ai-dememory

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Vector Readiness Status

memory.vector_status

Read-only

Determines whether recall fixtures support proceeding with a vector search experiment based on configurable recall threshold and minimum failed cases.

Instructions

Report whether recall fixtures justify a future vector search experiment.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "memory.vector_status"
  }
}

Input Schema

Name	Required	Description	Default
`min_failed_cases`	No
`recall_threshold`	No

Output Schema

Name	Required	Description	Default
`recall`	Yes
`decision`	Yes
`rationale`	Yes
`generated_at`	Yes
`failed_case_ids`	Yes
`min_failed_cases`	Yes
`recall_threshold`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.0.0Jul 9, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe, read-only operation. The description adds that the tool reports a judgment ('whether ... justify'), which gives functional context beyond the annotations. No behavioral contradictions.

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, front-loaded sentence that conveys the tool's core purpose with no unnecessary words. Every part earns its place.

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 the tool's simplicity (read-only check, two numeric parameters) and the presence of annotations and an output schema, the description covers the essential 'what' and 'why'. It could elaborate on the conditions for a 'justified' experiment, but the output schema likely fills that gap.

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 input schema provides names, types, defaults, and constraints for both parameters, but the description does not explain their meanings (e.g., what 'min_failed_cases' refers to, or how 'recall_threshold' influences the output). With 0% schema description coverage, the description should compensate, but it does not.

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 uses a specific verb ('Report') and clearly identifies the resource ('whether recall fixtures justify a future vector search experiment'). This distinguishes the tool from siblings like memory.recall_fixture_status, which likely reports raw fixture data, not a readiness judgment.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives (e.g., memory.recall_fixture_status or other recall-related tools). The agent must infer use cases from the purpose statement alone.

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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