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chacharin

mcp-lightrag

by chacharin

insert_texts

Add multiple raw text entries to a knowledge graph in a single batch call, returning a track ID to monitor processing status.

Instructions

Insert multiple pieces of raw text into the knowledge base in one call. Returns a single track_id covering the whole batch -- poll with get_track_status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textsYesThe text contents to add to the knowledge base. None may be empty.
file_sourcesNoSource name for each text, in the same order as texts. Always set this so query() references can name where an answer came from.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden. It discloses batch-level tracking via a single track_id and directs the caller to poll get_track_status, which surfaces an asynchronous workflow. It does not discuss partial-failure behavior or side effects, but the core execution model is transparent.

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 with no filler. The core action is front-loaded, and the return/status contract is stated compactly in the second sentence. Every word contributes.

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 low complexity (two params, one required), full schema coverage, and an output schema, the description covers input, return value, and polling flow. The only notable gap is explicit routing among the sibling insert/upload tools, but the batch semantics are sufficiently clear.

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%, so the schema already documents texts and file_sources well. The main description adds no parameter details, but the schema's file_sources description goes beyond basic semantics by advising to always set it for query() provenance. Baseline 3 is appropriate.

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 action ('Insert multiple pieces of raw text into the knowledge base'), a clear resource ('knowledge base'), and a distinguishing scope ('multiple... in one call'). This differentiates it from the sibling insert_text and upload_file tools without needing to inspect schemas.

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?

The description implies batch usage and provides follow-up guidance ('poll with get_track_status'), but it does not explicitly contrast with insert_text or state when the batch variant should be preferred over alternatives. Usage context is present but alternatives/exclusions are left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.