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Beever-AI

Beever Atlas

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search_relationships

Find how named entities like people, systems, or concepts connect in a channel's knowledge graph. Explore relationships between entities to understand their connections.

Instructions

Find how named ENTITIES connect in a channel's knowledge graph.

Call this to answer "how is X related to Y?" or "what touches the billing service?" by returning the subgraph of nodes and edges around the given entities. This explores the KNOWLEDGE graph of entities/relationships — distinct from get_wiki_graph, which returns the wiki PAGE-LINK graph (which wiki pages reference which). Use find_experts to rank people and trace_decision_history to follow decision supersession.

Prerequisite: a channel_id from list_channels and at least one entity name. Names should match how the channel refers to the entity; unknown names simply yield an empty subgraph.

Returns (instant for small hop counts, read-only, no side effects): {"nodes": [...], "edges": [...], "text": str, "entities_searched": [...]}. Each node has name and type (e.g. 'person', 'system', 'concept'); each edge has source, target, type (relationship label), confidence (0–1, extraction confidence), and context (snippet explaining the edge). text is a human-readable summary. Empty nodes/ edges means no connections were found — not an error.

Error modes: {"error": "authentication_missing"} if unauthenticated; {"error": "channel_access_denied", "channel_id": ...} if the channel is not readable; {"error": "invalid_parameter", ...} for a malformed channel_id. Other backend failures degrade to {"nodes": [], "edges": [], "channel_id": ...}.

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.
entitiesYesRequired. One or more entity NAMES to connect, e.g. ['Postgres', 'billing-service'] or ['Dana']. These are knowledge-graph node names (people, systems, concepts), not channel ids. Provide at least one; provide two+ to find paths between them.
hopsNoHow many graph edges to traverse out from the entities. Range 1–4, default 2. Larger values return wider but noisier subgraphs and are slower. Out-of-range values are silently clamped (e.g. 9 -> 4).

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 behavior: read-only, no side effects, instant for small hops, return structure, error modes, clamping of hops. It leaves no ambiguity about what happens in various scenarios.

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 sections and bullets, but slightly verbose. However, every sentence adds value and the information is front-loaded with the purpose.

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 (graph search) and no output schema provided, the description fully covers prerequisites, usage, return format, error handling, and edge cases (empty results, clamping). It is sufficiently complete for correct agent 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%, and the description adds significant context beyond the schema: how to obtain channel_id, that entities are names (not IDs), hops clamping, and typical values. This helps the agent use parameters correctly.

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: 'Find how named ENTITIES connect in a channel's knowledge graph.' It uses specific verbs and resource, and explicitly distinguishes from sibling tools like 'get_wiki_graph' by contrasting knowledge graph vs. wiki page-link graph.

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?

Provides explicit usage contexts ('to answer how is X related to Y?'), prerequisites (requires channel_id from list_channels and entity names), and behavior for missing entities (empty subgraph). Also mentions alternative tools for different tasks.

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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