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Tribal: Discover Knowledge

tribal_discover

Search a knowledge base using natural language queries. Returns semantically similar knowledge items with optional filters for project, kind, tags, and time.

Instructions

Search Tribal's knowledge base using natural language. Returns knowledge items ranked by semantic similarity to your query, with optional structured filters to narrow results.

Use this as your first step when you need context: before starting work on a feature, debugging an issue, or making a design decision. Ask questions the way you'd ask a colleague: "What do I know about connection pooling in this project?" or "Have I seen this async deadlock pattern before?"

Semantic search is the primary mechanism. Filters (project, kind, tags, time) narrow the candidate set but are not required. If you need to understand an item's evidence, contradictions, or derivation chain, follow up with tribal_explore using the item's ID.

Superseded items (replaced by newer understanding) are excluded by default. Set include_superseded to true for the historical picture.

Results include standing (evidential profile) when requested, which summarises each item's support count, contradiction count, observation frequency, and diversity of supporting evidence.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cursorNoOpaque pagination cursor from a previous response's next_cursor.
include_referencesNoReturn references (file paths, URLs, concepts) attached to each item.
include_standingNoCompute and return standing (evidential profile) for each result. Adds minor latency. Recommended when assessing reliability.
include_supersededNoInclude items that have been superseded by newer understanding. Default false.
kindsNoFilter to specific knowledge kinds. Use sparingly; semantic search naturally ranks relevant kinds higher. Most useful for explicit structural queries like 'show me all decision records for this project'.
limitNoMaximum number of results to return.
project_idNoFilter to a specific project. Three-way semantics: omit to use session context project (if set); pass a project ID to filter to that project; pass null to search globally, ignoring session context.
queryYesNatural language query. Describe what you're looking for conversationally. The system embeds this and finds semantically similar knowledge items.
tagsNoFilter by tags (AND semantics: items must have ALL specified tags). Tags are lowercase. For OR semantics, make separate queries.
time_rangeNoFilter by creation time. Either or both bounds may be specified.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
applied_project_idYesThe project used for filtering. Set when search was project-scoped (explicit ID or from session context). Null when search was global (no project filter applied, or project_id was explicitly null).
embedding_modelYesWhich embedding model was used for this query.
embedding_profile_idYesThe active embedding profile that produced these results. Cursors and feedback are bound to it; a reindex changes it.
exactYesTrue if all matching results are included. False if truncated by limit.
itemsYes
next_cursorYesPagination cursor. Null if no more results.
trace_idYesTrace ID for this retrieval. Pass to tribal_feedback to rate this session.
Behavior5/5

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

No annotations provided, so description carries full burden. It explains default exclusion of superseded items, behavior of include_standing and its latency, project_id three-way semantics, tag AND logic, and pagination via cursor. No contradictions or hidden behaviors.

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?

Description is well-structured and front-loaded with core purpose. Each sentence adds value, though slightly longer than minimal. No wasted words.

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?

For a tool with 10 parameters, nested objects, and an output schema, the description is comprehensive: covers usage, filters, pagination, standing, superseded items. No gaps remain.

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 description coverage is 100%, but description adds significant value: explains natural language embedding, semantic similarity ranking, project_id semantics, tag AND vs OR, and when to use kinds filter sparingly. Go 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 it's for searching Tribal's knowledge base using natural language, with semantic similarity ranking and optional filters. It distinguishes itself from sibling tools like tribal_explore (for exploring item details).

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 says 'Use this as your first step when you need context... before starting work on a feature, debugging, design decision.' Provides examples of queries and when to use filters vs not. Advises follow-up with tribal_explore for deeper understanding.

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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