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get_agent_bootstrap

Bootstrap an AI agent session with a single call to retrieve project context, architecture, key symbols, and error patterns.

Instructions

Single-call session bootstrap for AI agents. Replaces the 4-call sequence (get_session_brief → get_last_context → get_user_profile → get_error_patterns) with one ~300-token payload.

Returns: repo — project name architecture — 600-char architecture summary hot_symbols — ["fn:file:line", ...] top 8 symbols by behaviour weight last_focus — {files, query, agent} from last agent's context_pack framing — {depth, vocabulary} from user profile (empty if tracking off) error_patterns — top 3 recurring errors with prevention hints index_health — {symbols, files, status}

Claude: call this ONCE at session start instead of the 4 individual calls. Use individual tools only when you need the full detail each provides.

repo_path: optional absolute path to the target repository.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repo_pathNo
Behavior2/5

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

No annotations are provided, so the description must carry full burden for behavioral traits. It mentions the payload size (~300 tokens) but does not state whether the call is read-only, requires authentication, or any side effects. For a bootstrap tool, it lacks disclosure of performance or error behavior.

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 concise, with a clear opening sentence, bullet-pointed return values, and an explicit usage instruction. Every sentence adds value, and the structure makes it easy for an agent to parse.

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 no annotations or output schema, the description adequately covers the tool's purpose, return values, and context of use. It explains what each returned field means and when to use it, making it complete for an agent to decide and invoke correctly.

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?

The only parameter, repo_path, is described as 'optional absolute path to the target repository'. With 0% schema description coverage, the description adds complete meaning. The description of the parameter is clear and sufficient for an agent to understand its purpose.

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 that this tool is a single-call session bootstrap replacing a 4-call sequence, and explicitly lists the resources it returns (repo, architecture, hot_symbols, etc.). It distinguishes from sibling tools like get_session_brief, get_last_context, etc.

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 says 'call this ONCE at session start instead of the 4 individual calls' and advises to use individual tools only when full detail is needed. This provides clear when-to-use and when-not-to-use 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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