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liza-studio

skillmem — long-term memory for Claude Code & Codex

by liza-studio
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Python
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mem_list

Browse memories newest-first without a query, filter by kind or project, and return read-only metadata rows (no bodies) for follow-up retrieval.

Instructions

Browse memories most-recent-first without a query. Read-only. Returns up to limit (default 50, max 100) rows with slug, kind, title, project, updated_at, origin and approval state — no bodies; fetch one with mem_get. kind restricts to note / skill / feedback / project / reference / user, project to one project tag; archived records are excluded. Use mem_search when you know roughly what you are looking for; use mem_recall for task-relevant skills.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNo
limitNo
projectNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.12.0
    • changedInput schema / properties / project / type
      Previous value: -"string"New value: +[
+  "string",
+  "null"
+]
  2. First observedv0.10.5

TDQS

A5/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so: it declares read-only, the default (50) and max (100) row cap, the exact returned fields, that bodies are excluded, that archived records are filtered out, and the fallback to mem_get for one record.

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?

Front-loads the core behavior (browse, no query) and then adds return shape, parameter semantics, and sibling routing in a compact, dense passage with no filler sentences.

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?

With no output schema and no annotations, everything an agent needs is present: ordering, result shape, filtering, limits, and when to prefer siblings instead.

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?

Schema description coverage is 0%, so the description must compensate and does: it enumerates the allowed `kind` values (note/skill/feedback/project/reference/user), defines `project` as a single project tag, and gives `limit`'s default and ceiling. This adds meaning well beyond the bare 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?

States a specific verb and resource ('Browse memories most-recent-first'), plus scope: no query required. It distinguishes itself from siblings by contrasting with mem_search and mem_recall by name.

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 routing: 'Use mem_search when you know roughly what you are looking for; use mem_recall for task-relevant skills.' This gives the agent a clear condition for choosing each alternative.

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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