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vbcherepanov

total-agent-memory

memory_associate

Retrieve related memories from a knowledge graph using concept resonance instead of keyword search. In composition mode, find the minimum set of memories that cover all requested concepts.

Instructions

Associative recall — brain-like spreading activation through knowledge graph. Finds memories through concept resonance, not keyword search. In 'composition' mode, finds minimum set of memories covering all needed concepts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNorecall=find related, composition=build solution from partsrecall
queryYesNatural language query
projectNoFilter by project
max_resultsNo
min_coverageNoMin coverage for composition mode (0.0-1.0)

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior2/5

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

The annotations declare readOnlyHint=false, indicating the tool may have side effects, but the description frames it as a recall operation, which implies read-only behavior. It does not disclose any potential state modifications, performance implications, or other behavioral traits. The description adds no transparency beyond the functional modes, and the mismatch with the readOnlyHint is not directly contradictory but leaves the agent uncertain about side effects.

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 two sentences, front-loaded with the core concept. Every clause adds value: the mechanism, the contrast with keyword search, and the special mode. No filler or redundancy.

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?

While the description covers the tool's purpose and modes, it does not describe the return format or output structure. Since there is no output schema, the description should indicate what the tool returns (e.g., a list of memories, scores, or a composition result). It also omits any mention of parameter interactions or edge cases, such as how max_results and min_coverage behave together. These gaps leave the agent guessing about the response shape.

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?

Schema description coverage is 80%, so most parameters are already documented. The description adds meaningful context for the 'mode' parameter by explaining the composition mode's goal (minimum set of memories covering concepts). It also clarifies that 'query' is natural language for concept resonance rather than keywords. This goes beyond the schema's brief 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 clearly states the tool performs associative recall through a knowledge graph, explicitly contrasting it with keyword search. It also distinguishes the two modes (recall and composition), which differentiates it from siblings like memory_search_by_tag and memory_timeline. The verb 'finds' and resource 'memories' are specific and unambiguous.

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 explains that this is for associative recall rather than keyword search, implying when to use it. It also describes the composition mode for building solutions from parts. However, it does not explicitly name alternative tools or provide clear when-not-to-use scenarios, only a general contrast with keyword search.

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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