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Beever-AI

Beever Atlas

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ask_channel

Retrieve cited, reasoned answers to natural-language questions about a single channel's content. Ideal for understanding decisions, discussions, and summaries from team chat.

Instructions

Answer a natural-language QUESTION about one channel with synthesized, cited reasoning. The flagship retrieval tool — call it when the user asks anything that needs an ANSWER rather than raw rows.

When to use: any question about a channel's content where you want a composed answer with citations and reasoning across multiple messages ("what did we decide about X", "why did the project slip").

When NOT to use: exact keyword/semantic lookup of individual facts (use search_channel_facts); cross-channel recall when you don't know which channel holds the answer (use search_memory); a deterministic substring scan (use find_facts). Those are faster and return raw rows, not prose.

Prerequisites: a channel_id from list_channels.

Returns (instant for 'quick', long-running up to a 90s hard cap for 'deep'/'summarize'): a dict {answer: str, citations: [{fact_id, text, permalink, author, ts}], follow_ups: [str], metadata: {mode, ...}}. Read-only — no side effects, triggers no jobs.

Error modes (all returned as {error: ...} dicts, never exceptions): 'authentication_missing' (no principal); 'invalid_parameter' (empty/over- 4000-char question, or mode not in quick/deep/summarize); 'channel_access_denied' (token lacks access to channel_id); 'answer_timeout' (exceeded the 90s cap — retry with mode='quick'); 'adk_error' (internal pipeline failure).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
channel_idYesChannel id to query. Get it from list_channels (e.g. 'ch-eng'). Required.
questionYesNatural-language question, 1-4000 chars (e.g. 'What database did we pick and why?'). Longer questions return error 'invalid_parameter'. Required.
modeNoRetrieval depth. One of: 'quick' (BM25 only, no reasoning, ~3s), 'deep' (full pipeline with graph + multi-hop reasoning, ~20-60s), 'summarize' (structured summary, ~10-30s). Default 'deep'.deep
session_idNoOptional session id for multi-turn continuity (e.g. 'sess-abc123' from start_new_session). Omit for a per-principal default session. Default null.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Despite no annotations, the description fully discloses behavioral traits: read-only, no side effects, return format (dict with answer, citations, follow_ups, metadata), error modes as dicts (not exceptions), and time expectations (instant for quick, up to 90s for deep/summarize). This covers all key behaviors.

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 clear sections (When to use, When NOT to use, Prerequisites, Returns, Error modes). It is relatively long but every sentence provides value; however, it could be slightly more concise without losing clarity, justifying a 4 rather than 5.

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 (four parameters, multiple modes, error handling, output format), the description covers prerequisites, return structure, error modes, and timing. It references the output schema implicitly and provides all necessary context for an agent to invoke the tool correctly.

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 coverage is 100%, but the description adds significant value beyond the schema: for channel_id it references list_channels; for question it gives length constraint and example; for mode it explains the retrieval depth (BM25, graph+multi-hop, etc.); for session_id it explains multi-turn continuity and provides an example. This enriches understanding.

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's purpose: answering natural-language questions about a channel with synthesized, cited reasoning. It explicitly distinguishes itself from sibling tools like search_channel_facts, search_memory, and find_facts by specifying when to use each, ensuring the agent selects correctly.

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

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use the tool (any question needing an answer with reasoning) and when not to (exact keyword lookup, cross-channel recall, substring scan). It also lists prerequisites (channel_id from list_channels), making usage conditions 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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