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ragflow-kb-mcp
by feci

ragflow_delete_chunks

Destructive

Permanently removes specified chunks from a document in a RAGFlow knowledge base, letting agents clean up outdated or incorrect content by deleting chunks by ID.

Instructions

Permanently delete chunks from a document.

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": {
      "dataset_id": "<dataset_id>",
      "document_id": "<document_id>",
      "chunk_ids": "<chunk_ids>"
    },
    "name": "ragflow_delete_chunks"
  }
}

Input Schema

NameRequiredDescriptionDefault
chunk_idsYesIDs of the chunks to delete
dataset_idYesDataset (knowledge base) ID
document_idYesDocument ID

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare destructiveHint=true and readOnlyHint=false, so the safety profile is known. The word 'Permanently' adds useful context that deletion is irreversible, which annotations do not state outright, but nothing is said about scope of impact, permission requirements, or whether removed chunks can be recovered.

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?

A single front-loaded sentence with no filler. It is efficient, though 'Permanently' is the only qualifier and the sentence could carry one more clause of routing guidance without bloat.

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?

Schema is fully documented and an output schema exists, so return values need not be explained. However, for a destructive, three-required-parameter operation with no annotations beyond safety hints, the description leaves gaps around irreversible scope and whether any confirmation or dependency checks exist.

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%, with chunk_ids, dataset_id, and document_id all documented in the schema itself. The description adds no further meaning about how these three identifiers must relate (e.g., that the document must belong to the dataset). Baseline 3 applies when the schema carries the parameter semantics.

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 specific verb and resource ('Permanently delete chunks from a document'), which is enough to separate it from ragflow_delete_documents, ragflow_update_chunk, and ragflow_add_chunk. It does not explicitly name the sibling boundary, but the target resource is unambiguous.

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?

There is no guidance on when to use this instead of ragflow_update_chunk, ragflow_set_chunks_availability, or ragflow_delete_documents. No prerequisites or preconditions (e.g., whether chunks must be uncommitted or whether the document/dataset must match) are mentioned.

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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