Knowledge Manage Tool
knowledge_manageCreate and manage per-team knowledge bases with vector-indexed documents. Agents search via hybrid cosine similarity and keyword fallback at runtime.
Instructions
Per-team knowledge bases — vector-indexed document collections agents can search at runtime. Ingestion runs an embedding job (consumes credits via the team's embedding provider). search is hybrid: cosine similarity over pgvector + keyword fallback.
Actions:
list (read) — all knowledge bases for the team.
create (write) — name, description.
ingest (write — costs embedding credits) — knowledge_base_id; one of: content (raw text), url (fetched + extracted), file_id.
search (read) — knowledge_base_id, query; optional limit (default 10), threshold.
delete (DESTRUCTIVE) — knowledge_base_id. Drops all ingested chunks and embeddings.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| action | Yes | Action to perform: list, create, ingest, search, delete | |
| deadline_ms | No | Optional: max wall-clock time (ms) the tool may spend. If exceeded during the call, returns a DEADLINE_EXCEEDED error. Minimum 100 ms. Leave unset for no deadline. | |
| agent_id | No | Filter by linked agent UUID | |
| status | No | Filter by status: idle, ingesting, ready, error | |
| limit | No | Max results (default 20) | |
| name | Yes | Human-readable name for the knowledge base | |
| description | No | Optional description | |
| knowledge_base_id | Yes | UUID of the knowledge base | |
| content | Yes | Raw text content to ingest | |
| source_name | No | Display name for this source (e.g. filename, URL) | manual |
| source_type | No | Source type: text, file, or url | text |
| reindex | No | If true, delete existing chunks for this source before re-ingesting | |
| query | Yes | Natural language query | |
| top_k | No | Number of results to return (default 5, max 20) |