Skip to main content
Glama
phillipclapham

anneal-memory

by phillipclapham
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

recall

Query stored episodes by time, type, source, or keyword to retrieve prior context, find citations, or review recent work.

Instructions

Query episodes from memory with filters. Call this to find prior context before making decisions, to locate specific episodes for citation during graduation, or to review recent work. Returns matching episodes ordered by timestamp (newest first). Supports time range, type, source, and keyword filters. The keyword is matched as an exact phrase first; if no episode contains the whole phrase and it has two or more distinctive words, the call falls back to ranking episodes by how many of those words they contain (the reply says so and names the words each episode matched), so a multi-word query does not need to appear verbatim. A phrase of three or more distinctive words with only one or two exact hits is followed by a few word matches, listed under 'Also matching by words'. A durable fact whose cue words appear in the keyword (or two distinctive words of its text) is listed first, under 'Durable facts matching your words'. limit=0 returns nothing at all, facts included.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum episodes to return. Default 100. When a multi-word keyword has no exact match and is ranked word by word, the default is 10 instead; pass a limit to see more.
sinceNoISO 8601 timestamp — return episodes after this time.
untilNoISO 8601 timestamp — return episodes before this time.
offsetNoSkip first N matching episodes. Default 0.
sourceNoFilter by source/agent attribution.
keywordNoSearch episode content for this keyword or phrase. An exact phrase match is tried first; a multi-word phrase with no exact match is then matched word by word and ranked.
episode_typeNoFilter by episode type.
include_supersededNoAlso return episodes a newer episode replaced, marked with what replaced them. Default false: they are left out.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.9.41
    • changedInput schema / properties / keyword / description
      Previous value: -"Search episode content for this keyword."New value: +"Search episode content for this keyword or phrase. An exact phrase match is tried first; a multi-word phrase with no exact match is then matched word by word and ranked."
    • changedInput schema / properties / limit / description
      Previous value: -"Maximum episodes to return. Default 100."New value: +"Maximum episodes to return. Default 100. When a multi-word keyword has no exact match and is ranked word by word, the default is 10 instead; pass a limit to see more."
  2. Changed1 schema field changedv0.9.23
    • addedInput schema / properties / include_superseded
      Added value: +{
+  "default": false,
+  "description": "Also return episodes a newer episode replaced, marked with what replaced them. Default false: they are left out.",
+  "type": "boolean"
+}
  3. First observedv0.1.4

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and discharges it well: it discloses ordering (newest first), the exact-then-word-fallback ranking behavior, the special handling and labeling of durable facts, and the edge case that limit=0 returns nothing at all. It omits permission/auth requirements and does not explicitly state the operation is read-only, but the 'Query' framing and rich behavioral detail are well beyond structured fields.

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?

Purpose is front-loaded in the first sentence, followed by usage triggers and then behavioral mechanics, which is the right ordering. It is on the long side and partly restates the keyword-matching mechanics that already live in the schema, but nearly every sentence carries actionable detail, so the length is largely earned.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an 8-parameter tool with no annotations and no output schema, the description supplies the missing context: ordering, filtering support, fallback behavior, durable-fact promotion, and the limit=0 edge case. What remains thin is the shape of returned episodes and the relationship to crystal_recall, but overall the agent has enough to call it correctly.

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 already 100%, so baseline would be 3, but the description adds real meaning: it explains the keyword exact-phrase-first/word-rank-fallback matching and the durable-fact cue-word promotion, which is not captured in the schema text. The limit=0 edge case is also called out in both places, so some of that gain is already in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Query episodes from memory with filters') that an agent can act on immediately. However, it never distinguishes itself from the sibling crystal_recall, which appears to be a competing retrieval tool, so the agent has no basis for choosing between them from the description alone.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives concrete when-to-use triggers: find prior context before decisions, locate episodes for citation during graduation, review recent work. This is clear context but provides no exclusions or named alternatives, leaving the crystal_recall overlap unresolved.

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

Deploy Server

Other Tools