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Ingest Knowledge

neuron_ingest_knowledge
Idempotent

Ingest content into a knowledge base. Supports deduplication via externalId and optional LLM processing (summarize, extract_facts, or custom instruction).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYesTitle for the knowledge entry
folderNoFolder: 'general', 'skills', 'contexts', 'documents', or 'faqs' (default: general)
sourceNoSource type (default: mcp)mcp
contentYesContent to ingest
sourceUrlNoSource URL for reference
externalIdNoUnique external ID for deduplication (e.g. file path, URL)
processingNoOptional LLM processing before storage
knowledgeBaseIdYesKnowledge base ID to ingest into

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already indicate idempotence (idempotentHint=true) and non-destructiveness (destructiveHint=false). The description adds behavior about deduplication via externalId and optional LLM processing, but lacks details on authorization, error states, or rate limits.

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 concise sentences: first states purpose, second adds key features. No redundant info, front-loaded with core action.

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?

With 8 parameters, nested objects, and no output schema, the description is adequate but lacks details on return values, expected behavior on duplicate externalId, or error handling. Schema covers parameters, so baseline is met.

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 covers all parameters (100% coverage). Description adds meaning by explaining that externalId enables deduplication and processing enables LLM modes, going beyond schema descriptions.

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 clearly states the tool ingests content into a knowledge base, with specific verbs and resource. It mentions key features like deduplication and LLM processing, but does not differentiate from sibling tools like neuron_create_kb_entry or neuron_sync_knowledge.

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?

No guidance on when to use this tool versus alternatives such as neuron_create_kb_entry or neuron_sync_knowledge. No exclusions or prerequisites 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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TDQS

B3.4/5.0
Disambiguation3/5

Most tools are clearly separated by resource type, but there is meaningful overlap in messaging entry points (send_message, send_whatsapp, compose_message, bot_api_send) and contact ingestion/sync tools (import_contacts, populate_contacts, sync_whatsapp_contacts). The descriptions help disambiguate, but with 309 tools an agent will frequently need to read closely to pick the right one.

Naming Consistency4/5

The overwhelming majority of tools follow a consistent verb_noun snake_case pattern: create_*, get_*, list_*, update_*, delete_*. Minor deviations like sales_stats, lead_stats, wallet_balance, and whoami break the pattern slightly, but overall naming is highly predictable.

Tool Count1/5

309 tools is an extreme count for any MCP server, even a broad platform. This creates significant cognitive load and navigation overhead for agents, and far exceeds the well-scoped 3-15 tool range where coherence is strongest.

Completeness4/5

The tool surface is remarkably comprehensive across bots, contacts, campaigns, flows, knowledge bases, personas, marketplace, wallet, and products. Minor gaps exist — lead sources lack update/delete tools, and there is no single get_task or get_webhook alongside their list/update/delete counterparts — but these are workable gaps rather than dead ends.

Resources