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evermemos-mcp

evermemos-mcp

by tt-a1i
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

request_status

Check the async processing status of a prior remember request using its request_id. Determine if extraction is queued or complete, then verify success/error and lifecycle state.

Instructions

Check the async processing status for a prior remember request. Use this when remember returned a request_id and you need to know whether extraction is still queued or has been reported complete by upstream. Check success/error first, then interpret lifecycle.state.

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": {
      "request_id": "<request_id>"
    },
    "name": "request_status"
  }
}

Input Schema

NameRequiredDescriptionDefault
request_idYesQueued remember request_id returned by remember

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.7

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description carries the burden. It discloses the response interpretation order: 'Check success/error first, then interpret lifecycle.state,' and implies a read-only status poll. It doesn't elaborate on possible errors or side effects, but provides meaningful behavioral context.

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?

Two sentences: the first states the purpose, the second gives when-to-use and interpretation guidance. Every sentence earns its place with no redundancy.

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?

The tool is simple (one param, no output schema), and the description covers usage context and key response fields. It doesn't detail all lifecycle states or error formats, but the provided guidance is sufficient for a basic status-check tool.

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 coverage is 100% with the request_id parameter already described as 'Queued remember request_id returned by remember.' The description reinforces this provenance but adds no new semantic detail, so it meets the baseline 3.

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 the specific verb 'Check' and clearly identifies the resource: async processing status for a prior remember request. It distinguishes itself from sibling tools by focusing on status checking of a remember request_id.

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?

Explicitly states when to use: 'when remember returned a request_id and you need to know whether extraction is still queued or has been reported complete by upstream.' It gives clear context but doesn't explicitly list alternatives or when-not-to-use scenarios, hence a 4.

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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