REPOGEO REPORT · LITE

stacklok/toolhive

Default branch main · commit eb631d2a · scanned 6/23/2026, 6:42:30 AM

GitHub: 1,901 stars · 231 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface stacklok/toolhive, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Rephrase the README's introductory paragraph to explicitly mention "AI Model Context Protocol" and its platform nature.

Why:

CURRENT

ToolHive runs every MCP server in an isolated container, enforces identity and access policy per request when configured with an authentication source, and gives platform teams the observability they need to put MCP in production.

COPY-PASTE FIX

ToolHive is an enterprise-grade platform for securely running and managing AI Model Context Protocol (MCP) servers. It runs each MCP server in an isolated container, enforces identity and access policy per request, and provides platform teams with the observability needed to put MCP in production.

mediumtopics#2

Add more specific AI-related topics to improve categorization.

Why:

CURRENT

ai, ai-security, aicodeassistant, golang, kubernetes, mcp, mcp-security, mcp-servers, mcp-tools, model-context-protocol, security

COPY-PASTE FIX

ai, ai-security, aicodeassistant, golang, kubernetes, mcp, mcp-security, mcp-servers, mcp-tools, model-context-protocol, security, llm-ops, ai-context, context-protocol

lowreadme#3

Add a 'Comparison' section to the README to differentiate from generic infrastructure and ML platforms.

Why:

COPY-PASTE FIX

## ToolHive vs. Other Platforms

ToolHive is not a general-purpose Kubernetes platform or a generic ML orchestration tool like Kubeflow or MLflow. Instead, ToolHive is purpose-built for securely deploying and managing AI Model Context Protocol (MCP) servers, offering specialized features for policy enforcement, observability, and token optimization specifically for AI context services.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface stacklok/toolhive

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Kubernetes

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Kubernetes · recommended 1×
Istio · recommended 1×
Docker · recommended 1×
Podman · recommended 1×
NGINX Plus · recommended 1×

CATEGORY QUERY
How to securely run and manage multiple AI model context protocol servers?
you: not recommended
AI recommended (in order):
1. Kubernetes
2. Istio
3. Docker
4. Podman
5. NGINX Plus
6. Kong Gateway
7. HashiCorp Vault
8. Prometheus
9. Grafana
10. Falco
AI recommended 10 alternatives but never named stacklok/toolhive. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What open source platform helps deploy and manage AI context services on Kubernetes?
you: not recommended
AI recommended (in order):
1. Kubeflow
2. KServe
3. MLflow
4. OpenShift AI
5. Seldon Core
6. Cortex
AI recommended 6 alternatives but never named stacklok/toolhive. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of stacklok/toolhive?
pass
AI named stacklok/toolhive explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts stacklok/toolhive in production, what risks or prerequisites should they evaluate first?
pass
AI named stacklok/toolhive explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo stacklok/toolhive solve, and who is the primary audience?
pass
AI named stacklok/toolhive explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/stacklok/toolhive"><img src="https://repogeo.com/badge/stacklok/toolhive.svg" alt="RepoGEO" /></a>

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stacklok/toolhive — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite