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Beever Atlas

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find_facts

Find facts in a channel by exact substring match. Returns facts sorted by importance then recency. Use for deterministic fact retrieval.

Instructions

Find facts in one channel whose text literally CONTAINS a substring (deterministic, case-insensitive). Call it when you know an exact keyword and want every matching raw fact row, not a ranked or synthesized result. Returns a bare LIST and collapses missing-auth, access-denied, empty-query, and internal errors into an EMPTY LIST — so [] means no match OR no access; it never returns a structured error object.

Disambiguation: find_facts is a deterministic substring filter (predictable, no relevance ranking). For meaning-based / fuzzy retrieval use search_channel_facts (BM25+vector). For a synthesized answer use ask_channel. For decisions with rationale use find_decisions.

When to use: exact-keyword scans ("every fact mentioning 'rollback'"), optionally narrowed by fact_type. When NOT to use: you want semantically related results for a phrase (use search_channel_facts).

Prerequisites: a channel_id from list_channels.

Returns (instant, read-only): a LIST (not a dict) of up to limit facts, sorted by importance DESC then recency (message_ts) DESC. The importance values that drive the sort rank, highest first, are 'critical' > 'high' > 'medium' > 'low' (any other/empty value sorts lowest). Each item is {fact_id, memory_text, fact_type, importance, author_name, message_ts, page_slug (empty if not yet on a page)}. No side effects.

Error handling: on missing auth, access denial, empty query, or internal error this tool returns an EMPTY LIST [] (it never raises) — an empty list means no match OR no access; confirm with list_channels if unexpected.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
channel_idYesChannel id. Get it from list_channels (e.g. 'ch-eng'). Required.
queryYesCase-insensitive substring matched literally inside each fact's text (e.g. 'rollback'). NOT semantic — exact substring only. Required.
fact_typeNoOptional type filter. One of: 'decision', 'observation', 'opinion', 'question', 'action_item'. Omit for all types. Default null.
limitNoMax facts to return, 1-100 (out-of-range values are clamped). Default 20.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations, the description fully covers behavior: it returns a bare LIST, collapses errors into an empty list, is read-only with no side effects, sorts by importance then recency, and details the return fields. It also explains the implication of empty results.

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 logical sections, front-loading the core function. It is thorough but slightly verbose; however, every sentence adds value, earning a 4.

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 output schema is implicit (description includes return fields), and the input schema is fully covered, the description provides complete behavioral context: sorting, error handling, return format, and prerequisites. It leaves no gaps.

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?

Schema coverage is 100%, so baseline is 3. The description adds value beyond the schema by clarifying query as a literal substring, listing fact_type options, explaining limit clamping, and providing examples. This justifies a 4.

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 finds facts by literal substring matching in a channel, and distinguishes it from semantic search (search_channel_facts), synthesized answers (ask_channel), and decision lookup (find_decisions). It is specific about the deterministic, case-insensitive behavior.

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 explicitly tells when to use (exact-keyword scans) and when not to (semantic queries, use search_channel_facts), and lists prerequisites (channel_id from list_channels). It also explains error handling (empty list on no match or access issues).

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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