automaton

web_extract__chunk

Split a page into token-counted, paragraph-aware chunks for RAG pipelines. Price: $0.01 per call in USDC on Base (eip155:8453), paid with x402 in params._meta["x402/payment"]. HTTP: POST https://api.firstmention.uk/s/web-extract/chunk

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": {
      "url": "<url>"
    },
    "name": "web_extract__chunk"
  }
}

Input Schema

Name	Required	Description	Default
`url`	Yes
`overlap`	No
`max_tokens`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed8 schema fields changedNov 7, 2026
- addedInput schema / properties / max_tokens / maximum
```
Added value: +8000
```
- addedInput schema / properties / max_tokens / minimum
```
Added value: +50
```
- changedInput schema / properties / max_tokens / type
```
Previous value: -"number"New value: +"integer"
```
- addedInput schema / properties / overlap / maximum
```
Added value: +500
```
- addedInput schema / properties / overlap / minimum
```
Added value: +0
```
- changedInput schema / properties / overlap / type
```
Previous value: -"number"New value: +"integer"
```
- addedInput schema / properties / url / format
```
Added value: +"uri"
```
- addedInput schema / required
```
Added value: +[
+  "url"
+]
```
AddedNov 6, 2026

TDQS

A3.5/5.0

Behavior4/5

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

With no annotations, the description carries full behavioral burden and does disclose the most consequential trait: this is a paid call ($0.01 in USDC on Base via x402 in params._meta['x402/payment']) plus the HTTP endpoint. That payment requirement is genuinely useful and not derivable from the schema. It does not describe the return shape, chunk-size defaults, or error/payment-failure behavior, which holds it below 5.

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?

Two sentences, front-loaded with the core purpose before the payment/transport details. Nothing is wasted and the structure lets an agent grasp intent immediately.

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

Completeness3/5

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

For a paid, three-parameter tool with no annotations and no output schema, the description covers purpose and payment well but leaves return values, chunk defaults, and parameter behavior undocumented. Adequate to attempt a call, but an agent still has gaps before invoking it confidently.

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

Parameters2/5

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

Schema description coverage is 0% across three parameters (url, overlap, max_tokens). The phrase 'token-counted' hints at max_tokens and 'paragraph-aware' loosely hints at chunking strategy, but the description never explains overlap semantics, defaults, or units, so it fails to compensate for the schema gap.

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

Purpose4/5

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

The description states a specific verb and resource: 'Split a page into token-counted, paragraph-aware chunks for RAG pipelines.' The qualifiers 'token-counted' and 'paragraph-aware' distinguish it functionally from extraction siblings like web_extract__extract. It stops short of naming the sibling it differs from, which keeps it at 4 rather than 5.

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

Usage Guidelines3/5

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

'for RAG pipelines' implies the use case, giving the agent a rough sense of context. However, there is no explicit when-to-use or when-not-to-use guidance, and no pointer to alternatives such as web_extract__extract or web_extract__batch.

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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