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trw_session_start

Restore prior learnings and active run status to start a session with full context. Use for new sessions, resuming after compaction, or recalling focused topic.

Instructions

Load prior learnings + any active run so you start with full context.

Use when:

  • Starting a new session (first action, before reading code or editing).

  • Resuming after context compaction and you need the pin and learnings reloaded.

  • Switching onto an unfamiliar task and want a focused recall on the topic.

Recalls high-impact learnings (patterns, gotchas, architecture decisions) and checks for an active run (phase, progress, last checkpoint). Partial-failure resilient: a failure in one sub-step does not block the others.

Input:

  • query: optional focus string. When set, performs a focused recall on your topic AND a baseline high-impact recall, then merges + dedupes. Empty string or "*" uses default wildcard behavior.

  • verbose: when False (default) returns a COMPACT payload — the learnings list is capped to the top-K most relevant (with a learnings_omitted "N more" indicator) and the low-signal diagnostic sub-blocks (embed_health/assertion_health/sync_health/step_durations_ms) are folded into a one-line health_summary to cut token cost. Run/pin recovery, errors, framework_reminder, and degraded advisories are always preserved. Set verbose=True for the full diagnostic payload (legacy behavior).

Output: SessionStartResultDict with fields {learnings: list, learnings_count: int, learnings_omitted?: int, run: RunStatusDict, auto_recalled?: list, health_summary?: str (compact), embed_health?: dict (verbose), assertion_health?: dict (verbose), framework_reminder: str, errors: list, success: bool, compact: bool, payload_token_estimate: int}.

Example: trw_session_start(query="sqlite extension macos") → {"learnings": [...], "learnings_count": 8, "compact": true, "health_summary": "embed=ok; start=42ms (verbose=True for ...)", "run": {"active_run": "/path/...", "phase": "IMPLEMENT"}, ...}

See Also: trw_init, trw_recall

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo
verboseNo
Behavior5/5

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

No annotations exist, but the description fully discloses behavioral traits: compact vs verbose payload, partial-failure resilience, and output structure, compensating for the lack of annotations.

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 well-structured with clear sections, bullet points, and an example. Every sentence contributes value, making it informative yet concise.

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 simplicity (two optional params, no nested objects, no output schema), the description thoroughly covers inputs, outputs, edge cases, and behavior, leaving no ambiguity.

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?

Both parameters (query and verbose) are explained in detail, covering default behavior, wildcard, and compact/verbose differences, adding substantial meaning beyond the schema which has 0% coverage.

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 the tool loads prior learnings and active runs for full context, and distinguishes itself from siblings like trw_init and trw_recall via a 'See Also' section.

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 use cases are listed: starting a new session, resuming after compaction, switching tasks, with guidance on when to use and partial-failure resilience, plus references to alternative tools.

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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