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Beever Atlas

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trace_decision_history

Reconstruct how a decision evolved over time in a channel by building an ordered timeline of superseded choices, revealing the 'why we changed' trail.

Instructions

Reconstruct how a decision EVOLVED over time in a channel.

Call this to answer "how did the team arrive at the current approach for X?" or "what earlier choices were overridden?" It walks SUPERSEDES edges in the knowledge graph to build an ordered timeline of superseded → current decisions. Distinct from find_decisions (which lists current decision facts with no history) and search_channel_facts (current state only, no chronology); use this tool specifically when you need the chronological "why we changed" trail.

Prerequisite: a channel_id from list_channels. Best results on mature channels where decisions have been revised; new channels often have no supersession chain yet (empty result, not an error).

Returns (instant, read-only, no side effects): {"decisions": [...]} ordered oldest → newest. Each item has entity (the decision that was made), superseded_by (the decision that replaced it, or empty for the current one), relationship (edge label, typically 'SUPERSEDES'), confidence (0–1 extraction confidence), context (snippet explaining the change), and position (0-based index in the timeline). An empty list means no recorded supersession chain.

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 {"decisions": []}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
channel_idYesRequired. The channel id to trace within, obtained from list_channels (e.g. 'ch-eng'). Not a human channel name.
topicYesRequired. The decision area to trace, e.g. 'database choice', 'API versioning', 'auth provider'. Matched against decision entities in the knowledge graph; use the subject of the decision, not a yes/no question.

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: it declares the operation as 'instant, read-only, no side effects,' describes the exact return format including fields like `entity`, `superseded_by`, `relationship`, `confidence`, `context`, `position`, and lists all error modes (authentication, access denied, invalid parameter). It also explains that empty list means no supersession chain.

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 with clear sections: purpose, when to call, distinction from siblings, prerequisites, return format, and errors. Every sentence adds essential information, and it is front-loaded with the key use case.

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 (2 params, no enums, with output schema), the description covers all necessary context: purpose, usage, behavior, return format, and error modes. The output schema exists, so the description's detailed field explanation is supplementary and complete.

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% with clear descriptions, but the tool description adds significant value: for `channel_id`, it specifies the source (list_channels) and warns it's not a human name; for `topic`, it provides examples and cautions against yes/no questions. This extra context enhances understanding beyond the 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 clearly states the tool's purpose: 'Reconstruct how a decision EVOLVED over time in a channel.' It uses a specific verb ('reconstruct') and resource ('decision evolution over time'), and explicitly distinguishes from sibling tools `find_decisions` and `search_channel_facts` by contrasting their scopes.

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 when-to-use guidance: 'answer how did the team arrive at the current approach for X?' and when-not-to-use: 'for current decision facts use find_decisions, for current state use search_channel_facts.' It also includes a prerequisite (channel_id from list_channels) and notes that empty results are normal for new 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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