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Beever-AI

Beever Atlas

Official

whoami

Confirm your authenticated identity and retrieve accessible connection IDs. Use this tool first in any session to establish access rights.

Instructions

Confirm who you are authenticated as and which connection ids you can reach.

Call this FIRST in any session, before any other tool, to (1) verify your auth token resolved to a principal and (2) get the connection ids needed by list_channels. Returns only connection IDS here; use list_connections when you also need each connection's platform, status, and sync metadata.

When to use: once at session start. Do NOT call repeatedly — the response is stable for the whole session.

Latency: instant (single in-memory/DB lookup; never triggers a sync or job).

Returns a dict:

  • principal_id (str): your authenticated identity, e.g. "user_42".

  • connections (list[str]): connection ids you may access, e.g. ["conn_abc123", "conn_def456"]. Empty list if you own no connections.

  • server_version (str): deployed Atlas version, e.g. "0.1.0".

Error modes: returns {"error": "authentication_missing"} when the request carries no valid principal (token absent/invalid). No access-denied path — the response is always scoped to the caller.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Discloses latency ('instant'), scalability ('single in-memory/DB lookup; never triggers a sync or job'), and error modes ('authentication_missing'). Since no annotations are provided, the description fully bears the burden, and it does so comprehensively.

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-organized into clear sections (purpose, usage, latency, return values, errors). Every sentence is necessary and informative, with no fluff.

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 zero input parameters, the description covers everything an agent needs: what the tool does, when to use it, what it returns, and how it behaves. It is fully self-contained and handles all context for the 27 sibling tools.

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?

There are no parameters, so the baseline is 4. The description adds value by detailing the return schema (principal_id, connections, server_version) and error conditions, going beyond the empty input schema.

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 specifies a verb ('confirm') and a clear resource ('authentication and connection ids'). It distinguishes itself from sibling 'list_connections' by explaining that 'whoami' returns only connection IDs, while 'list_connections' provides additional metadata.

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?

Explicitly instructs to 'Call this FIRST in any session, before any other tool' and advises against repeated calls. It also states when to use the alternative 'list_connections', providing clear usage context.

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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