Agent Resilience & Networking MCP

streaming_jsonlines_validator

Validates and repairs gigabyte-scale JSON Lines streams, fixing truncated strings, unescaped newlines, trailing commas, and encoding glitches.

Instructions

Streams gigabyte-scale JSON Lines (JSONL) datasets, auto-repairing truncated strings, unescaped newlines, trailing commas, and encoding glitches. (0.025 USDC on Base L2)

Tool call template

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": {
      "payload": "<payload>"
    },
    "name": "streaming_jsonlines_validator"
  }
}

Input Schema

Name	Required	Description	Default
`payload`	Yes	Input parameters or JSON string payload for the tool execution
`paymentSignature`	No	Base L2 USDC micropayment signature or transaction hash for x402 settlement

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Oct 3, 2026

TDQS

B3.4/5.0

Behavior3/5

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

There are no annotations, so the description is the sole source of behavioral information. It discloses that the tool streams data and auto-repairs specific error types, which are core behaviors. However, it does not mention side effects, performance characteristics, failure modes, or whether the input is modified in place.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two concise sentences with no filler. The main functionality is stated first, followed by the cost. Every word contributes information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has no annotations and no output schema, so the description must explain the full contract. It explains what the tool does but omits critical details such as input format expectations (inline JSONL vs. file path), output structure, and any limitations. The description is not sufficient for an agent to invoke it correctly without further inference.

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 schema already provides descriptions for both parameters, so the baseline score is 3. The tool description adds context that the payload should contain JSONL data, but it does not clarify whether the payload is inline content or a reference to an external dataset. The paymentSignature parameter's purpose is already adequately described in the schema.

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

Purpose5/5

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

The description clearly identifies a specific function: streaming JSONL datasets and repairing common formatting errors. It mentions concrete error types (truncated strings, unescaped newlines, trailing commas, encoding glitches), which makes the tool's purpose distinct from generic JSON repair tools. The scope is unambiguous even without naming sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to choose this tool over alternatives such as repair_malformed_json or robust_csv_stream_repair. There is no mention of prerequisites, such as requiring a payment signature, or conditions that would make this tool inappropriate. An agent is left to infer usage from the context alone.

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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