Skip to main content
Glama
vbcherepanov

total-agent-memory

memory_explain_search

Read-onlyIdempotent

Performs a memory search and returns a per-tier breakdown with scores and flags to help debug why records did or did not appear in results.

Instructions

v11.0: same as memory_search_fast but returns a per-tier breakdown (fts/semantic/graph/fuzzy/hyde with raw scores, the merged RRF list, rerank_applied flag, embedding_space). Use to debug why a record did or didn't surface for a query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoall
limitNo
queryYes
projectNo
embedding_spaceNo

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

The description discloses the key output behaviors: per-tier breakdown with raw scores, merged RRF list, rerank_applied flag, and embedding_space. It does not contradict the read-only, idempotent, and non-destructive annotations, and adds meaningful detail about what the tool returns beyond what annotations imply.

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 a single, efficient sentence that immediately conveys the relationship to memory_search_fast, the added output details, and the intended use case. No unnecessary words or repetition.

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 explains the output structure and use case, which are important for a debugging tool. However, it lacks any explanation of the parameter semantics (especially type and project) and does not describe the expected behavior when parameters are omitted. Given the moderate complexity of the tool (5 parameters, no output schema), the description is only partially complete.

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

Parameters2/5

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

The schema provides zero parameter descriptions (0% coverage). The description only mentions embedding_space, leaving query, type, limit, and project unexplained. The parameter names are somewhat self-explanatory, but the enum values for type and the role of project are not described, so the description fails to compensate for the lack of schema documentation.

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 tool's purpose: it is a variant of memory_search_fast that returns a per-tier breakdown for debugging search results. It explicitly names the sibling tool and the specific use case, leaving no ambiguity.

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 explicitly says to use it 'to debug why a record did or didn't surface for a query,' which provides clear usage guidance. However, it does not explicitly state when not to use it (e.g., for normal search use memory_search_fast), though this is strongly implied by the 'same as' phrasing.

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/vbcherepanov/total-agent-memory'

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