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chat_ask

Ask a question to a notebook's sources and get cited answers, recall prior conversation turns, and receive suggested follow-up questions.

Instructions

Ask a notebook's sources a question, and/or recall prior turns. Accepts a notebook name or ID.

Pass conversation_id to continue a specific conversation; omit it to continue the notebook's most-recent conversation (or start a new one).

source_ids (optional) scopes the question to specific sources by id/prefix/title; omit it to query every source. It accepts a real list, a JSON-array string, or a comma-separated string (the comma form cannot carry a source title that itself contains a comma — use a JSON array or a real list for those).

history (optional, default 0): the max number of prior Q&A pairs (each a {question, answer}) to also return (oldest-first), from the conversation as it stood before this question. There is no unbounded "all" value — pass a generously large number (e.g. 100) for the whole conversation. Omit question (leave it empty) with history > 0 to recall prior pairs without asking anything new; a recall-only call also echoes the conversation_id it read. Pass neither and the call is rejected.

Returns the answer plus citation references (when a question is asked). The internal raw_response debugging blob is never included. references controls citation detail: lite (default) returns source_id / citation_number / cited_text; full adds chunk-level char offsets and scores.

suggest_followups (optional, default False): when True the result also carries a suggested_prompts list of AI-suggested follow-up questions (each a {title, prompt}), scoped to the same source_ids and steered by question when one is given. It works on its own too — pass it with no question (and history 0) to get suggested questions without asking anything. When omitted/False the result never contains a suggested_prompts key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
historyNo
notebookYes
questionNo
referencesNolite
source_idsNo
conversation_idNo
suggest_followupsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description carries the full burden and excels: it discloses edge-case behaviors (comma-form source_ids limitation, 'no unbounded all' history), output details (raw_response never included, suggested_prompts key omitted when false), and side effects of a recall-only call echoing conversation_id. This is exemplary transparency.

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 long but every sentence earns its place, using bold parameter names and clear sub-clauses. It front-loads the core purpose before diving into parameter details, and avoids repetition or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (7 parameters, multiple modes, and an output schema), the description covers all necessary usage details: parameter interactions, defaults, output guarantees, and rejection conditions. The existence of an output schema means return-value details are not required, so the added output behavior notes are a bonus.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description adds rich semantics for every parameter: notebook, conversation_id continuation, source_ids accepted formats, history default and meaning, references enum, and suggest_followups behavior. It even explains a subtle comma-embedded-title limitation, fully compensating for the schema's lack of descriptions.

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 opens with a specific action ('Ask a notebook's sources a question, and/or recall prior turns') and names the resource ('a notebook's sources'). It also covers the tool's dual modes, clearly distinguishing it from sibling tools like suggest_prompts (which only suggests follow-ups) and source_read (which reads sources).

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 clear context on when to use each parameter: continuing vs starting a conversation, scoping with source_ids, recall-only usage with history, and standalone suggestion mode. It also states a rejection condition ('Pass neither and the call is rejected'). However, it does not explicitly compare against sibling tools such as suggest_prompts, leaving some alternative-selection ambiguity.

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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