# MetaGO Algorithms (npm · @metago-ai/algorithms)

927 deterministic algorithms as 57 MCP tools: coupling, value, distance, correlation, reduction.

- Trust score: 81/100 (high trust)
- Change this week: 0
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-11-05

## Components

- npm · `@metago-ai/algorithms`: 81/100 (this document), [markdown](https://verifymcp.io/servers/metago-ai-metago-algorithms/metago-ai-algorithms.md), [page](https://verifymcp.io/servers/metago-ai-metago-algorithms/metago-ai-algorithms)

## Channel facts

- Registry: `npm`
- Package: `@metago-ai/algorithms`
- Version: `1.0.2`
- Transport: `stdio`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, 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 2026-11-05.

- **Supply Chain Security**: 98/100
  - No malware found by supply-chain analysis.
  - No known CVEs affecting this package version or its production dependencies.
  - No install/post-install scripts declared.
  - 30 of 94 dependencies flagged as unhealthy.
- **Provenance & Transparency**: 48/100
  - Source repository is publicly reachable at the declared URL.
  - Provenance check failed: no build-provenance attestation is published.
  - Clear OSI-approved license (MIT).
  - Actively maintained (last published 8 days ago).
  - Publishes a security disclosure policy (SECURITY.md).
- **Schema Quality & AI Usability**: 63/100
  - AI-judged instruction clarity (fair).
  - Tool/resource definitions use about 4138 tokens (~72/item across 57 items; 57 tools + 0 resources), lean.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 100/100
  - No destabilizing schema changes in the last 30 days.
- **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**: 75/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - 0 of 2 tool(s) whose name or description implies an irreversible operation declare an MCP destructiveHint annotation; "execute" implies "execute" and declares no destructiveHint at all, which the MCP spec reads as destructive by default.
  - An AI judge read all 57 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### How do I install the MetaGO Algorithms MCP server?

MetaGO Algorithms runs locally as an npm package, launched with npx -y @metago-ai/algorithms. 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 metago-ai-metago-algorithms -- npx -y @metago-ai/algorithms
```

### Cursor

```json
{
  "mcpServers": {
    "metago-ai-metago-algorithms": {
      "command": "npx",
      "args": [
        "-y",
        "@metago-ai/algorithms"
      ]
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "metago-ai-metago-algorithms": {
      "command": "npx",
      "args": [
        "-y",
        "@metago-ai/algorithms"
      ]
    }
  }
}
```

### Codex

```bash
codex mcp add metago-ai-metago-algorithms -- npx -y @metago-ai/algorithms
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "metago-ai-metago-algorithms": {
      "type": "local",
      "command": [
        "npx",
        "-y",
        "@metago-ai/algorithms"
      ],
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add metago-ai-metago-algorithms --command npx --arg -y --arg @metago-ai/algorithms
```

### Hermes

```yaml
mcp_servers:
  metago-ai-metago-algorithms:
    command: "npx"
    args: ["-y", "@metago-ai/algorithms"]
```

### Netclaw

```json
{
  "McpServers": {
    "metago-ai-metago-algorithms": {
      "Transport": "stdio",
      "Command": "npx",
      "Arguments": [
        "-y",
        "@metago-ai/algorithms"
      ]
    }
  }
}
```

### Vellum

```bash
assistant mcp add metago-ai-metago-algorithms -t stdio -c npx -a -y @metago-ai/algorithms
```

### Other

```json
{
  "mcpServers": {
    "metago-ai-metago-algorithms": {
      "command": "npx",
      "args": [
        "-y",
        "@metago-ai/algorithms"
      ]
    }
  }
}
```

## 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.

### 2026-11-05 (score 81, +1)

- [functional improvement] Security disclosure: unverified → pass

### 2026-11-04 (score 80, −1)

- [functional regression] Security disclosure: pass → unverified

### 2026-10-28 (score 81, +1)

- [security] Stability: 0.97 → pass

### 2026-10-26 (score 80, +1)

No change was recorded against any check on this day. Stability & Change Management went from 90 to 93. That category is still filling its 30-day observation window: 27 days of observed history at the previous scan, 28 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-24 (score 79, +1)

No change was recorded against any check on this day. Stability & Change Management went from 83 to 87. That category is still filling its 30-day observation window: 25 days of observed history at the previous scan, 26 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-21 (score 78, +1)

No change was recorded against any check on this day. Stability & Change Management went from 73 to 77. That category is still filling its 30-day observation window: 22 days of observed history at the previous scan, 23 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-19 (score 77, +1)

No change was recorded against any check on this day. Stability & Change Management went from 67 to 70. That category is still filling its 30-day observation window: 20 days of observed history at the previous scan, 21 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-17 (score 76, +1)

No change was recorded against any check on this day. Stability & Change Management went from 60 to 63. That category is still filling its 30-day observation window: 18 days of observed history at the previous scan, 19 at this one. The score rises as the window fills, whether or not the server changes.

## MCP tools (57)

### `audit_check` (~88 tokens)

审计检查（20 个算法）。根据 operation 参数选择族内具体算法。operation 可选算法名关键词（如 cosine/euclidean/pearson）。不指定 operation 时执行族内首个算法。

Input parameters:

- `input` (object, required): 算法输入参数
- `operation` (string): 操作类型（算法名关键词），如 cosine/euclidean/pearson

### `bias_detect` (~88 tokens)

偏见检测（65 个算法）。根据 operation 参数选择族内具体算法。operation 可选算法名关键词（如 cosine/euclidean/pearson）。不指定 operation 时执行族内首个算法。

Input parameters:

- `input` (object, required): 算法输入参数
- `operation` (string): 操作类型（算法名关键词），如 cosine/euclidean/pearson

### `build_coupling_matrix` (~68 tokens)

构建耦生矩阵。适用：可视化关系网络。返回 NxN 矩阵。（算法 ID: ALG_T1_C_019）

Input parameters:

- `entities` (object, required): 实体列表
- `relations` (object, required): 关系列表

### `build_value_vector` (~52 tokens)

构建价值向量。适用：多维度价值量化。返回价值向量。（算法 ID: ALG_T1_C_026）

Input parameters:

- `dimensions` (object, required): 维度数据

### `chebyshev_distance` (~67 tokens)

计算切比雪夫距离（L∞ 范数）。适用：棋盘距离。返回 ≥0。（算法 ID: ALG_T1_C_006）

Input parameters:

- `a` (array, required): 向量 A
- `b` (array, required): 向量 B

### `conflict_resolve` (~89 tokens)

冲突解决（49 个算法）。根据 operation 参数选择族内具体算法。operation 可选算法名关键词（如 cosine/euclidean/pearson）。不指定 operation 时执行族内首个算法。

Input parameters:

- `input` (object, required): 算法输入参数
- `operation` (string): 操作类型（算法名关键词），如 cosine/euclidean/pearson

### `cooccurrence_frequency` (~60 tokens)

共现频率计算。适用：关联分析、推荐。返回频率值。（算法 ID: ALG_T1_C_013）

Input parameters:

- `items` (object, required): 项列表
- `window` (object, required): 窗口大小

### `cosine_similarity` (~84 tokens)

计算两个向量的余弦相似度。适用：文本相似度、推荐系统、聚类。返回 [-1,1]，越接近 1 越相似。（算法 ID: ALG_T1_C_001）

Input parameters:

- `a` (array, required): 向量 A
- `b` (array, required): 向量 B

### `coupling_calculate` (~90 tokens)

耦生度计算（65 个算法）。根据 operation 参数选择族内具体算法。operation 可选算法名关键词（如 cosine/euclidean/pearson）。不指定 operation 时执行族内首个算法。

Input parameters:

- `input` (object, required): 算法输入参数
- `operation` (string): 操作类型（算法名关键词），如 cosine/euclidean/pearson

### `coupling_clustering` (~59 tokens)

耦生聚类分析。适用：关系网络分组。返回聚类结果。（算法 ID: ALG_T1_C_025）

Input parameters:

- `entities` (object, required): 实体列表
- `k` (number, required): 聚类数

### `coupling_trend` (~50 tokens)

耦生趋势分析。适用：时间序列关系变化。返回趋势方向。（算法 ID: ALG_T1_C_024）

Input parameters:

- `timeSeries` (object, required): 时间序列数据

### `covariance` (~56 tokens)

协方差计算。适用：变量共变关系。返回协方差矩阵。（算法 ID: ALG_T1_C_030）

Input parameters:

- `a` (array, required): 数据 A
- `b` (array, required): 数据 B

### `creation_create` (~87 tokens)

创造生成（50 个算法）。根据 operation 参数选择族内具体算法。operation 可选算法名关键词（如 cosine/euclidean/pearson）。不指定 operation 时执行族内首个算法。

Input parameters:

- `input` (object, required): 算法输入参数
- `operation` (string): 操作类型（算法名关键词），如 cosine/euclidean/pearson

### `decision_decide` (~89 tokens)

决策制定（48 个算法）。根据 operation 参数选择族内具体算法。operation 可选算法名关键词（如 cosine/euclidean/pearson）。不指定 operation 时执行族内首个算法。

Input parameters:

- `input` (object, required): 算法输入参数
- `operation` (string): 操作类型（算法名关键词），如 cosine/euclidean/pearson

### `detect_asymmetry` (~55 tokens)

检测不对称耦生。适用：发现单向依赖。返回不对称对列表。（算法 ID: ALG_T1_C_023）

Input parameters:

- `pairs` (object, required): 对列表

### `dialog_manage` (~87 tokens)

对话管理（20 个算法）。根据 operation 参数选择族内具体算法。operation 可选算法名关键词（如 cosine/euclidean/pearson）。不指定 operation 时执行族内首个算法。

Input parameters:

- `input` (object, required): 算法输入参数
- `operation` (string): 操作类型（算法名关键词），如 cosine/euclidean/pearson

### `dice_coefficient` (~76 tokens)

计算 Dice 系数（2*交集/(|A|+|B|)）。适用：字符串/集合相似度。返回 [0,1]。（算法 ID: ALG_T1_C_003）

Input parameters:

- `a` (array, required): 集合 A
- `b` (array, required): 集合 B

### `euclidean_distance` (~63 tokens)

计算欧氏距离。适用：空间距离、KNN。返回 ≥0 的数值。（算法 ID: ALG_T1_C_004）

Input parameters:

- `a` (array, required): 向量 A
- `b` (array, required): 向量 B

### `evaluate_bidirectional` (~68 tokens)

双向耦生度评估。适用：分析双向关系强度。返回 {forward, backward, symmetric}。（算法 ID: ALG_T1_C_015）

Input parameters:

- `backward` (object, required): 反向数据
- `forward` (object, required): 正向数据

### `evolution_evolve` (~89 tokens)

进化计算（50 个算法）。根据 operation 参数选择族内具体算法。operation 可选算法名关键词（如 cosine/euclidean/pearson）。不指定 operation 时执行族内首个算法。

Input parameters:

- `input` (object, required): 算法输入参数
- `operation` (string): 操作类型（算法名关键词），如 cosine/euclidean/pearson

### `execute` (~73 tokens)

异步执行任意算法（支持所有 927 算法）。当用户指定算法 ID 时使用。返回 {success, output, provenance, durationMs}。

Input parameters:

- `id` (string, required): 算法 ID，如 ALG_T1_C_001
- `input` (object, required): 算法输入参数

### `execute_sync` (~74 tokens)

同步执行 T1 算法（300 个同步算法）。比 execute 更快，但不支持 async handler。返回 {success, output, provenance, durationMs}。

Input parameters:

- `id` (string, required): 算法 ID，如 ALG_T1_C_001
- `input` (object, required): 算法输入参数

### `extract_pca` (~64 tokens)

主成分提取（PCA）。适用：特征提取、降维。返回主成分。（算法 ID: ALG_T1_C_028）

Input parameters:

- `components` (number, required): 成分数
- `data` (object, required): 数据矩阵

### `frequency_process` (~88 tokens)

频率处理（48 个算法）。根据 operation 参数选择族内具体算法。operation 可选算法名关键词（如 cosine/euclidean/pearson）。不指定 operation 时执行族内首个算法。

Input parameters:

- `input` (object, required): 算法输入参数
- `operation` (string): 操作类型（算法名关键词），如 cosine/euclidean/pearson

### `fuzzy_string_match` (~68 tokens)

模糊字符串匹配。适用：拼写纠错、模糊搜索。返回 [0,1]。（算法 ID: ALG_T1_C_011）

Input parameters:

- `a` (array, required): 字符串 A
- `b` (array, required): 字符串 B

### `get_algorithm_info` (~51 tokens)

获取算法元数据（ID/名称/族/层级/复杂度/输入输出 schema/描述）。当需要了解算法详情时使用。

Input parameters:

- `id` (string, required): 算法 ID

### `get_statistics` (~33 tokens)

获取算法全局统计（总数/已实现数/各层级数/各族数）。无需参数。

### `idea_generate` (~87 tokens)

创意生成（20 个算法）。根据 operation 参数选择族内具体算法。operation 可选算法名关键词（如 cosine/euclidean/pearson）。不指定 operation 时执行族内首个算法。

Input parameters:

- `input` (object, required): 算法输入参数
- `operation` (string): 操作类型（算法名关键词），如 cosine/euclidean/pearson

### `identify_strong_pairs` (~60 tokens)

识别强耦生对。适用：发现关键关系。返回强对列表。（算法 ID: ALG_T1_C_018）

Input parameters:

- `pairs` (object, required): 对列表
- `threshold` (number, required): 阈值

### `identify_weak_pairs` (~62 tokens)

识别弱耦生对。适用：发现需要加强的关系。返回弱对列表。（算法 ID: ALG_T1_C_017）

Input parameters:

- `pairs` (object, required): 对列表
- `threshold` (number, required): 阈值

### `intuition_gauge` (~90 tokens)

直觉度量（49 个算法）。根据 operation 参数选择族内具体算法。operation 可选算法名关键词（如 cosine/euclidean/pearson）。不指定 operation 时执行族内首个算法。

Input parameters:

- `input` (object, required): 算法输入参数
- `operation` (string): 操作类型（算法名关键词），如 cosine/euclidean/pearson

### `is_superconductive` (~61 tokens)

超导态判定（耦生度>1）。适用：识别超强耦合关系。返回 boolean。（算法 ID: ALG_T1_C_016）

Input parameters:

- `couplingScore` (number, required): 耦生分数

### `jaccard_coefficient` (~79 tokens)

计算两个集合的 Jaccard 相似系数（交集/并集）。适用：集合相似度。返回 [0,1]。（算法 ID: ALG_T1_C_002）

Input parameters:

- `a` (array, required): 集合 A（数组）
- `b` (array, required): 集合 B（数组）

### `kendall_tau` (~63 tokens)

计算 Kendall Tau 秩相关系数。适用：序数变量相关性。返回 [-1,1]。（算法 ID: ALG_T1_C_009）

Input parameters:

- `a` (array, required): 数据 A
- `b` (array, required): 数据 B

### `learning_learn` (~88 tokens)

学习训练（49 个算法）。根据 operation 参数选择族内具体算法。operation 可选算法名关键词（如 cosine/euclidean/pearson）。不指定 operation 时执行族内首个算法。

Input parameters:

- `input` (object, required): 算法输入参数
- `operation` (string): 操作类型（算法名关键词），如 cosine/euclidean/pearson

### `list_algorithms` (~84 tokens)

列出算法列表，可按 tier（T1/T2/T3）或 family（COUPLING/VALUE/BIAS 等）过滤。返回 {total, algorithms}。

Input parameters:

- `family` (string): 按族过滤，如 COUPLING
- `limit` (number): 返回数量限制
- `tier` (string): 按层级过滤

### `list_families` (~27 tokens)

列出所有 20 个算法族及其算法数量。无需参数。

### `logic_reason` (~89 tokens)

逻辑推理（65 个算法）。根据 operation 参数选择族内具体算法。operation 可选算法名关键词（如 cosine/euclidean/pearson）。不指定 operation 时执行族内首个算法。

Input parameters:

- `input` (object, required): 算法输入参数
- `operation` (string): 操作类型（算法名关键词），如 cosine/euclidean/pearson

### `manhattan_distance` (~65 tokens)

计算曼哈顿距离（L1 范数）。适用：网格距离。返回 ≥0。（算法 ID: ALG_T1_C_005）

Input parameters:

- `a` (array, required): 向量 A
- `b` (array, required): 向量 B

### `memory_operate` (~89 tokens)

记忆操作（49 个算法）。根据 operation 参数选择族内具体算法。operation 可选算法名关键词（如 cosine/euclidean/pearson）。不指定 operation 时执行族内首个算法。

Input parameters:

- `input` (object, required): 算法输入参数
- `operation` (string): 操作类型（算法名关键词），如 cosine/euclidean/pearson

### `negentropy_measure` (~91 tokens)

负熵测量（49 个算法）。根据 operation 参数选择族内具体算法。operation 可选算法名关键词（如 cosine/euclidean/pearson）。不指定 operation 时执行族内首个算法。

Input parameters:

- `input` (object, required): 算法输入参数
- `operation` (string): 操作类型（算法名关键词），如 cosine/euclidean/pearson

### `normalize_coupling` (~79 tokens)

归一化耦生分数到 [0,1]。适用：标准化比较。返回归一化值。（算法 ID: ALG_T1_C_020）

Input parameters:

- `max` (number, required): 最大值
- `min` (number, required): 最小值
- `score` (number, required): 原始分数

### `pearson_correlation` (~63 tokens)

计算皮尔逊相关系数。适用：线性相关性分析。返回 [-1,1]。（算法 ID: ALG_T1_C_007）

Input parameters:

- `a` (array, required): 数据 A
- `b` (array, required): 数据 B

### `proactive_suggest` (~88 tokens)

主动建议（20 个算法）。根据 operation 参数选择���内具体算法。operation 可选算法名关键词（如 cosine/euclidean/pearson）。不指定 operation 时执行族内首个算法。

Input parameters:

- `input` (object, required): 算法输入参数
- `operation` (string): 操作类型（算法名关键词），如 cosine/euclidean/pearson

### `reasoning_chain` (~87 tokens)

推理链（49 个算法）。根据 operation 参数选择族内具体算法。operation 可选算法名关键词（如 cosine/euclidean/pearson）。不指定 operation 时执行族内首个算法。

Input parameters:

- `input` (object, required): 算法输入参数
- `operation` (string): 操作类型（算法名关键词），如 cosine/euclidean/pearson

### `record_symmetric` (~74 tokens)

记录对称耦生关系。适用：双向等权关系存储。返回记录结果。（算法 ID: ALG_T1_C_022）

Input parameters:

- `entityA` (object, required): 实体 A
- `entityB` (object, required): 实体 B
- `score` (number, required): 分数

### `reduce_dimension` (~61 tokens)

降维计算。适用：高维数据可视化。返回降维后数据。（算法 ID: ALG_T1_C_027）

Input parameters:

- `data` (object, required): 高维数据
- `targetDim` (number, required): 目标维度

### `search_algorithms` (~56 tokens)

按关键词搜索算法（在名称/描述/关键词中匹配）。返回匹配的算法列表。

Input parameters:

- `limit` (number): 返回数量限制，默认 20
- `query` (string, required): 搜索关键词

### `security_assess` (~89 tokens)

安全评估（48 个算法）。根据 operation 参数选择族内具体算法。operation 可选算法名关键词（如 cosine/euclidean/pearson）。不指定 operation 时执行族内首个算法。

Input parameters:

- `input` (object, required): 算法输入参数
- `operation` (string): 操作类型（算法名关键词），如 cosine/euclidean/pearson

### `semantic_similarity` (~63 tokens)

语义相似度计算。适用：自然语言语义匹配。返回 [0,1]。（算法 ID: ALG_T1_C_012）

Input parameters:

- `a` (array, required): 文本 A
- `b` (array, required): 文本 B

### `simplified_svd` (~56 tokens)

简化奇异值分解（SVD）。适用：矩阵分解、推荐系统。返回 U/S/V。（算法 ID: ALG_T1_C_029）

Input parameters:

- `matrix` (object, required): 输入矩阵

### `sort_coupling_scores` (~68 tokens)

排序耦生分数。适用：按强度排列关系。返回排序后的列表。（算法 ID: ALG_T1_C_021）

Input parameters:

- `order` (string, required): 排序顺序（asc/desc）
- `scores` (object, required): 分数列表

### `spearman_correlation` (~67 tokens)

计算斯皮尔曼秩相关系数。适用：非线性单调关系。返回 [-1,1]。（算法 ID: ALG_T1_C_008）

Input parameters:

- `a` (array, required): 数据 A
- `b` (array, required): 数据 B

### `time_analyze` (~87 tokens)

时间分析（49 个算法）。根据 operation 参数选择族内具体算法。operation 可选算法名关键词（如 cosine/euclidean/pearson）。不指定 operation 时执行族内首个算法。

Input parameters:

- `input` (object, required): 算法输入参数
- `operation` (string): 操作类型（算法名关键词），如 cosine/euclidean/pearson

### `time_decay_cooccurrence` (~78 tokens)

时间衰减共现计算。适用：时间加权关联分析。返回衰减后的频率。（算法 ID: ALG_T1_C_014）

Input parameters:

- `halfLife` (object, required): 半衰期
- `items` (object, required): 项列表
- `timestamps` (object, required): 时间戳列表

### `value_assess` (~89 tokens)

价值评估（65 个算法）。根据 operation 参数选择族内具体算法。operation 可选算法名关键词（如 cosine/euclidean/pearson）。不指定 operation 时执行族内首个算法。

Input parameters:

- `input` (object, required): 算法输入参数
- `operation` (string): 操作类型（算法名关键词），如 cosine/euclidean/pearson

### `weighted_cosine` (~82 tokens)

计算加权余弦相似度。适用：带权重的相似度计算。返回 [-1,1]。（算法 ID: ALG_T1_C_010）

Input parameters:

- `a` (array, required): 向量 A
- `b` (array, required): 向量 B
- `weights` (array, required): 权重向量

## Diagnostics

Captured diagnostic sections: Provenance, Dependencies. The full working is on the page: https://verifymcp.io/servers/metago-ai-metago-algorithms/metago-ai-algorithms#diagnostics

## Score history

- 2026-11-05: 81
- 2026-11-04: 80
- 2026-11-03: 81
- 2026-11-02: 81
- 2026-11-01: 81
- 2026-10-31: 81
- 2026-10-30: 81
- 2026-10-29: 81
- 2026-10-28: 81
- 2026-10-27: 80
- 2026-10-26: 80
- 2026-10-25: 79
- 2026-10-24: 79
- 2026-10-23: 78
- 2026-10-22: 78
- 2026-10-21: 78
- 2026-10-20: 77
- 2026-10-19: 77
- 2026-10-18: 76
- 2026-10-17: 76
- 2026-10-16: 75
- 2026-10-15: 75
- 2026-10-14: 74
- 2026-10-13: 74
- 2026-10-12: 73
- 2026-10-11: 72
- 2026-10-06: 71
- 2026-10-04: 67
- 2026-10-03: 67
- 2026-10-02: 67

## Common questions

### What is the MetaGO Algorithms MCP server?

MetaGO Algorithms is an MCP server listed in the public MCP registry as io.github.metago-ai/metago-algorithms. 927 deterministic algorithms as 57 MCP tools: coupling, value, distance, correlation, reduction. This page covers its npm package (@metago-ai/algorithms).

### Is the MetaGO Algorithms MCP server safe to use?

MetaGO Algorithms scores 81 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 5 November 2026. It declares no install or post-install scripts. 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 MetaGO Algorithms MCP server expose?

MetaGO Algorithms exposes 57 tools: audit_check, bias_detect, build_coupling_matrix, build_value_vector, chebyshev_distance, and 52 more. Their descriptions and schemas cost roughly 4,138 tokens of context every time the server is loaded.

### Is the MetaGO Algorithms MCP server still maintained?

MetaGO Algorithms is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

### What licence is the MetaGO Algorithms MCP server under?

MetaGO Algorithms declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.

## Links

- npm package: https://www.npmjs.com/package/@metago-ai/algorithms
- Socket report: https://socket.dev/npm/package/@metago-ai/algorithms
- Repository: https://github.com/metago-ai/metagolifeform
- Website: https://metago.life/
- Changelog RSS feed: https://verifymcp.io/servers/metago-ai-metago-algorithms/metago-ai-algorithms.xml
- Changelog JSON feed: https://verifymcp.io/servers/metago-ai-metago-algorithms/metago-ai-algorithms.json
- HTML version of this page: https://verifymcp.io/servers/metago-ai-metago-algorithms/metago-ai-algorithms