short sentences, zero waste, with the operation stated first and the performance/safety note second. Every clause earns 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?
For a single-parameter pure function with no output schema, the description covers the action and even sketches the return shape ('JSON array of row objects'). The remaining gap is header-row and malformed-input semantics, which materially affect output shape.
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?
There is one required parameter and schema coverage is 100%, so the baseline is 3. The description adds no meaning beyond the schema's 'TSV text to convert' — nothing about size limits, encoding expectations, or whether a header line is expected.
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?
States a specific verb and resource pair ('Convert tab-separated values into a JSON array of row objects'), which is enough to distinguish it from csv_to_json, json_to_csv and json_to_yaml without opening schemas. It does not explicitly name the sibling it supersedes, so it stops short of 5.
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?
Usage is only implied by the input format — an agent infers 'use this when your data is tab-delimited.' There is no explicit when-to-use, no exclusion such as 'use csv_to_json for comma-delimited input', and no guidance on inputs that are already JSON.
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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