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Tribal: Ingest Knowledge

tribal_ingest

Submit raw text to extract structured knowledge: facts, heuristics, procedures, decision records. The system deduplicates and links new insights to existing knowledge.

Instructions

Submit raw text for knowledge extraction into Tribal. The system extracts structured knowledge items (facts, heuristics, procedures, decision records), detects duplicates, identifies relationships with existing knowledge, and stores the results.

This is an asynchronous operation. Returns a job_id immediately. Use tribal_job_status to poll for completion.

Use this tool when you've learned something worth preserving: a debugging insight, an architectural decision, a reusable pattern, a gotcha about a library, or any experience that would help you or another agent working on this codebase in the future.

Do NOT use this for storing code snippets, file contents, or documentation. Tribal stores knowledge about work, not the artefacts themselves.

Project, model, and principal are sourced from session context (see tribal_set_context). You only need to provide the content itself.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesThe raw text to extract knowledge from. Write naturally: describe what you learned, what went wrong, what the fix was, why a decision was made. The system handles structuring. Richer input produces better results: include context, reasoning, and specifics rather than terse summaries.
project_idNoOverride the session's active project for this ingest. Optional; defaults to the project set in session context.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYesUnique identifier for the ingest job. Use with tribal_job_status to track progress.
Behavior4/5

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

Discloses key behavioral traits: asynchronous operation with immediate job_id return, knowledge extraction and storage. Mentions that project/model/principal come from context. However, no annotations exist, so the description carries full burden; it could mention idempotency or side effects, but the provided info is sufficient for safe use.

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?

Concise with clear structure: main action, async note, usage guidance, exclusions, and context sourcing. Every sentence serves a purpose; no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (async, multi-step extraction) and existence of an output schema, the description covers essential operational aspects, usage, and parameter context. It does not detail error handling or output structure (covered by schema), but is complete enough for effective use.

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% (baseline 3). The description adds value: explains how to write content naturally for best results, and clarifies project_id as optional override. This goes beyond the schema's basic descriptions.

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 uses specific verbs ('Submit raw text for knowledge extraction') and identifies the resource ('into Tribal'). It explains the system's extraction process (facts, heuristics, etc.) and clearly differentiates from siblings by focusing on ingestion, not discovery, exploration, or other operations.

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?

Explicit guidance on when to use: 'when you've learned something worth preserving' with concrete examples. Also states when NOT to use: 'Do NOT use this for storing code snippets...'. Recommends polling with tribal_job_status for async completion and notes context sourcing from tribal_set_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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