Wever Labs Agentic Rails

Data evidence check

wever_data-evidence-check

Read-only

DiligenceOps: inspect a submitted JSON sample and return a signed field-by-field verification report. Checks required fields, empty values, expected and consistent types, timestamp parsing and age, source/provider attribution, shape, record count and detectable duplicate keys. Maximum payload 256 KB (262144 UTF-8 bytes). required_fields leads the report; dataset_type selects defaults only when required_fields is absent. endpoint is recorded as supplied without fetching. Facts describe the submitted sample, not seller ratings or underlying data truth. POST /api/data-evidence-check. Paid URL: 0.10 USDC per call on Base via x402 exact, with the shared 10 signed-wallet free calls per rolling 30 days. Bind X-Wever-Wallet-Proof to the exact JSON arguments and /api/data-evidence-check. Read /api/data-evidence-check-contract for the field and freshness rules, receipt verification and public key. Human page: https://weverlabs.com/data-evidence-check/.

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": "wever_data-evidence-check"
  }
}

Input Schema

Name	Required	Description
`payload`	Yes	Sample JSON object or serialized JSON. Maximum 262144 UTF-8 bytes (256 KB). A serialized sample may contain an object or array of records.
`endpoint`	No	Original URL, recorded exactly as supplied. Processing uses the submitted sample.
`dataset_type`	No	Selects default field expectations only when required_fields is absent.
`required_fields`	No	Absolute JSON pointers, or dot paths with [index] and [*]. Simple paths are relative to each detected record. An explicit empty array selects no default expectations.

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 10, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already mark this read-only and open-world, and the description adds substantial context beyond them: 256 KB payload cap, 0.10 USDC per call, shared free calls, signed-wallet proof binding, endpoint recorded without fetching, and a contract endpoint for freshness and receipt rules.

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

Conciseness4/5

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

The description is front-loaded with purpose and checks, then moves to parameters, behavior, payment/auth, and contract references. It is dense but nearly every sentence adds operational value; a little tightening of repeated size/payment details would improve it.

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

Completeness5/5

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

For a paid, read-only verification tool with no output schema, the description covers what is checked, input limits, parameter interactions, payment/auth requirements, non-fetching behavior, and where to read the contract. An agent has enough context to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so parameter documentation is already strong, but the description adds relational meaning: required_fields leads the report and dataset_type selects defaults only when required_fields is absent; endpoint is recorded as supplied.

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?

States a specific verb ('inspect') and resource ('submitted JSON sample') and names the output ('signed field-by-field verification report'), then enumerates the checks performed. The scope is distinctive enough that an agent can identify this as the sample-verification tool even without sibling names.

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

Usage Guidelines4/5

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

Gives clear context for diligence use and explicitly limits interpretation ('Facts describe the submitted sample, not seller ratings or underlying data truth'), but does not name when to prefer or avoid this tool versus sibling diligence/rail tools.

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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