Skip to main content
Glama

memory_search

Filter stored memories by status, category, date, or entity. Leave the query empty to list items created or reactivated since a given timestamp.

Instructions

Filtered vector search by status, category, date, or entity. Pass an EMPTY query to enumerate by recency instead (no vector search, no embedder needed): with since/active_since this answers 'what's new/changed since ' without knowing what to query for.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNo
queryYesSearch text. Empty string switches to enumeration mode: nodes listed by last_reactivated_at descending, honoring the same filters.
filtersNoSupported keys: status (list), category, entity, since (ISO date — created_at >= since; tz-naive treated as UTC), active_since (ISO date — last_reactivated_at >= active_since; tz-naive treated as UTC). With a non-empty query, results are vector-seeded and k-limited; use an empty query for enumeration.

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior2/5

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

No annotations are provided, and the description does not mention whether the operation is read-only, whether it has side effects, or any auth or rate-limit considerations. Since it is a search tool, it likely does not modify data, but the description does not confirm this, leaving behavior partly unclear.

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?

The description is concise and mixes the core purpose with the special enumeration mode efficiently. The punctuation and parenthetical asides are slightly complex, but the overall structure is clear and not verbose.

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

Completeness3/5

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

The description covers the main usage modes and filter details, but it does not mention what the return values look like (no output schema) or how the `k` parameter affects the result set. For a search tool, this leaves some context about expected output incomplete.

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?

The `filters` parameter receives a detailed explanation covering its keys and semantics, and the `query` parameter's empty-string behavior is well described. However, the `k` parameter is only given a type and default without any explanation of its meaning (e.g., maximum number of results), so parameter semantics are only partially covered.

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 clearly states the core function as a filtered vector search by status, category, date, or entity, and explicitly introduces the enumeration mode with an empty query. This makes the purpose immediately understandable and distinguishable from typical search 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?

The description provides practical guidance on when to use the empty-query enumeration mode, especially for answering 'what's new/changed since <ts>' without needing a query. It does not explicitly compare with sibling tools, but the use cases are well enough defined for the primary scenarios.

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

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Astrix-Labs/papez'

If you have feedback or need assistance with the MCP directory API, please join our Discord server