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Beever Atlas

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get_extraction_status

Check if a channel's messages are fully extracted before trusting retrieval results. Get counts per extraction state: pending, extracting, done, failed.

Instructions

Report how far fact EXTRACTION has progressed for a channel's messages, as a count per status. Call it to judge whether a channel's knowledge is fully ingested before you trust retrieval results, or to track progress after triggering a sync.

Distinguish from get_job_status: this counts MESSAGES by extraction state (corpus readiness); get_job_status reports the lifecycle of one async JOB by job_id. Use this for "is this channel done extracting?"; use get_job_status for "did my trigger_sync/refresh_wiki job finish?".

When to use: gauge corpus completeness, or detect a backlog (high pending) or failures (non-zero failed) before relying on ask_channel/search_channel_facts.

Prerequisites: a channel_id from list_channels.

Returns (instant, read-only): {channel_id, counts: {pending, extracting, done, failed}, total} where each count is the number of messages in that state and total is their sum. No side effects.

Error modes (returned as dicts): 'authentication_missing' (no principal); 'channel_access_denied' (token lacks access to channel_id); 'extraction_status_failed' (internal error reading the queue).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
channel_idYesChannel id whose extraction progress to report. Get it from list_channels (e.g. 'ch-eng'). Required.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Although no annotations are provided, the description fully covers behavioral traits: it states 'instant, read-only', 'no side effects', and lists all error modes. This compensates for the lack of annotations.

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 well-structured into logical paragraphs with clear sections. Every sentence adds value, and it is concise while still being comprehensive. There is no redundancy.

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 simplicity, the description covers purpose, usage guidelines, parameter, return format, side effects, and error modes. The presence of an output schema (not shown but referenced) further supports completeness.

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?

The schema has 100% coverage for the single parameter channel_id. The description adds meaningful context beyond the schema by providing an example value ('ch-eng') and specifying the source (list_channels), which enhances usability.

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 reports fact extraction progress as a count per status for a channel's messages. It uses specific verbs and resource, and distinguishes from get_job_status by explaining the difference between counting messages by extraction state versus reporting job lifecycle.

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?

It provides explicit when-to-use scenarios: judging corpus completeness before relying on retrieval, tracking progress after sync, and detecting backlogs. It also distinguishes from get_job_status and lists prerequisites (channel_id from list_channels).

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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