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Glama

XMemo

by io.github.yonro

Recall XMemo memory

recall
Read-onlyIdempotent

Recall the few most relevant saved memories before answering. This is a lightweight, unscoped lookup; use search_memory for a scoped lookup, or recall_context for a multi-item context pack.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results to return.
queryYesNatural-language question or search text.
sinceNoRFC 3339 timestamp with timezone offset (e.g., '2026-09-01T00:00:00+09:00'). Trigger: Call this whenever the user query mentions any relative or absolute time constraint (今天/今日/昨天/昨日/前天/上周/8月1日/8月10日到9月1日/最近...). Model responsibility: You (the calling LLM) know current date and user local time, so you must convert natural language time expressions into absolute timestamps. Consequence: If omitted, the server falls back to heuristic guessing which may misinterpret timezone day boundaries or relative dates.
untilNoRFC 3339 timestamp with timezone offset (e.g., '2026-09-02T00:00:00+09:00'). When used with since, defines the half-open interval [since, until) with until > since (maximum 90 days span). Trigger: Pass when the user query specifies an end date or bounded window (e.g., 昨天 end of day, 截至8月10日, 8月10日到9月1日). Model responsibility: Convert natural language end bounds into absolute timestamps with timezone offsets. Consequence: If omitted, the window is open-ended on the upper side or derived heuristically.
query_modeNoOptional query intent: 'topic' (default) or 'activity_log'. Trigger: Set 'activity_log' for event feeds within a bounded [since, until) window; set 'topic' for topic retrieval. Model responsibility: 'activity_log' requires explicit bounded 'since' and 'until' timestamps. Consequence: 'activity_log' without bounds fails closed; explicit 'topic' blocks stream bypass.
time_orderNoSort order for returned memories: 'desc' (newest first) or 'asc' (oldest first). Trigger: Pass 'desc' when the user asks for 'latest', 'recent', '从新到旧', or timeline recency, or 'asc' for chronological order. Model responsibility: Explicitly set desired temporal ordering when the user expresses recency intent. If omitted and since/until are omitted, default relevance ranking is used without temporal sorting.
hypothetical_answerNoInput value for the Recall tool.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed5 schema fields changed
    • addedInput schema / properties / hypothetical_answer
      Added value: +{
+  "default": "",
+  "description": "Input value for the Recall tool.",
+  "title": "Hypothetical Answer",
+  "type": "string"
+}
    • addedInput schema / properties / query_mode
      Added value: +{
+  "default": "",
+  "description": "Optional query intent: 'topic' (default) or 'activity_log'. Trigger: Set 'activity_log' for event feeds within a bounded [since, until) window; set 'topic' for topic retrieval. Model responsibility: 'activity_log' requires explicit bounded 'since' and 'until' timestamps. Consequence: 'activity_log' without bounds fails closed; explicit 'topic' blocks stream bypass.",
+  "title": "Query Mode",
+  "type": "string"
+}
    • addedInput schema / properties / since
      Added value: +{
+  "default": "",
+  "description": "RFC 3339 timestamp with timezone offset (e.g., '2026-09-01T00:00:00+09:00'). Trigger: Call this whenever the user query mentions any relative or absolute time constraint (今天/今日/昨天/昨日/前天/上周/8月1日/8月10日到9月1日/最近...). Model responsibility: You (the calling LLM) know current date and user local time, so you must convert natural language time expressions into absolute timestamps. Consequence: If omitted, the server falls back to heuristic guessing which may misinterpret timezone day boundaries or relative dates.",
+  "title": "Since",
+  "type": "string"
+}
    • addedInput schema / properties / time_order
      Added value: +{
+  "default": "",
+  "description": "Sort order for returned memories: 'desc' (newest first) or 'asc' (oldest first). Trigger: Pass 'desc' when the user asks for 'latest', 'recent', '从新到旧', or timeline recency, or 'asc' for chronological order. Model responsibility: Explicitly set desired temporal ordering when the user expresses recency intent. If omitted and since/until are omitted, default relevance ranking is used without temporal sorting.",
+  "title": "Time Order",
+  "type": "string"
+}
    • addedInput schema / properties / until
      Added value: +{
+  "default": "",
+  "description": "RFC 3339 timestamp with timezone offset (e.g., '2026-09-02T00:00:00+09:00'). When used with since, defines the half-open interval [since, until) with until > since (maximum 90 days span). Trigger: Pass when the user query specifies an end date or bounded window (e.g., 昨天 end of day, 截至8月10日, 8月10日到9月1日). Model responsibility: Convert natural language end bounds into absolute timestamps with timezone offsets. Consequence: If omitted, the window is open-ended on the upper side or derived heuristically.",
+  "title": "Until",
+  "type": "string"
+}
  2. Changed7 schema fields changed
    • addedInput schema / additionalProperties
      Added value: +false
    • removedInput schema / properties / agent_id
      Removed value: -{
-  "default": "",
-  "description": "Optional client-supplied agent label for memory attribution.",
-  "title": "Agent Id",
-  "type": "string"
-}
    • removedInput schema / properties / agent_instance_id
      Removed value: -{
-  "default": "",
-  "description": "Optional stable, non-secret agent instance ID for per-client attribution.",
-  "title": "Agent Instance Id",
-  "type": "string"
-}
    • removedInput schema / properties / explain
      Removed value: -{
-  "default": false,
-  "description": "Whether to include retrieval explanation details with search or recall results.",
-  "title": "Explain",
-  "type": "boolean"
-}
    • removedInput schema / properties / memory_type
      Removed value: -{
-  "default": "%",
-  "description": "Memory type/category filter or value, such as episodic, identity, procedural, semantic, working, auto, or %.",
-  "title": "Memory Type",
-  "type": "string"
-}
    • removedInput schema / properties / path_filter
      Removed value: -{
-  "default": "%",
-  "description": "Memory path filter; % matches all paths.",
-  "title": "Path Filter",
-  "type": "string"
-}
    • removedInput schema / properties / prefer_working
      Removed value: -{
-  "default": false,
-  "description": "Whether to prioritize working/session-state memories in retrieval.",
-  "title": "Prefer Working",
-  "type": "boolean"
-}
  3. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds useful behavioral context by noting the lookup is lightweight, unscoped, and returns only the few most relevant memories, which helps the agent calibrate expectations beyond the 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 two tight sentences with no filler. It front-loads the core behavior, then immediately provides routing to alternatives, earning every word.

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 rich input schema, detailed parameter descriptions, output schema, and safe annotations, the description provides sufficient high-level context. It covers what the tool does, its lightweight nature, and how it differs from the closest siblings, so an agent has what it needs to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all seven parameters in detail, including triggers, model responsibilities, and consequences. The description itself adds no parameter-level meaning, but the baseline of 3 is appropriate because the schema carries the full burden.

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 uses a specific verb ('Recall') and resource ('saved memories'), and clearly characterizes the operation as a lightweight, unscoped lookup returning the few most relevant results. It also distinguishes itself from sibling tools by naming search_memory and recall_context, so an agent can tell them apart without opening schemas.

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?

The description explicitly states when to use this tool versus alternatives: use search_memory for a scoped lookup and recall_context for a multi-item context pack. It also frames the tool as something to run 'before answering,' giving clear contextual guidance.

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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TDQS

A3.9/5.0
Disambiguation3/5

Multiple memory-retrieval tools (recall, search_memory, recall_context, read_memory, memory_overview) have overlapping purposes; detailed descriptions help but an agent could easily pick the wrong one. Similarly, ledger/open_ledger and todo/open_todo_board split text vs UI interaction, and forget overlaps with todo delete_all and ledger deletion.

Naming Consistency3/5

Most tools follow a verb_noun snake_case pattern (read_memory, update_memory, search_memory, open_ledger). However, several tools use bare nouns or verbs (forget, ledger, project, todo, recall, remember) and memory_overview is noun_noun, creating inconsistent conventions.

Tool Count3/5

19 tools is on the heavy side for the apparent scope, and the surface includes several pairs that duplicate the same domain in text vs UI form (ledger/open_ledger, todo/open_todo_board). Still, the count is defensible given the combined memory, project, TODO, and ledger coverage.

Completeness4/5

Memory has full lifecycle coverage (remember, read, search/recall, update, forget, restore), and TODO and Ledger workflows are largely complete. Minor gaps exist—no explicit project deletion and no pure text list-projects tool—but core workflows do not dead-end.

Resources