Skip to main content
Glama
MihaiBuilds

Memory Vault

by MihaiBuilds

recall

Find relevant information from stored memories using a hybrid search that ranks results by relevance. Retrieve answers to questions or topics while filtering by space, date, and token budget.

Instructions

Search your memories for information relevant to a query.

Returns chunks ranked by relevance using hybrid search (vector + full-text + RRF). Uses query enrichment (keyword extraction + variation) for better recall. Results are budgeted to fit within max_tokens to avoid flooding context.

Args: query: The search query — a question, topic, or keyword phrase. spaces: Filter to specific memory spaces (e.g. ["default", "projects"]). If omitted, searches all spaces. since: Only return memories after this date (ISO format, e.g. "2025-01-01"). limit: Maximum number of results (default 10, max 50). max_tokens: Token budget for results (default 2000). ef_search: How much of the vector index to search, 1-1000. Omit to use the default (40). Raise it when a search should have found something and did not — better recall, slower query. Worth trying before concluding a memory is missing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes
sinceNo
spacesNo
ef_searchNo
max_tokensNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

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
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden, and it delivers: it explains hybrid search (vector + full-text + RRF), query enrichment, relevance ranking, token budgeting to avoid context flooding, and the trade-off of raising ef_search. This gives the agent a strong model of what happens when the tool is invoked.

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 well organized with a brief behavioral overview followed by a clear Args list. Every sentence adds useful information, and the ef_search guidance is valuable rather than filler. The structure is front-loaded and scannable.

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 six parameters, no annotations, and no inline schema descriptions, the description covers all necessary invocation details: defaults, formats, filtering, and tuning behavior. The output schema exists, so not detailing return values is acceptable. The description is complete enough for an agent to call the tool correctly.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must document every parameter, and it does. It explains query, spaces, since with ISO example, limit and max_tokens with defaults, and ef_search with a concrete tuning recommendation. This fully compensates for the absent schema descriptions.

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 opens with 'Search your memories for information relevant to a query,' which clearly states a specific verb, resource, and intent. The sibling tools are all memory management operations (remember, forget, move), so this retrieval-focused description immediately distinguishes recall from them.

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

Usage Guidelines3/5

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

The description makes clear this tool is for searching and retrieving memory, and it gives specific guidance on when to increase ef_search. However, it does not explicitly state when to use this tool instead of the sibling tools, nor does it mention alternatives or exclusions. Usage context is implied rather than spelled out.

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/MihaiBuilds/memory-vault'

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