# Imagine AI Benchmark (remote · benchmark.useimagine.ai)

LinkedIn content benchmark for 900+ B2B companies and their teams, with an open scoring method.

- Trust score: 80/100 (high trust)
- Change this week: +3
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-11-04

## Components

- remote · `benchmark.useimagine.ai`: 80/100 (this document), [markdown](https://verifymcp.io/servers/ai-useimagine-benchmark/api-mcp.md), [page](https://verifymcp.io/servers/ai-useimagine-benchmark/api-mcp)

## Channel facts

- Endpoint: `https://benchmark.useimagine.ai/api/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `0.1.0`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-11-04.

- **Endpoint Security**: 80/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - No authorisation is required to call this server. Every tool declares its destructiveHint and none is destructive, so open access doesn't expose one.
  - HTTPS is enforced; there's no plaintext access path.
  - The HSTS (Strict-Transport-Security) header is present.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 56/100
  - AI-judged instruction clarity (good).
  - Context-footprint check failed: tool/resource definitions use about 1237 tokens (~247/item across 5 items; 5 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 83/100
  - Stability observed for 25 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
- **Tool Safety**: 100/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - We read all 5 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 6 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 60/100
  - Spec-recency check failed: implements MCP spec 2025-06-18; the latest is 2026-07-28.

## Install

### How do I install the Imagine AI Benchmark MCP server?

Imagine AI Benchmark is a hosted endpoint at https://benchmark.useimagine.ai/api/mcp, so there is nothing to install locally. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

### Claude

```bash
claude mcp add --transport http ai-useimagine-benchmark 'https://benchmark.useimagine.ai/api/mcp'
```

### Cursor

```json
{
  "mcpServers": {
    "ai-useimagine-benchmark": {
      "url": "https://benchmark.useimagine.ai/api/mcp"
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "ai-useimagine-benchmark": {
      "type": "http",
      "url": "https://benchmark.useimagine.ai/api/mcp"
    }
  }
}
```

### Codex

```toml
[mcp_servers.ai-useimagine-benchmark]
url = "https://benchmark.useimagine.ai/api/mcp"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "ai-useimagine-benchmark": {
      "type": "remote",
      "url": "https://benchmark.useimagine.ai/api/mcp",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add ai-useimagine-benchmark --url 'https://benchmark.useimagine.ai/api/mcp' --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  ai-useimagine-benchmark:
    url: "https://benchmark.useimagine.ai/api/mcp"
```

### Netclaw

```json
{
  "McpServers": {
    "ai-useimagine-benchmark": {
      "Transport": "http",
      "Url": "https://benchmark.useimagine.ai/api/mcp"
    }
  }
}
```

### Vellum

```bash
assistant mcp add ai-useimagine-benchmark -t streamable-http -u 'https://benchmark.useimagine.ai/api/mcp'
```

### Other

```json
{
  "mcpServers": {
    "ai-useimagine-benchmark": {
      "type": "http",
      "url": "https://benchmark.useimagine.ai/api/mcp"
    }
  }
}
```

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-11-03 (score 80, +1)

No change was recorded against any check on this day. Stability & Change Management went from 77 to 80. That category is still filling its 30-day observation window: 23 days of observed history at the previous scan, 24 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-11-01 (score 79, +1)

No change was recorded against any check on this day. Stability & Change Management went from 70 to 73. That category is still filling its 30-day observation window: 21 days of observed history at the previous scan, 22 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-30 (score 78, +1)

No change was recorded against any check on this day. Stability & Change Management went from 63 to 67. That category is still filling its 30-day observation window: 19 days of observed history at the previous scan, 20 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-28 (score 77, +1)

No change was recorded against any check on this day. Stability & Change Management went from 57 to 60. That category is still filling its 30-day observation window: 17 days of observed history at the previous scan, 18 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-26 (score 76, +1)

No change was recorded against any check on this day. Stability & Change Management went from 50 to 53. That category is still filling its 30-day observation window: 15 days of observed history at the previous scan, 16 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-23 (score 75, +1)

No change was recorded against any check on this day. Stability & Change Management went from 40 to 43. That category is still filling its 30-day observation window: 12 days of observed history at the previous scan, 13 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-21 (score 74, +1)

No change was recorded against any check on this day. Stability & Change Management went from 33 to 37. That category is still filling its 30-day observation window: 10 days of observed history at the previous scan, 11 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-19 (score 73, +1)

No change was recorded against any check on this day. Stability & Change Management went from 27 to 30. That category is still filling its 30-day observation window: 8 days of observed history at the previous scan, 9 at this one. The score rises as the window fills, whether or not the server changes.

## MCP tools (5)

### `list_companies` (~132 tokens)

List tracked companies

List companies in the benchmark catalog (customers, competitors, industry peers). Filter by name substring, industry, or tracking. Returns ids you pass to every other tool.

Input parameters:

- `industry` (string): Exact industry label, e.g. "Sales & Marketing Tech", "AI Infrastructure", "Fintech". Omit to see all industries.
- `limit` (integer): Max rows, default 200, max 500.
- `query` (string): Case-insensitive name substring, e.g. "rho".
- `trackingOnly` (boolean): Only companies in the scheduled scrapes (tracking_enabled). Default false.

### `company_fit_summary` (~237 tokens)

One company, the research way

A company's page fit plus the distribution of its people's geometric means (median, baseline band), top people and who is under the 18-post floor. Company typical post = median over up to 5 counted fitted people plus the company page: founders, CEO and C-suite who posted in the last 90 days first, then the most active fitted people (by original posts in that window). With exactly one fitted account (a person or the page) the company ranks on that account's own fit, flagged "1 account"; it is held back while teammates still await their history pull. people.counted / countedPersonIds say who is in. Use for 'how is X doing on LinkedIn'.

Input parameters:

- `company` (string): Company name or dashboard slug, if no id.
- `companyId` (string): Company UUID. Workspace keys: omit = your own company.
- `dateFrom` (string): Inclusive start date YYYY-MM-DD (post_date).
- `dateTo` (string): Inclusive end date YYYY-MM-DD (post_date).
- `workspaceId` (string): Internal callers only: default to this workspace's own company.

### `industry_landscape` (~280 tokens)

Industry landscape

Every account in an industry (or a custom company set): per-company rollup (page fit + people median over the 5 most active fitted people (peopleCounted), preliminary = "Few posts" under 40 team posts, solo = "1 account" when one fitted account carries the company), population vs the research baseline, and category lift with the author effect removed (which post types beat each author's own typical post). Public callers get bands and pooled lifts, the same as Discover. Use for "what works in fintech" or "where does X sit in its market".

Input parameters:

- `companyIds` (array): Custom company set (e.g. a customer + its competitors). Workspace keys: omit (with no industry) = own company + current competitors from the dashboard.
- `dateFrom` (string): Inclusive start date YYYY-MM-DD (post_date).
- `dateTo` (string): Inclusive end date YYYY-MM-DD (post_date).
- `industry` (string): Industry label, e.g. "Sales & Marketing Tech". Omit with companyIds for a custom set; omit both = the live dashboard set for workspace keys, the whole catalog for operators.
- `limit` (integer): Max accounts listed, default 40, max 300.
- `workspaceId` (string): Internal callers only: default the set from this workspace.

### `methodology` (~35 tokens)

How to read benchmark numbers

Imagine AI research rules (log-normal engagement, geometric means, research baseline 49/77/130). Call once before interpreting any numbers.

### `category_breakdown` (~185 tokens)

Category (post type) breakdown

Post counts and engagement per content category (product update, hiring, personal story, industry insight, ...) for companies or people.

Input parameters:

- `companyIds` (array): Company UUIDs. Workspace keys: omit = the live dashboard set (own company + current competitors, see the workspace tool).
- `dateFrom` (string): Inclusive start date YYYY-MM-DD (post_date).
- `dateTo` (string): Inclusive end date YYYY-MM-DD (post_date).
- `includeReposts` (boolean): Include reposts. Default false.
- `personIds` (array): Person UUIDs (narrows to these people).
- `source` (string): 'company' = the company page only, 'people' = its tracked people only, 'all' = both. Default varies per tool.
- `workspaceId` (string): Internal callers only: default the set from this workspace.

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/ai-useimagine-benchmark/api-mcp#diagnostics

## Score history

- 2026-11-04: 80
- 2026-11-03: 80
- 2026-11-02: 79
- 2026-11-01: 79
- 2026-10-31: 78
- 2026-10-30: 78
- 2026-10-29: 77
- 2026-10-28: 77
- 2026-10-27: 76
- 2026-10-26: 76
- 2026-10-25: 75
- 2026-10-24: 75
- 2026-10-23: 75
- 2026-10-22: 74
- 2026-10-21: 74
- 2026-10-20: 73
- 2026-10-19: 73
- 2026-10-18: 72
- 2026-10-17: 72
- 2026-10-16: 71
- 2026-10-15: 71
- 2026-10-14: 70
- 2026-10-13: 70
- 2026-10-12: 70
- 2026-10-10: 69

## Common questions

### What is the Imagine AI Benchmark MCP server?

Imagine AI Benchmark is an MCP server listed in the public MCP registry as ai.useimagine/benchmark. LinkedIn content benchmark for 900+ B2B companies and their teams, with an open scoring method. This page covers its hosted endpoint (https://benchmark.useimagine.ai/api/mcp).

### Is the Imagine AI Benchmark MCP server safe to use?

Imagine AI Benchmark scores 80 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

### What tools does the Imagine AI Benchmark MCP server expose?

Imagine AI Benchmark exposes 5 tools: list_companies, company_fit_summary, industry_landscape, methodology, category_breakdown. Their descriptions and schemas cost roughly 869 tokens of context every time the server is loaded.

### Does the Imagine AI Benchmark MCP server require authentication?

No. We connected to Imagine AI Benchmark without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.

### Is the Imagine AI Benchmark MCP server still maintained?

Imagine AI Benchmark is still listed as active in the MCP registry. We last reached this channel on 4 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

## Links

- Remote endpoint: https://benchmark.useimagine.ai/api/mcp
- Website: https://benchmark.useimagine.ai/
- Changelog RSS feed: https://verifymcp.io/servers/ai-useimagine-benchmark/api-mcp.xml
- Changelog JSON feed: https://verifymcp.io/servers/ai-useimagine-benchmark/api-mcp.json
- HTML version of this page: https://verifymcp.io/servers/ai-useimagine-benchmark/api-mcp