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Beever-AI

Beever Atlas

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get_tensions

Identify open disagreements and conflicting positions in a channel by listing unresolved tensions, surfacing blockers and competing stances.

Instructions

List unresolved TENSIONS in one channel — points of open disagreement or conflicting positions surfaced across its wiki. Call it to find what is still contested or undecided, as opposed to settled decisions (find_decisions).

When to use: surfacing open conflicts, blockers, or competing stances. When NOT to use: settled decisions (find_decisions) or general fact lookup (find_facts).

Prerequisites: a channel_id from list_channels.

Note: tension detection is currently empty for most channels — the wiring is in place but few channels have tension data yet, so an empty result is normal and does not indicate an error. The same call returns real data automatically once tensions exist, with no signature change.

Returns (instant, read-only): a LIST (not a dict) of {tension_id, title, status, since (YYYY-MM-DD), positions: [{author, stance, fact_id}], page_slug}. No side effects.

Error handling: on missing auth, access denial, or internal error this tool returns an EMPTY LIST [] (it never raises) — indistinguishable from "no tensions"; confirm access with list_channels if unexpected.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
channel_idYesChannel id. Get it from list_channels (e.g. 'ch-eng'). Required.
statusNoOptional status filter. One of: 'open', 'blocked', 'deferred' (e.g. 'open'). Pass null (the default) to return tensions of ALL statuses; omit for the same effect.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Since no annotations are provided, the description fully discloses behavioral traits: read-only, instant, returns a list (not dict), no side effects. It also explains error handling (returns empty list on errors) and contextualizes empty results as normal for most channels.

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 for purpose, usage, prerequisites, notes, return format, and error handling. Every sentence adds value without 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 presence of an output schema and full schema coverage, the description still adds value by detailing return structure, error behavior, and normal empty results. It is complete for a tool of this complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description does not add new meaning beyond what the schema already provides for the parameters. The prerequisite mention is usage guidance, not parameter semantics.

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 lists unresolved tensions in a channel, with specific verb and resource. It distinguishes from sibling tools like find_decisions and find_facts, providing clear differentiation.

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 provides when to use (surfacing open conflicts) and when not to use (settled decisions, fact lookup). Also notes prerequisite channel_id from list_channels, offering comprehensive 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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