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by io.github.JcJamet
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split_chunks

Read-onlyIdempotent

Split text into chunks of at most N tokens (cl100k_base: ~4 chars/token) with optional overlap. Designed for RAG ingestion pipelines.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesText to split into chunks
overlapNoToken overlap between consecutive chunks (default: 0)
chunk_tokensYesMaximum tokens per chunk (10–8000)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
chunksNo
chunk_countNo
overlap_tokensNo
tokens_per_chunkNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / chunk_tokens / description
      Previous value: -"Maximum tokens per chunk (10–8000)"New value: +"Maximum tokens per chunk (10–8000)"
  2. Changed1 schema field changed
    • addedOutput schema / properties
      Added value: +{
+  "chunk_count": {
+    "type": "number"
+  },
+  "chunks": {
+    "type": "array"
+  },
+  "overlap_tokens": {},
+  "tokens_per_chunk": {}
+}
  3. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "type": "object"
+}
  4. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds the tokenizer detail ('cl100k_base: ~4 chars/token') and optional overlap, which helps agents predict output behavior without repeating annotation info.

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 action; no filler. The first sentence conveys the core behavior and the second adds context.

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

Completeness4/5

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

Given the tool's low complexity, the description plus the full input schema and output schema provide sufficient context. The only gap is no explicit alternative reference, but the use case is adequately scoped.

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 all parameters are documented. The description adds the cl100k_base tokenizer and char/token ratio, which clarifies how chunk_tokens is interpreted—beyond what the schema provides.

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 opens with 'Split text into chunks' – a specific verb and resource that clearly distinguishes from siblings like count_tokens and truncate_to_tokens. The mention of 'at most N tokens' and 'overlap' further specifies the tool's unique behavior.

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?

The description states 'Designed for RAG ingestion pipelines,' giving a clear context of when to use it. It does not explicitly name alternatives or exclusions, but the chunking use case is self-evident and no misleading guidance is present.

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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