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Glama

XMemo

by io.github.yonro

Analyze XMemo memory text

analyze_memory_text
Read-onlyIdempotent

Counts a word or phrase across XMemo memories, optionally grouped by day, month, agent, location, or type, when the user asks how often a given word or topic appears.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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.
phraseYesExact word or phrase to count across visible XMemo memories.
group_byNoOptional grouping: none, month, agent, location, memory_type, type, source, path, bucket, or day.none
match_modeNoHow to match phrase: exact, case_insensitive, or regex_lite.exact
max_examplesNoMaximum number of short snippets to include.
include_examplesNoWhether to include a few short matching memory snippets.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

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

  1. Changed2 schema fields changed
    • changedInput schema / properties / since / description
      Previous value: -"Only include memories or activity on or after this ISO 8601 timestamp."New value: +"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."
    • changedInput schema / properties / until / description
      Previous value: -"Only include memories or activity on or before this ISO 8601 timestamp."New value: +"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."
  2. Added
  3. Removed
  4. First observed

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already cover readOnly, idempotent, and non-destructive behavior, so the description is not required to repeat safety information. It adds the count-and-group semantics but does ot disclose any further behavioral nuances such as timezone fallback, visibility limits, or example-snippet behavior; those live in the schema instead.

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?

A single front-loaded sentence states the action, scope, optional grouping, and the user trigger without waste. Every clause contributes either to selection or invocation.

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 read-only counting/analytic tool, the description provides the essential selection and invocation context. The rich input schema, output schema, and annotations carry the remaining details, so nothing critical is missing at the decision level.

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% and each parameter already has detailed definitions and triggers, so the description does not need to compensate. It adds a small amount by refreshing the grouping concepts (day, month, agent, location, type), but that is largely redundant with 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?

The description clearly states the verb ('Counts') and resource ('XMemo memories'), so the core purpose is unmistakable. It also includes the user-intent trigger ('when the user asks how often...'), which helps separate it from nearby tools like search_memory or recoll, though it does not explicitly name or contrast sibling tools.

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?

It starts a clear when-to-use condition: user asks how often a word or topic appears. This gives a solid usage signal and discourages using it for pure retrieval or listing. It does not explicitly mention when not to use it or name alternatives, so it stopes short of full routing 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