x402 JSON Repair
Server Details
Pay-per-call JSON repair + JSON Schema validation for AI agents (USDC on Base, x402).
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 5/5 across 2 of 2 tools scored.
The two tools have completely distinct purposes: one repairs malformed JSON, the other converts tabular text to JSON. There is no overlap or ambiguity.
Both names are descriptive but follow slightly different patterns: 'structured_json_repair' uses a noun-verb structure, while 'tabular_to_json' uses a noun-preposition-verb. However, the naming is clear and consistent in style (snake_case).
With only 2 tools, the server is highly focused on JSON repair and tabular conversion. The count is appropriate for its narrow scope; adding more tools would dilute coherence.
The server covers the core tasks of repairing JSON and converting tabular data to JSON. It lacks a tool for operations like validating clean JSON or converting back to tabular, but these are beyond its stated purpose.
Available Tools
2 toolsstructured_json_repairStructured JSON RepairARead-onlyIdempotentInspect
Repair messy or invalid JSON (the kind LLMs and tools often emit) into clean, valid JSON, and optionally validate/coerce it against a JSON Schema. Pure deterministic compute — no network or model calls.
What it fixes: trailing commas, single-quoted strings, unquoted keys, Python literals (None/True/False), NaN/Infinity, Markdown code-fence wrappers, and truncated/garbled tails.
When to use: you received text that should be JSON but JSON.parse fails, or you have JSON that must conform to a specific schema and want types coerced (e.g. "36" -> 36, "true" -> true).
When NOT to use: the input is already known-valid JSON and no schema check is needed.
Args:
input (string, required): the raw/malformed JSON text.
schema (object, optional): a JSON Schema (draft 2020-12) to validate and coerce against.
coerce (boolean, optional, default true): coerce primitive types to satisfy the schema before validating.
Returns structuredContent: { "ok": boolean, // true if valid JSON (and schema-valid when a schema was given) "data": any, // the repaired/validated JSON value; null if unfixable "changed": boolean, // true if any repair or coercion modified the input "errors": string[], // actionable messages when ok is false "repairs": string[] // description of each fix applied }
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Raw or malformed JSON text to repair. Examples: "{name: 'Ada', age: '36',}", a ```json fenced block, or a truncated '{"items":[1,2,3'. | |
| coerce | No | When true (default), coerce primitives to satisfy the schema before validating (e.g. "36" -> 36). | |
| schema | No | Optional JSON Schema (draft 2020-12) object to validate and coerce the repaired JSON against. |
Output Schema
| Name | Required | Description |
|---|---|---|
| ok | Yes | True if the result is valid JSON (and schema-valid when a schema was provided). |
| data | No | The repaired/validated JSON value (object, array, or primitive). null when repair failed. |
| errors | Yes | Actionable error messages when ok is false (empty when ok is true). |
| changed | Yes | True if any repair or coercion changed the input. |
| repairs | Yes | Human-readable description of each repair or coercion applied. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses behavioral traits beyond annotations: pure deterministic compute, no network/model calls, specifics of what it fixes (trailing commas, single quotes, etc.). No contradiction with annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false).
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?
Well-structured with clear sections (What it fixes, When to use, Args, Returns). Concise yet comprehensive – every sentence adds value, no fluff.
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?
Given the tool's complexity (3 params, optional schema validation), the description fully explains inputs, return structure, and edge cases. The output schema is not present but the return fields are fully documented in the description, making it complete for an agent to understand.
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 coverage is 100%, and the description adds significant value: examples for 'input' (truncated text, code fences), explains 'coerce' with type coercion examples, and details the return structure. This goes beyond the schema's minimal descriptions.
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 clearly states the tool's purpose: repair messy/invalid JSON into clean valid JSON, with optional schema validation. It distinguishes itself from the sibling tool 'tabular_to_json' which converts tabular data, making its unique role 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?
Explicitly provides when-to-use (e.g., JSON.parse fails, schema conformance needed) and when-not-to-use (input already valid JSON without schema check). This gives clear guidance for agent decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
tabular_to_jsonTabular to JSONARead-onlyIdempotentInspect
Convert messy tabular text into clean, typed JSON rows. Auto-detects CSV, TSV, or a Markdown table and returns one JSON object per row plus an inferred column/type summary. Pure deterministic compute — no network or model calls.
What it handles: delimiter sniffing (comma/semicolon/tab/pipe), quoted fields with embedded commas and newlines, BOM, ragged rows (padded/truncated), Markdown separator rows and escaped pipes, header auto-detection, and per-column type inference (integer/number/boolean/null/string).
When to use: you have CSV/TSV/Markdown-table text (often emitted by tools or LLMs) and want structured, typed rows — optionally validated/coerced against a JSON Schema.
When NOT to use: the data is already clean JSON, or it is HTML/xlsx/binary (not supported).
Args:
input (string, required): raw tabular text.
format ("auto"|"csv"|"tsv"|"markdown", default "auto"): force a format or auto-detect.
hasHeader ("auto"|"true"|"false", default "auto"): whether the first row is a header.
inferTypes (boolean, default true): coerce cells to number/integer/boolean/null; else keep strings.
schema (object, optional): JSON Schema (draft 2020-12) to validate/coerce each row object against.
Returns structuredContent: { "ok": boolean, // false if the input cannot be parsed as a table "format": "csv"|"tsv"|"markdown", "columns": [{ "name": string, "type": string }], "rows": [{ ... }], // one object per row, keyed by column name "rowCount": number, "changed": boolean, // true if any normalization/coercion happened "errors": string[], // actionable messages when ok is false "repairs": string[] // description of each normalization applied }
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Raw tabular text: a CSV/TSV block or a Markdown table. | |
| format | No | Force a parser or auto-detect (default 'auto'). | auto |
| schema | No | Optional JSON Schema (draft 2020-12) to validate/coerce each row object against. | |
| hasHeader | No | Whether the first row is a header. 'auto' uses a heuristic. | auto |
| inferTypes | No | When true (default), infer cell types (number/integer/boolean/null); else keep strings. |
Output Schema
| Name | Required | Description |
|---|---|---|
| ok | Yes | True if the input parsed as a table (and every row is schema-valid when a schema was given). |
| rows | Yes | One JSON object per data row, keyed by column name. |
| errors | Yes | Actionable error messages when ok is false (empty when ok is true). |
| format | Yes | The detected/used format. |
| changed | Yes | True if any normalization or coercion changed the input. |
| columns | Yes | Inferred column names and types. |
| repairs | Yes | Human-readable description of each normalization applied. |
| rowCount | Yes | Number of data rows returned. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds context: 'Pure deterministic compute — no network or model calls.' Details handling of edge cases (BOM, ragged rows, quoted fields, etc.) and that it returns errors/repairs, enriching beyond annotations.
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?
Well-structured with intro, capabilities, usage guidance, parameter list, and return structure. Front-loaded with purpose. Slightly verbose in the 'What it handles' list, but each detail adds value for a complex tool. Could be trimmed slightly but overall appropriate.
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?
Given 5 parameters, 100% schema coverage, output schema, and annotations, the description covers return values, error handling, and edge cases. No gaps evident. Complete for the tool's complexity.
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 coverage is 100% with descriptions for all 5 parameters. The description's Args list adds further context, e.g., 'format' can force a parser, 'hasHeader' uses heuristic, 'inferTypes' coerces cells, 'schema' validates/coerces. Adds meaning beyond schema alone.
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 starts with a clear verb+resource: 'Convert messy tabular text into clean, typed JSON rows.' It specifies auto-detection of CSV/TSV/Markdown and type inference. It distinguishes itself from sibling 'structured_json_repair' which handles JSON repair, not conversion.
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?
Explicit 'When to use' and 'When NOT to use' sections. States to use for CSV/TSV/Markdown text; not for clean JSON, HTML, xlsx, or binary. Provides clear context for decision-making.
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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