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Knowledge Manage Tool

knowledge_manage
Destructive

Create 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

TableJSON Schema
NameRequiredDescriptionDefault
actionYesAction to perform: list, create, ingest, search, delete
deadline_msNoOptional: 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_idNoFilter by linked agent UUID
statusNoFilter by status: idle, ingesting, ready, error
limitNoMax results (default 20)
nameYesHuman-readable name for the knowledge base
descriptionNoOptional description
knowledge_base_idYesUUID of the knowledge base
contentYesRaw text content to ingest
source_nameNoDisplay name for this source (e.g. filename, URL)manual
source_typeNoSource type: text, file, or urltext
reindexNoIf true, delete existing chunks for this source before re-ingesting
queryYesNatural language query
top_kNoNumber of results to return (default 5, max 20)
Behavior5/5

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

The description discloses critical behaviors: ingestion runs an embedding job consuming credits, search is hybrid cosine similarity with keyword fallback, and delete is destructive. This adds substantial context beyond the annotations, which only indicate destructiveHint.

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?

The description is front-loaded with a concise summary, then breaks down each action in a structured bullet list. Every sentence adds value without redundancy, making it easy for an agent to parse.

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 the tool's complexity and lack of output schema, the description covers key aspects: actions, credit cost, search method, and destructive delete. Minor gaps include no explicit mention of return formats for list/search, but overall it is adequate.

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?

While the input schema has 100% coverage with parameter descriptions, the tool description groups parameters by action (e.g., ingest uses knowledge_base_id plus content/url/file_id), adding semantic meaning beyond individual parameter definitions.

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?

The description clearly states the tool manages per-team knowledge bases with vector indexing. It lists five specific actions (list, create, ingest, search, delete), each with a clear verb and resource, distinguishing it from sibling 'manage' tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

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, such as listing vs. creating, and notes that ingest consumes credits. It does not explicitly exclude alternative tools, but siblings are distinct categories, so usage context is clear.

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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