midas-memory-mcp

by vornicx

Overview Schema Source Related Servers Discussions Admin

Python

Local

Forget matching

forget_matching

Destructive

Erase memories matching a query with a reviewable dry run. Preview deletions before permanent removal.

Instructions

Topic-level erasure ("forget what you know about X") with a reviewable audit.

Matches memories at relevance >= min_relevance. By default this is a DRY RUN: it returns what would be deleted so you (or the user) can review; call again with dry_run=false to delete. Deletion bypasses durability protections — an explicit erasure request outranks retention — and returns the full list of removed memories as the audit trail.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "forget_matching"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No
`query`	Yes
`dry_run`	No
`namespace`	No
`min_relevance`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 12, 2026

TDQS

A4.4/5.0

Behavior5/5

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

The description adds significant detail beyond annotations: dry-run safety, that deletion bypasses durability protections, and that the tool returns the list of removed memories as an audit trail. This fully discloses the behavioral implications.

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?

Three sentences, each serving a distinct purpose: purpose statement, dry-run explanation, and deletion behavior/audit. No verbose or redundant phrasing. Well front-loaded.

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?

Given no output schema and 5 parameters, the description covers the key behaviors (dry-run, deletion, audit). It could mention namespace filtering, but overall it provides enough context for an agent to use the tool correctly.

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?

Schema description coverage is 0%, so the description must compensate. It explains min_relevance (relevance threshold) and dry_run (dry-run vs actual deletion) but does not describe query, limit, or namespace parameters. While helpful for two parameters, it leaves three undocumented.

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 states 'Topic-level erasure ("forget what you know about X") with a reviewable audit.', which clearly specifies the verb (erase/forget) and resource (memories matching a topic). It distinguishes from siblings like 'forget' and 'forget_all' by focusing on concept-based matching.

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 the default dry-run behavior and the need to call again with dry_run=false to delete. It also notes that deletion bypasses durability protections. However, it does not explicitly compare to alternative tools (e.g., when to use 'forget' vs matching) or state prerequisites.

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