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query_memory

Retrieve stored facts tied to a query from solo or shared agent memory to avoid re-explaining context. Use at session start or whenever recalling prior details prevents repeat questions.

Instructions

Recall prior facts relevant to query, from this agent's own memory (scope="solo") or the pool shared across this user's agents (scope="shared"). Call this at session start, and any other time recalling prior context would save the user from re-explaining something - check here before asking them to repeat themselves or guessing at context you don't have.

digest=True ignores query and returns the most recently written active facts instead, as an opt-in "catch me up" convenience; call it explicitly at session start if you want one, it's never automatic.

A pending_ingest field appears when memory files have been written that the graph hasn't heard about yet. Read each listed file and call write_episode with the entities and facts it states, then mark it done with echo-memory pending --done <path>. The queue exists because extraction needs a model and this server never calls one.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo
scopeYes
top_kNo
digestNo

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A4.6/5.0
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 does well: it discloses that digest=True ignores the query, returns the most recently written active facts, and is never automatic. It also reveals the pending_ingest field behavior and why the queue exists, which is valuable operational context.

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 front-loaded with the main purpose, then usage, then digest, then the pending_ingest workflow. Each paragraph is relevant and non-redundant; the length is justified by the tool's complexity.

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?

The description covers purpose, usage timing, scope semantics, digest behavior, and the pending_ingest workflow. It is less complete on the output format of ordinary recall results and top_k semantics, which matters more because there is no output schema.

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?

The schema has 0% description coverage, so the description must compensate. It explains scope values ('solo' vs 'shared') and the digest parameter's behavior in detail. However, top_k is never mentioned, and the behavior of a null query without digest is left unspecified.

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 states a specific verb and resource: 'Recall prior facts relevant to query' from memory. It immediately clarifies the two scopes ('solo' vs 'shared'), making the tool's function unmistakable and distinct from write-oriented siblings like write_episode.

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?

Usage is explicitly prescribed: 'Call this at session start, and any other time recalling prior context would save the user from re-explaining something.' It also tells the agent to check here before asking the user to repeat themselves, and it explains when digest should be invoked explicitly.

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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