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Oleg67

halflife-mcp

by Oleg67

memory_remember

Store one long-term memory with kind, content, and context so AI agents can recall preferences, procedures, facts, and episodes. Duplicates reinforce existing memories; contradictions replace them.

Instructions

Store one memory. kind: preference | procedural | fact | episodic | trace. content: the memory itself, short (for preference/procedural include why and how to apply). description: one keyword-rich line (used for matching; defaults to the first line). provisional: true when inferred rather than stated by the user. contradicts: id of a memory this one replaces. entities: [{"name", "type"}] for the specific files/tools/projects/people/concepts it is about (type: file, repo, tool, library, service, person, concept, ticket, command, project, organization). relations: [{"src", "rel", "dst"}] between those entities (rel: uses, depends_on, part_of, configures, fixed, caused, contradicts, supersedes, related_to, owns, prefers). source: where it came from (URL, file path). A near-duplicate reinforces the existing memory instead of creating a new one.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindYes
nameNo
scopeNo
sourceNo
contentYes
entitiesNo
relationsNo
confidenceNo
contradictsNo
descriptionNo
provisionalNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly=false, destructive=false, openWorld=false), so the bar is lower, and the description adds real behavior: near-duplicates reinforce instead of creating a new memory, description defaults to the first line, and contradicts replaces an existing memory. It omits what happens on write failure or how the returned id is used.

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?

Front-loads the core action, then uses compact per-field clauses with no filler; every line maps to a parameter or behavior. It is dense and list-like but appropriate for an 11-parameter tool.

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?

With an output schema present, return values need not be described, and the description compensates well for 0% schema coverage by documenting most fields and the dedup/replacement behavior. The unaddressed name, scope, and confidence parameters keep it from being fully complete.

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 coverage is 0%, so the description carries the full burden and does most of it, defining kind values, content expectations, description, provisional, contradicts, entities (with allowed types), relations (with allowed rel values), and source. However, three parameters (name, scope, confidence) are never explained, leaving a real gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Opens with a specific verb+resource ('Store one memory') and enumerates the kind taxonomy, so the agent knows exactly what is being written. It does not explicitly contrast with the closest sibling memory_reinforce, though the near-duplicate note gestures at it.

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?

Usage is implied rather than stated: the kind list and the 'provisional: true when inferred rather than stated' note hint at when to write, but there is no explicit when-to-use/when-not or routing to memory_recall/memory_forget. Adequate but leaves the agent to infer intent.

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