mcp-registry/com.vendo-ai/vendo-ai · v0.1.0
As of 2026-10-26 20:57 UTC, the Vendo Ai MCP server is up (it requires sign-in) with a Glimind reliability score of 88/100 (grade B). Over the last 30 days it answered 100% of Glimind's liveness checks.
Autonomous AI sales: agents, signal-based prospecting, outreach, CRM — 149 tools.
Point your MCP client at this streamable-http endpoint: https://api.vendo-ai.com/mcp
Loading this server's tool list costs an agent ~73,313 tokens before any work — more than 99% 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 →
4 tool definition(s) exceed ~1,200 tokens each (largest: create_campaign, ~2,090).
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 ~5,093 tokens every session. How to fix →
396 of 396 tool(s) carry no readOnlyHint/destructiveHint annotations.
Annotate read-only tools with {"readOnlyHint": true}. Agents and gateways can then run them without a confirmation prompt, and route around destructive ones. How to fix →
396 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 →
630 parameter(s) across 259 tool(s) have no description.
Add a "description" to each parameter with its format and an example ("ISO 8601 date, e.g. 2026-10-17"). This is the commonest cause of wrong arguments on the first call. How to fix →
129 tool(s) look like they change state (create/send/delete/pay…) but declare no destructiveHint.
Set annotations {"readOnlyHint": false, "destructiveHint": true|false, "idempotentHint": …}. Clients use these to ask the user before a side effect and to auto-approve safe reads. How to fix →
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 →
Requires authentication; tool definitions could not be read anonymously.
If discovery should be public, allow initialize + tools/list without credentials and gate only tools/call. Directories and routers can then list your tools.
In the last 30 days: 0 tool(s) removed and 3 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 →
Paste-ready definitions with the problems above fixed. Changes marked verify need a human look. 200 tool(s) still need parameter descriptions written by someone who knows the API. All of them as JSON: /v1/readiness/mcp-registry%2Fcom.vendo-ai%2Fvendo-ai/fixes
{
"name": "add_agent_document",
"description": "Upload a material the agent learns from (PDF, DOC, DOCX, TXT, XLS, XLSX, CSV, PPT, PPTX, KEY; ≤10 MB) from a public URL or base64. Used the next time the script is generated (generate_agent_script). Free.",
"inputSchema": {
"type": "object",
"$schema": "http://json-schema.org/draft-07/schema#",
"required": [
"id",
"filename"
],
"properties": {
"id": {
"type": "string",
"description": "Agent id (UUID) — from list_agents"
},
"file_url": {
"type": "string",
"description": "Public https URL of the file"
},
"filename": {
"type": "string",
"description": "File name with extension, e.g. \"price-list.pdf\""
},
"file_base64": {
"type": "string",
"description": "Base64 content (use instead of file_url)"
}
},
"additionalProperties": false
},
"annotations": {
"readOnlyHint": false,
"destructiveHint": false,
"idempotentHint": false
},
"title": "Attach a knowledge file to the agent"
}{
"name": "add_campaign_linkedin_account",
"description": "Add a connected account to a running campaign; pending contacts are rebalanced across accounts and backfilled. It starts sending invites in the next cycles. No credits.",
"inputSchema": {
"type": "object",
"$schema": "http://json-schema.org/draft-07/schema#",
"required": [
"id",
"account_id"
],
"properties": {
"id": {
"type": "string",
"description": "Campaign id (UUID) from list_campaigns"
},
"account_id": {
"type": "string"
}
},
"additionalProperties": false
},
"annotations": {
"readOnlyHint": false,
"destructiveHint": false,
"idempotentHint": false
},
"title": "Add a LinkedIn account to a campaign"
}{
"name": "add_custom_audience_contacts",
"description": "Append up to 5000 contacts to a custom audience. Deduplicated inside the audience by email, LinkedIn URL or phone (duplicates and contacts with no email/LinkedIn/phone are skipped). Returns added / skipped / total_contacts. No credits.",
"inputSchema": {
"type": "object",
"$schema": "http://json-schema.org/draft-07/schema#",
"required": [
"id",
"contacts"
],
"properties": {
"id": {
"type": "string",
"description": "Custom audience id (UUID)"
},
"contacts": {
"type": "array",
"items": {
"type": "object",
"properties": {
"email": {
"type": "string"
},
"phone": {
"type": "string"
},
"position": {
"type": "string"
},
"last_name": {
"type": "string"
},
"first_name": {
"type": "string"
},
"company_name": {
"type": "string"
},
"linkedin_url": {
"type": "string"
}
},
"additionalProperties": false
},
"maxItems": 5000,
"minItems": 1
}
},
"additionalProperties": false
},
"annotations": {
"readOnlyHint": false,
"destructiveHint": false,
"idempotentHint": false
},
"title": "Add contacts to a custom audience"
}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.vendo-ai/vendo-ai"],"email":"[email protected]"}'Agents: watch_tool({"toolId": "mcp-registry/com.vendo-ai/vendo-ai"}) over MCP.
create_campaign ~2,090 tokensupdate_campaign ~2,031 tokenscreate_agent_connection ~1,721 tokensupdate_agent_connection ~1,651 tokenssdr_create_search ~1,042 tokensStatic 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:
[](https://glimind.com/server/mcp-registry%2Fcom.vendo-ai%2Fvendo-ai?ref=badge)
<a href="https://glimind.com/server/mcp-registry%2Fcom.vendo-ai%2Fvendo-ai?ref=badge"><img src="https://glimind.com/v1/readiness/badge/mcp-registry%2Fcom.vendo-ai%2Fvendo-ai.svg" alt="MCP agent-readiness"></a>
.. image:: https://glimind.com/v1/readiness/badge/mcp-registry%2Fcom.vendo-ai%2Fvendo-ai.svg :target: https://glimind.com/server/mcp-registry%2Fcom.vendo-ai%2Fvendo-ai?ref=badge :alt: MCP agent-readiness
Glimind records every version of this server's tool definitions in a signed, append-only transparency log. Current manifest sha256:ed77c1ae7944997b…, first seen 2026-10-26, included at leaf 73501 of 75,543. Version history · verify what your client received.
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Glimind tells agents whether a server is up right now and how to call it, then fails over to a live alternative when it isn't.
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"
}
}
}
As of 2026-10-26 20:57 UTC, the Vendo Ai MCP server is up (it requires sign-in) with a Glimind reliability score of 88/100 (grade B). Over the last 30 days it answered 100% of Glimind's liveness checks.
Vendo Ai MCP server advertises 300+ tools, including activate_campaign_linkedin, activate_campaign_phone, add_agent_document, add_campaign_linkedin_account, add_custom_audience_contacts.
Point your MCP client at this streamable-http endpoint: https://api.vendo-ai.com/mcp
Glimind scanned all 396 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.
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.