Is the Ask Ai Data Connector Ask Ai MCP server down?

mcp-registry/com.ask-ai-data-connector/ask-ai · v1.0.0

healthy
Last checked 2026-10-21 13:47 UTC · handshake 331ms

As of 2026-10-21 13:47 UTC, the Ask Ai Data Connector Ask Ai MCP server is up with a Glimind reliability score of 91/100 (grade A). Over the last 30 days it answered 100% of Glimind's liveness checks. It had 12 outages in 90 days, with a median recovery of 3 minutes.

uptime (30d checks)
100%
checks observed
4,111
outages (90d)
12
tools
66

Ask questions across Shopify, Klaviyo, GA4 and 20+ e-commerce sources in plain English.

Install Ask Ai Data Connector Ask Ai MCP server

Add to Cursor Add to VS Code

Claude Code

claude mcp add --transport http ask-ai-data-connector-ask-ai-mcp-server https://ask-ai-data-connector.com/mcp

Claude Desktop · Windsurf · Cline (mcpServers JSON)

{
  "mcpServers": {
    "ask-ai-data-connector-ask-ai-mcp-server": {
      "type": "http",
      "url": "https://ask-ai-data-connector.com/mcp"
    }
  }
}

Third-party server, listed as discovered. Review its source before installing.

Agent-readiness: C (79/100)

C (79/100): 0 error(s), 5 warning(s) across 66 tool(s); ~29,309 tokens of tool definitions.
Scores higher than 31% of 15,461 MCP servers Glimind has audited.
Its tool list costs an agent ~29,309 tokens per session — more than 95% of servers.
79 → 79 since 2026-10-17
no tool-poisoning patterns
Schemas88
Descriptions100
Safety hints100
Token cost23
Client compatibility70
Protocol100
Reliability85
Stability68

What to fix (7)

3 tool definition(s) exceed ~1,200 tokens each (largest: get_connector_data, ~2,396).

Move long reference material out of descriptions (link a doc or expose a resource), drop repeated examples, and collapse large enums. Trimming these to ~600 tokens saves ~3,326 tokens every session. How to fix →

Tools: get_connector_data, record_metric_snapshot, set_intervention · saves ~3,326 tokens/session

66 tools on one server. Tool-selection accuracy drops as the menu grows, and some clients (e.g. Cursor) cap the number of active tools.

Group tools by task into smaller servers or toolsets, and merge near-duplicates behind one tool with a mode parameter. How to fix →

2 tool(s) have parameters with no type (no type/enum/anyOf/$ref).

Give every parameter a "type" (or "enum"/"anyOf"). Untyped parameters make models guess — strings for numbers, objects for arrays. How to fix →

Tools: get_insights, query_metric_snapshots

Loading this server's tool list costs an agent ~29,309 tokens before any work — more than 95% of MCP servers.

Split rarely-used tools into a separate server or a toolset the client can enable, or expose one "search tools" entry point. Many agents load every connected server's tools into every request. How to fix →

Handshake + tools/list takes ~1,516 ms.

Serve tools/list from memory, keep the process warm, and host close to your users. Every new agent session pays this before its first call.

No tool declares an outputSchema.

Declare outputSchema and return structuredContent (MCP 2025-06-18+). Agents can then parse results without guessing, and chain tools reliably. How to fix →

In the last 30 days: 0 tool(s) removed and 8 schema change(s).

Add new tools or optional parameters instead of renaming or removing; deprecate first (say so in the description) and remove later. Agents cache tool contracts. How to fix →

Auto-fix: 2 corrected tool definitions

Paste-ready definitions with the problems above fixed. Changes marked verify need a human look. All of them as JSON: /v1/readiness/mcp-registry%2Fcom.ask-ai-data-connector%2Fask-ai/fixes

get_insights 1 change
{
  "name": "get_insights",
  "description": "Get the business improvement checklist (the FINDINGS to act on). If insights exist, returns the current checklist with status. If no insights exist (or refresh is requested), returns a data bundle with all key metrics from connected sources — use this data to generate actionable business recommendations (typically 3–8, as many as the data supports), then call save_insights to store them. You can also call save_insights with a single insight at any time during conversation. Use report: 'thread' (",
  "inputSchema": {
    "type": "object",
    "properties": {
      "tags": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Filter to insights that contain ANY of these tags (OR semantics). E.g. ['seo'] or ['paid']."
      },
      "report": {
        "enum": [
          "checklist",
          "thread"
        ],
        "type": "string",
        "description": "Default: 'checklist'. 'thread' retrieves a linked sequence of related findings as one story — requires threadId or insightId. (For fix outcomes / win-rate, use get_interventions(report: 'outcomes').)"
      },
      "status": {
        "description": "Filter by status. Pass a single value (e.g. 'open') or an array (e.g. ['open','addressed']) for OR semantics. Allowed values: open, addressed, superseded, dismissed.",
        "type": "string"
      },
      "refresh": {
        "type": "boolean",
        "description": "Set to true to generate fresh insights even if existing ones are present. Default: false — returns existing checklist."
      },
      "category": {
        "enum": [
          "revenue",
          "marketing",
          "operations",
          "customers",
          "seo"
        ],
        "type": "string",
        "description": "Filter to a single category."
      },
      "threadId": {
        "type": "string",
        "description": "Filter to a single thread (works on checklist and thread reports). Used to view a story end-to-end without category/tag filtering."
      },
      "insightId": {
        "type": "string",
        "description": "For report: 'thread' — resolve the threadId from this insight and return the full thread (alternative to passing threadId directly)."
      },
      "includeCompleted": {
        "type": "boolean",
        "description": "Default true. Set to false to return only active (open) insights."
      }
    }
  },
  "annotations": {
    "title": "Saved insights",
    "readOnlyHint": true,
    "openWorldHint": false,
    "idempotentHint": true
  },
  "title": "Saved insights"
}
query_metric_snapshots 1 change
{
  "name": "query_metric_snapshots",
  "description": "Query recorded metric snapshots as a time series. Use to answer 'show me all CVR snapshots for UK over the last 6 months', 'how has ROAS evolved by month?', etc. Returns canonical snapshot data (matches the dashboard and monthly digest exactly) plus a `charts` array of ready-to-render line chart specs (one per metric; multi-series when the query spans multiple stores or segments). DEFAULT TO RENDERING THE CHART when the LLM is asked to show a metric over time — these are the SAME numbers the mer",
  "inputSchema": {
    "type": "object",
    "properties": {
      "list": {
        "type": "boolean",
        "description": "If true, returns the metric registry (canonical keys, units, descriptions). Use this when you don't know which metrics are available."
      },
      "limit": {
        "type": "integer",
        "description": "Max snapshots to return (default 100, max 500)"
      },
      "store": {
        "type": "string",
        "description": "Filter to a specific store/region key. Accepts short form ('acme-store') or full domain ('acme-store.myshopify.com') — both normalize to the short form used in storage."
      },
      "metric": {
        "type": "string",
        "description": "Canonical metric key. Omit to return snapshots across all metrics (filtered by other params)."
      },
      "source": {
        "type": "string",
        "description": "Filter by source ('shopify', 'ga4', 'tw', etc.)"
      },
      "endDate": {
        "type": "string",
        "description": "Only include snapshots whose periodStart is <= this date (YYYY-MM-DD)"
      },
      "segment": {
        "type": "string",
        "description": "Filter to a specific segment"
      },
      "insightId": {
        "type": "string",
        "description": "Only snapshots linked to this insight"
      },
      "startDate": {
        "type": "string",
        "description": "Only include snapshots whose periodEnd is >= this date (YYYY-MM-DD)"
      },
      "confidence": {
        "description": "Filter by confidence. Pass 'high' / 'medium' / 'low' to match exactly, or 'minHigh' / 'minMedium' to require AT LEAST that level (treats null as 'high'). Useful for benchmarks where you only want trustworthy values.",
        "type": "string"
      },
      "periodKind": {
        "enum": [
          "rolling",
          "calendar",
          "custom"
        ],
        "type": "string",
        "description": "Filter by period anchoring. 'calendar' = discrete buckets only; 'rolling' = trailing windows only. Mixing the two in a single time series silently distorts trends."
      },
      "periodLength": {
        "enum": [
          "day",
          "week",
          "month",
          "quarter",
          "year"
        ],
        "type": "string",
        "description": "Filter by bucket size — combine with periodKind to e.g. fetch only calendar-month rows."
      }
    }
  },
  "annotations": {
    "title": "Metric snapshot history",
    "readOnlyHint": true,
    "openWorldHint": false,
    "idempotentHint": true
  },
  "title": "Metric snapshot history"
}

Get alerted

Get an email or webhook when this server's readiness drops by 10+ points, tool-poisoning text appears, or a tool breaks:

curl -X POST https://glimind.com/v1/subscriptions -H 'content-type: application/json' -d '{"tools":["mcp-registry/com.ask-ai-data-connector/ask-ai"],"email":"[email protected]"}'

Agents: watch_tool({"toolId": "mcp-registry/com.ask-ai-data-connector/ask-ai"}) over MCP.

Passed

Largest tool definitions

Static checks of the advertised tools/list (schema, descriptions, annotations, tool-poisoning patterns, names, size) plus observed protocol, uptime, latency and contract changes. Read-only: no tool is ever called. Weights: schema 15, docs 20, safety 15, efficiency 15, compatibility 15, protocol 5, reliability 10, stability 5. Token counts are estimates (~3.6 characters per token).

Operator? Fix these, then re-check live. Embed the readiness badge:

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Tool-definition log

Glimind records every version of this server's tool definitions in a signed, append-only transparency log. Current manifest sha256:9a966c9b77d0b6cb…, first seen 2026-10-19, included at leaf 27373 of 39,865. Version history · verify what your client received.

Tools (66)

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Add to Cursor Add to VS Code

Claude Code

claude mcp add --transport http glimind https://glimind.com/mcp

Claude Desktop · Windsurf · Cline (mcpServers JSON)

{
  "mcpServers": {
    "glimind": {
      "type": "http",
      "url": "https://glimind.com/mcp"
    }
  }
}

FAQ

Is the Ask Ai Data Connector Ask Ai MCP server down right now?

As of 2026-10-21 13:47 UTC, the Ask Ai Data Connector Ask Ai MCP server is up with a Glimind reliability score of 91/100 (grade A). Over the last 30 days it answered 100% of Glimind's liveness checks. It had 12 outages in 90 days, with a median recovery of 3 minutes.

How many tools does Ask Ai Data Connector Ask Ai MCP server provide?

Ask Ai Data Connector Ask Ai MCP server advertises 66 tools, including ask_moby, complete_intervention, get_abandoned_checkouts, get_analytics, get_anomalies.

How do I install Ask Ai Data Connector Ask Ai MCP server?

Point your MCP client at this streamable-http endpoint: https://ask-ai-data-connector.com/mcp

Is the Ask Ai Data Connector Ask Ai MCP server safe to connect to an AI agent?

Glimind scanned all 66 tool definitions for tool-poisoning patterns — hidden or invisible characters, instructions that override the model, concealment from the user, requests to read credential or MCP config files, conversation exfiltration and cross-tool shadowing — and found none. This checks what the server tells the model, not what its code does; review the source before granting access to sensitive data.

Operator of Ask Ai Data Connector Ask Ai MCP server?

Status page: 90-day uptime, every outage and how long it lasted.

Claim this listing to get the Verified operator marker, a remediation checklist and owner analytics, or embed the live badge from any of the tool pages above. Maintainers: alerts, deploy attestation and monthly reports.

Status is an automated estimate from Glimind's liveness checks (handshake and tool listing only; no tool is ever called) and may lag or differ from your network. Glimind is not affiliated with this server or its operator. Operator? Request a correction or opt out at https://glimind.com/opt-out.

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