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bettermemory

by 0Mattias
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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memory_list

List active memories as one-line summaries to triage stored facts. Filter by scope/client/model or include bodies; calendar-only staleness flags signal where to inspect.

Instructions

List active memories. By default returns one-line summaries (IDs, scopes, summary, no body) — cheap triage. Pass with_bodies=True to inline full bodies in one call; useful for small stores where N round trips of list -> show -> show would be wasteful. Don't reach for with_bodies casually — it pulls every memory in scope into your context, which is the failure mode this project exists to avoid. Filter by scopes if you only care about a subset, or by client / model — each row's DECLARED actor, exact. A row's staleness_verdict is CALENDAR-ONLY — no path, claim or commit drift — so a row can read fresh while memory_show on it returns spot_check_required. Read a non-fresh row as a signal to look, never a fresh one as a clearance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNo
clientNo
scopesNo
with_bodiesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv7.15.0
    • addedInput schema / properties / client
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null
+}
    • addedInput schema / properties / model
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null
+}
  2. First observedv3.34.0

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 behavioral burden, and it delivers: default vs. body-inlined output, exact-match filtering semantics on declared actor, and a precise caveat that `staleness_verdict` is calendar-only and does not include path/claim/commit drift. The guidance 'never a fresh one as clearance' prevents a dangerous misinterpretation of results.

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 longer than average, but every sentence adds operational value: purpose, default output shape, the with_bodies trade-off, filter semantics, and the staleness caveat. It front-loads the core behavior and keeps warnings adjacent to the features they qualify.

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 four optional parameters and an output schema, the description is complete: it covers all parameter semantics, warns about the main failure mode (context bloat), and explains output fields like `staleness_verdict`. There is no missing operational guidance an agent would need to call this tool correctly.

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%, yet the description compensates for every parameter: `with_bodies` is explained in depth, `scopes` is defined as subset filtering, and `client`/`model` are clarified as exact filters on each row's declared actor. The defaults are implied by the focus on default triage mode.

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 opens with a specific verb and resource — 'List active memories' — and immediately differentiates the default mode (one-line summaries with IDs, scopes, summary, no body) from the `with_bodies=True` variant, which is clearly distinct from siblings like `memory_show`. An agent knows exactly what this tool returns and how it differs from closer alternatives.

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?

It explicitly tells the agent when `with_bodies=True` is justified (small stores avoiding N round trips of `list -> show -> show`) and warns against casual use due to context bloat. It also gives clear filtering advice (`scopes`, `client`/`model`) and says a non-`fresh` verdict is a 'signal to look', implicitly directing follow-up to `memory_show`.

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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