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get_agent_bootstrap

Bootstrap agent sessions with a single call that packages repository architecture, recent context, user profile, error patterns, and timeline into one compact payload.

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) mood — {state, evidence, suggested_adaptation}; neutral/empty on sparse data error_patterns — top 3 recurring errors with prevention hints index_health — {symbols, files, status} recent_timeline — last 5 entries (past 7 days) merged across sessions, episodes, decisions, and errors — {ts, kind, summary, ref} each; see data/memory/timeline.py::merge() for the full query surface (since/include_archived/limit)

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

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.1

TDQS

A4.7/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden and largely meets it: it discloses the payload size (~300 tokens), describes edge-case behavior such as 'neutral/empty on sparse data' and 'empty if tracking off', and documents the merged timeline query surface. It stops short of explicitly stating side-effect/read-only guarantees, but the 'get' semantics and return-oriented structure strongly imply a non-mutating operation.

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?

Well-structured and front-loaded: purpose and usage guidance come first, followed by a compact bulleted list of return fields, ending with the parameter definition. Every block earns its place and there is no filler or duplication of schema information.

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 there is no output schema, the description thoroughly documents the rich return payload with examples and types. It also covers usage timing, alternatives, sparse-data behavior, and points to timeline.py::merge() for extended query surface details. This is comprehensive for a bootstrap tool of this complexity.

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 description coverage is 0%, but the only parameter is simple and optional. The description adds meaningful semantics beyond the schema's 'Repo Path' title: 'optional absolute path to the target repository'. It could further explain the default behavior when omitted, but for an optional single path this is adequate.

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?

States a specific verb plus resource ('get_agent_bootstrap') and explains it as a single-call session bootstrap replacing a 4-call sequence. It explicitly differentiates from sibling tools like get_session_brief, get_last_context, get_user_profile, and get_error_patterns by naming them and describing the consolidation.

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?

Provides explicit usage instructions: call ONCE at session start instead of the 4 individual calls, and use individual tools only when full detail is needed. This is unambiguous when-to-use guidance with clear alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.