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Beever-AI

Beever Atlas

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find_experts

Identify the top experts in a channel for a given topic by ranking people based on knowledge graph signals and expertise scores.

Instructions

Rank the PEOPLE most knowledgeable about a topic in one channel.

Call this to answer "who should I ask about X in #channel?" or to route a question to the right person. This is the only tool that ranks PEOPLE; use search_channel_facts / find_facts to find FACTS, and find_experts only when you specifically need a human.

Prerequisite: a channel_id from list_channels. Do NOT call with a channel display name.

Returns (instant, read-only, no side effects): {"experts": [...]} — a list ranked by expertise_score descending. Each entry has handle (e.g. '@dana'), expertise_score (relative float, higher = more authoritative; not a fixed 0–1 scale, only meaningful for ranking within this result), fact_count (number of contributing facts), and top_topics (list of related topics that person engages with). An empty list means no graph signal for that topic — not an error.

Error modes: {"error": "authentication_missing"} if the caller is unauthenticated; {"error": "channel_access_denied", "channel_id": ...} if the principal cannot read the channel; {"error": "invalid_parameter", ...} for a malformed channel_id. Other backend failures degrade gracefully to {"experts": []}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
channel_idYesRequired. The channel id to search within, obtained from list_channels (e.g. 'ch-eng'). Not a human channel name.
topicYesRequired. Topic or keyword to rank experts on, e.g. 'kubernetes', 'billing', 'auth'. Matched against knowledge-graph edges, so use a concept the channel actually discusses.
limitNoMaximum number of experts to return. Range 1–20, default 5. Values outside the range are silently clamped (e.g. 50 -> 20, 0 -> 1).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations provided, so the description carries full burden. It states the tool is 'instant, read-only, no side effects'. It explains the return structure, including that expertise_score is a relative float only meaningful for ranking, and describes error modes like authentication_missing and graceful degradation to empty list.

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 a one-line summary, usage context, prerequisites, return format, and error modes. It is slightly verbose but every sentence adds value, so it earns a 4 rather than a 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?

For a tool with 3 parameters, no annotations, and an output schema described in text, the description is very complete. It covers return structure, error modes, meaning of empty list, and ranking scale. No gaps remain for effective usage.

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 context beyond the schema: for channel_id, it specifies it comes from list_channels and is not a display name; for topic, it mentions matching against knowledge-graph edges; for limit, it describes range and clamping behavior.

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: 'Rank the PEOPLE most knowledgeable about a topic in one channel.' It uses a specific verb (rank), identifies the resource (people/topic/channel), and distinguishes itself from sibling tools like search_channel_facts and find_facts by emphasizing that it ranks people, not facts.

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?

Explicit guidance is given: 'Call this to answer "who should I ask about X in #channel?"' or to route a question. It provides a prerequisite (channel_id from list_channels) and specifies not to use a display name. It also contrasts with siblings (use find_facts for facts, this for humans).

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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