Deterministically repair malformed JSON from LLM output (strip code fences, fix trailing commas / single quotes / unquoted keys, balance braces). Payment: $0.005 USDC on Base via x402.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Malformed JSON text to repair. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses deterministic behavior, the types of malformations addressed, and payment details (USDC on Base via x402). It does not mention failure modes or return format, but the provided transparency is valuable.
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, well-structured sentence with front-loaded purpose, followed by specific examples of repairs and payment information. Every clause adds value with no unnecessary words.
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 simple one-parameter tool, the description covers purpose, usage context, and payment. It omits the return value format and failure behavior, but given the simplicity and lack of output schema, it is nearly complete.
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?
The schema covers 100% of the parameter descriptions ('text' is described as 'Malformed JSON text to repair'). The description adds behavioral details about what repairs are performed but does not add new semantics for the parameter itself. Baseline 3 is 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?
The description uses a specific verb 'repair' with a clear resource 'malformed JSON' and context 'from LLM output'. It lists concrete operations (strip code fences, fix trailing commas, single quotes, unquoted keys, balance braces), making it easily distinguishable from the unrelated sibling tools.
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?
It explicitly states the intended use case (repairing LLM output) and provides context for when to invoke it. However, it does not mention alternatives or when not to use it, which would warrant a 5. Since siblings are unrelated, the clear context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: crypto market data, free sample, DNS lookup, URL extraction, JSON repair, and USDC analytics. Even the two crypto tools are differentiated by the free sample variant.
All tool names follow a consistent snake_case pattern with a noun_verb or descriptive style (e.g., crypto_market_pulse, dns_lookup, extract_urls). No mixed conventions.
Six tools is a reasonable count, though the server covers a wide range of utilities from JSON repair to crypto analytics. The count is appropriate but the scope feels slightly scattered.
For a utility server, the coverage is adequate: it provides core functions like JSON repair, DNS lookup, and URL extraction. Minor gaps exist (e.g., no generic text processing), but the stated utilities are well covered.