JSON to TOON
Server Details
Convert JSON to TOON (about 40% fewer LLM tokens) and TOON back to JSON. Free, no auth.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
TDQS
Scored across 2 tools
The two tools are exact inverses of each other: json_to_toon converts one direction and toon_to_json the other. There is no plausible way to confuse them, and the descriptions reinforce the directionality.
Both names follow the identical source_to_target pattern (json_to_toon, toon_to_json), which is perfectly predictable and self-documenting.
Two tools is on the thin side per the rubric, but for a pure format converter this is the minimal complete surface — one encoder and one decoder. Adding more would be redundant rather than useful.
The round-trip lifecycle (encode and decode) is fully covered with no dead ends. Only minor niceties like validation, batch conversion, or formatting options are absent, which agents can work around.
Available Tools
2 toolsjson_to_toonConvert JSON to TOONARead-onlyInspect
Convert JSON to TOON (Token-Oriented Object Notation), a compact format that uses about 40% fewer LLM tokens than JSON. Best for uniform arrays of objects.
| Name | Required | Description | Default |
|---|---|---|---|
| json | Yes | JSON document to convert (as a string) | |
| delimiter | No | Array delimiter (default comma) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered without description help. The description adds useful context that the output is roughly 40% smaller in tokens, but says nothing about failure modes for malformed JSON, size limits, or what is returned. Adds some value beyond annotations, not rich behavioral context.
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 tight sentences with no filler: the conversion and format definition come first, the applicability hint second. Every clause carries information an agent needs.
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 two-parameter, annotation-covered converter this is close to complete: the operation, the output format's purpose, and the best-fit input shape are all stated. Since no output schema exists, the description could have said more about the returned value (e.g. a TOON string) or error behavior on invalid JSON, which is the only real gap.
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 100%, with both 'json' and 'delimiter' (enum: comma/tab/pipe) documented in the schema itself. The description adds no detail about input format expectations or how the delimiter choice affects output, so the baseline 3 applies.
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 states a specific verb and resource ('Convert JSON to TOON') and defines the target format, so the direction of conversion is unambiguous. It never names its sibling toon_to_json to explicitly confirm it is the inverse operation, but the verb+resource pairing leaves no real ambiguity.
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?
'Best for uniform arrays of objects' gives a concrete applicability condition that tells the agent when this tool is a good fit. There is no when-not guidance and no explicit reference to the toon_to_json alternative, but the usage context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
toon_to_jsonConvert TOON to JSONARead-onlyInspect
Decode a TOON document back to pretty-printed JSON.
| Name | Required | Description | Default |
|---|---|---|---|
| toon | Yes | TOON document to decode |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds the useful detail that output is pretty-printed JSON, but says nothing about error behavior for malformed/unsupported TOON input, which is the main behavioral risk for a decoder.
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?
A single front-loaded sentence with zero filler; the action, input, and output format are all conveyed without waste.
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 trivial one-parameter conversion tool with no output schema, the description tells the agent the input type and the exact return format, which is essentially everything needed to call it. Only the failure mode for invalid TOON input is unaddressed.
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 100% for the single 'toon' parameter, so the schema fully documents the input. The description adds no syntax, format, or encoding detail beyond what the schema already states, making the baseline 3 appropriate.
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 (Decode) and resource (a TOON document) plus the output format (pretty-printed JSON), which makes the direction of conversion unambiguous. It never names the sibling json_to_toon, so the differentiation is implicit rather than explicit, but 'back to JSON' makes the inverse relationship clear.
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 implied by the direction stated in the description and the tool name; an agent can infer this is the TOON→JSON path. There is no explicit when-to-use statement, no mention of when not to use it, and no reference to the json_to_toon alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
- First observed
json_to_toon - First observed
toon_to_json
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