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Split a page into chunks

ragfeed_chunks
Read-onlyIdempotent

Fetch one page and split it along its headings into chunks ready for a vector database: each chunk has a stable UUID, the text to embed and flat metadata (title, heading path, URL, language). Costs 1 credit per page.

Input Schema

NameRequiredDescriptionDefault
urlYesFull http(s) address of the page or document (HTML, PDF, Word, Excel, …).
offsetNoSkip this many chunks (to page through long documents).
profileNoChunk size: precise ≈250, balanced ≈500, broad ≈1000 tokens.balanced
max_chunksNoMost chunks to return in this answer.
max_tokensNoExact upper limit per chunk instead of the profile.
id_namespaceNoNamespace for the chunk IDs, e.g. a project name.
embedding_modelNoCount tokens for this embedding model so no chunk exceeds its limit.

Output Schema

NameRequiredDescriptionDefault
urlNoAddress of the page.
errorNoOnly on failed calls (isError true).
chunksNoVector-database records: id (UUID), text to embed and flat metadata.
offsetNoPosition of the first returned chunk.
nextOffsetNoOffset for the next part; null when nothing is left.
totalChunksNoNumber of chunks of the whole page.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and non-destructiveness, so the safety profile is covered. The description adds genuinely new behavioral context the annotations cannot convey: a cost of 1 credit per page and the shape of the produced chunks (stable UUID, embeddable text, flat metadata). It stops short of covering failure modes or credit-refund behavior.

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?

A single front-loaded sentence: the action and output come first, the cost note is last. No filler and nothing repeated from the schema.

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?

With an output schema present, return values need not be described, and annotations cover the safety profile; the description supplies the credit cost and chunk contents that structured fields cannot. The only gap is error/limit behavior for a 7-parameter tool with paging and token-cap interactions.

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?

Schema description coverage is 100%, including defaults, ranges and enum meanings for profile and embedding_model, so the schema carries the parameter burden and baseline 3 applies. The description restates output metadata fields but adds no syntax or interaction detail (e.g. how max_tokens overrides profile, or how offset interacts with max_chunks).

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?

States a concrete verb and resource ('Fetch one page and split it along its headings into chunks') plus the destination use case (vector database), which is more specific than the title alone. However, it never distinguishes itself from close siblings like ragfeed_extract or ragfeed_scrape, which also fetch a single page, so the agent must infer the difference from the schema.

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?

'Fetch one page' implies the single-page scope versus ragfeed_crawl, and the chunk-oriented output implies a vector-DB ingestion use case. But there is no explicit when-to-use, when-not-to-use, or named alternative; the routing guidance is only implied.

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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