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cuba_ingesta

Ingest structured items, parse raw text, or auto-extract facts from conversations via LLM sampling. Enables deduplication and conflict handling for knowledge memory.

Instructions

Bulk knowledge ingestion. 'ingest': array of {entity_name, content, observation_type} items. 'parse': split long text by paragraphs + heuristic classify. 'auto_extract' (v0.11): the calling client's LLM extracts salient durable facts from a turn/conversation via MCP Sampling ($0, no API key) and ingests them — the automatic-extraction that mem0/Zep have. All routes share the dedup/PE-gating/embedding pipeline; none delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textNoRaw text: paragraphs to split (parse) or a turn/conversation to extract facts from (auto_extract)
itemsNoArray of {entity_name, content, observation_type?} objects (for ingest action, max 200)
actionYesIngestion mode. 'ingest' for structured items, 'parse' for raw text splitting, 'auto_extract' for LLM extraction via MCP sampling.
untrustedNoSet when the text came from somewhere you do not control (a fetched page, a pasted document, a third party). Everything extracted lands quarantined — stored and inspectable via cuba_eco action=pending, but withheld from cuba_faro until promoted. Default false.
entity_hintNoOptional main-subject hint for auto_extract (biases entity_name)
entity_nameNoEntity to attach parsed observations to (for parse action)
supersede_conflictsNov0.11 (auto_extract): when a new fact replaces/contradicts an existing related one, ask the judge and mark the old observation superseded (knowledge-update; never deletes). Default false.
Behavior4/5

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

No annotations provided, so the description carries the full burden. It discloses that all routes share a dedup/PE-gating/embedding pipeline and none delete, and explains the quarantine behavior via the 'untrusted' parameter. This adds meaningful behavioral context beyond the schema.

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?

The description is well-structured with a clear front-loaded purpose and bullet-like explanations for each action. It is moderately concise, though slightly verbose; each sentence earns its place.

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?

The description covers actions, parameters, and behavioral aspects adequately, but lacks information about return values or output format. Since no output schema exists, the description could be more complete by describing what each action returns.

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 description coverage is 100%, but the description adds extra meaning: it explains each action mode, the quarantine behavior of 'untrusted', and the knowledge-update feature of 'supersede_conflicts'. This provides value beyond the parameter descriptions in the schema.

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 'Bulk knowledge ingestion' and lists three specific actions (ingest, parse, auto_extract) with brief explanations. It distinguishes the tool's function but does not explicitly differentiate from sibling tools like cuba_faro or cuba_eco.

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 provides context for when to use each action (e.g., 'ingest for structured items', 'parse for raw text splitting'), but lacks explicit guidance on when not to use this tool or mentions of alternatives among siblings.

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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