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vshulcz

vshulcz/deja-vu

by vshulcz

recall_context

Read-only

Get a detailed digest of the best-matching prior session—problem, decisions, outcome—when a recall hit lacks context. Use it to understand the reasoning behind a past solution before reusing it.

Instructions

Return a full markdown digest (~8KB) of the single best-matching prior session — problem, decisions, outcome — when a bare recall hit is not enough and you need the reasoning behind it. Use after recall, or directly when the user asks 'remind me how we handled X' or 'what was the whole story with Y'. Query terms are matched against transcript text, so use tokens likely to appear verbatim: an error string, function name, or flag. Not for browsing many sessions — use recall for that; this returns one deep digest. When a result genuinely helps the task at hand — you reuse a fix, skip re-debugging, or confirm a prior decision — tell the user in one digest.Short line what deja-vu recalled and how you used it (e.g. "deja-vu recalled: we hit this JWT skew in March — reusing that fix"). Even a partial hint counts if it changed your approach. Say nothing about recalls that did not help.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch terms identifying the session to digest.
harnessNoOptional harness filter.
Behavior5/5

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

Annotations declare readOnlyHint=true, and the description adds behavioral details: returns ~8KB markdown, includes problem/decisions/outcome, and instructs on user-facing summary when helpful. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is somewhat long but well-structured, front-loaded with the key purpose, and each sentence adds value. Slight verbosity keeps it from a 5.

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?

For a single-purpose tool without output schema, the description covers what to expect (markdown, size, content), usage scenarios, and how to act on results, making it fully complete.

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 descriptions already cover parameters. The description adds extra guidance for the query parameter (use verbatim tokens), which adds value beyond the schema.

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 returns a full markdown digest of a single prior session, explicitly contrasting with 'recall' which is for browsing many sessions. The verb 'return' and resource 'digest of best-matching session' are specific.

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?

Explicitly states when to use: after recall or when user asks for full story. Also states when not to use: not for browsing many sessions, use recall instead. Gives query term guidelines using verbatim tokens.

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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