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Tribal: Set Session Context

tribal_set_context

Set or override session-level context to declare your model identity or switch projects at session start.

Instructions

Set or override session-level context for Tribal. Use this at the start of a session to declare your model identity, or when switching to a different project.

Session context is used as the default for all subsequent tool calls. For example, setting a project here means tribal_ingest and tribal_discover will use it automatically without needing project_id on every call.

The server resolves what it can at connection start (project from git remote, principal from auth). Use this tool to fill in what the server cannot infer (model name, provider) or to override what it resolved (e.g., switching projects).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoThe model you are running as. e.g., 'claude-sonnet-4-5-20250514'. Set once at session start.
project_idNoOverride the active project. Use when working in a different project than the one resolved from the git remote.
providerNoThe inference provider. e.g., 'anthropic', 'openai'. Set once at session start.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
actorYes
principal_keyYes
projectNo
Behavior5/5

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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It explains that session context becomes default for subsequent calls, how server resolution works, and that this tool fills gaps or overrides. The description is transparent about the tool's impact and limitations.

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 efficiently structured: first sentence defines purpose, then usage guidance, then behavioral explanation. Every sentence adds value and no waste. It is front-loaded with the core purpose.

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 tool's low complexity, full schema coverage, and presence of an output schema, the description adequately covers all necessary information. It explains purpose, usage, parameter details, and behavioral implications, leaving 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 valuable context beyond the schema: for 'model' and 'provider' it recommends setting once at session start, and for 'project_id' it clarifies override behavior. This improves usability without being verbose.

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's action ('Set or override session-level context for Tribal') and resource ('session context'). It distinguishes itself from siblings by emphasizing that it configures defaults used by other tribal tools, which is a distinct role from data ingestion or discovery tools.

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 states when to use the tool ('at the start of a session' or 'when switching projects') and explains the default server inference and when to override. It also implies when not to use it (when server inference suffices), providing clear guidance.

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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