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Beever-AI

Beever Atlas

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read_provenance

Trace any fact back to its original chat message to verify sources, build citations, or audit provenance. Returns platform, author, timestamp, and raw message.

Instructions

Trace ONE fact back to the original chat message it was extracted from. Call it to verify or cite a fact — given a fact_id from another tool, it returns where the fact came from (platform, message, author, timestamp, and the raw message text when reachable).

When to use: confirming a fact's source, building a citation, or auditing provenance. Prerequisites: a fact_id surfaced by find_facts, find_decisions, search_channel_facts, search_memory, or an ask_channel citation.

Returns (instant, read-only): {fact_id, memory_text, source: {platform, message_id, url, author, ts}, raw_message}. raw_message is the original chat body, or an empty string if the source message is no longer reachable — every other field is still populated in that case. No side effects.

Error modes (returned as dicts): 'authentication_missing' (no principal); 'fact_not_found' (unknown fact_id — also returned, deliberately, when the caller lacks access to the fact's channel, so cross-tenant existence is never leaked); 'provenance_read_failed' (internal error).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fact_idYesFact id to trace, as returned in the ``fact_id`` field of another tool's result (e.g. 'fact_abc123' from find_facts, find_decisions, search_channel_facts, or ask_channel citations). Required.

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 description bears full burden. It describes return format, instant and read-only nature, no side effects, and details error modes including privacy-preserving behavior for cross-tenant access. Exceptionally transparent.

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?

Well-structured with clear sections for purpose, when-to-use, prerequisites, return format, and errors. Every sentence is informative with no 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?

Despite low complexity, description provides full coverage: purpose, usage guidelines, prerequisites, return format with details, error modes, and behavioral traits. Very complete.

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?

Single parameter fact_id is well-described in schema. Description adds value by specifying that fact_id comes from other tools' fact_id field and listing example tools, which aids correct invocation.

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?

Description clearly states 'Trace ONE fact back to the original chat message it was extracted from' with a specific verb and resource. It distinguishes from siblings like find_facts and trace_decision_history by focusing on provenance of a single fact.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use: 'confirming a fact's source, building a citation, or auditing provenance' and lists prerequisites (fact_id from specific tools). Missing explicit when-not, but context is clear.

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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