How do I use TrueSource GEO MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@TrueSource GEO MCP Server Audit my-website.com for AI readiness" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

TrueSource GEO MCP Server

by SaschaDeforth

Overview Schema Related Servers Score Discussions

TypeScript

Remote

TrueSource GEO MCP Server

AI-Readiness auditing as an MCP tool surface — for Claude Desktop, Cursor, VS Code Copilot, and 70+ other MCP clients.

What is this?

This is a Model Context Protocol (MCP) server that exposes TrueSource's GEO-Audit methodology as standardized tools. Any MCP-compatible AI assistant can:

Audit websites for AI-readiness (robots.txt, llms.txt, schema markup, E-E-A-T signals)
Generate GEO files (AI-optimized robots.txt, llms.txt)
Create VibeTags for emotional AI brand resonance
Check robots.txt for AI bot allow/block status

Related MCP server: maxaeo-ai-visibility-mcp

Quick Start

Option A: npx (Recommended)

⚠️ The -y flag is critical — without it, npx silently waits for install confirmation in the background and the server freezes.

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "truesource": {
      "command": "npx",
      "args": ["-y", "truesource-geo-mcp"],
      "env": {
        "TRUESOURCE_API_KEY": "your-api-key-here"
      }
    }
  }
}

Cursor / VS Code

Add to .cursor/mcp.json or VS Code MCP settings:

{
  "mcpServers": {
    "truesource": {
      "command": "npx",
      "args": ["-y", "truesource-geo-mcp"],
      "env": {
        "TRUESOURCE_API_KEY": "your-api-key-here"
      }
    }
  }
}

Option B: Local Build

cd packages/geo-mcp
npm install
npm run build

{
  "mcpServers": {
    "truesource-geo": {
      "command": "node",
      "args": ["/absolute/path/to/packages/geo-mcp/build/index.js"],
      "env": {
        "TRUESOURCE_API_KEY": "your-api-key-here"
      }
    }
  }
}

3. Restart Your MCP Host & Use

Ask your AI assistant:

"What's the AI-readiness score for my-website.com?"
"Generate a robots.txt for https://example.com"
"Check which AI bots are blocked on competitor.com"
"Create VibeTags for our brand at my-website.com"

Available Tools

Tool	Trigger	Description
`geo_score`	"audit", "score", "check AI readiness"	Full AI-readiness audit → 0-100 score, grade, checks, recommendations
`geo_inject`	"generate robots.txt", "create llms.txt"	Generate AI-optimized robots.txt + llms.txt (ready to deploy)
`vibetags_generate`	"brand perception", "VibeTags", "how AI sees"	Emotional AI brand resonance analysis (4-layer VibeGap Bridge)
`geo_check_robots`	"robots.txt", "which bots blocked"	Quick robots.txt AI bot status check (9 bots)

Environment Variables

Variable	Required	Default	Description
`TRUESOURCE_API_KEY`	Yes	—	API key for Railway backend authentication
`TRUESOURCE_API_URL`	No	Railway production URL	GEO-Inject API base URL
`TRUESOURCE_TIMEOUT`	No	`30000`	Request timeout in ms (MCP hosts timeout at ~60s)

Testing with MCP Inspector

npm run inspect

This opens the interactive MCP Inspector at http://localhost:6274, where you can:

See all registered tools and their schemas
Invoke tools with test data
Inspect JSON-RPC request/response pairs

Architecture

┌─────────────────────────────┐
│  MCP Host (Claude Desktop)  │
│  User: "Audit example.com"  │
└─────────────┬───────────────┘
              │ Stdio (JSON-RPC 2.0)
              ▼
┌─────────────────────────────┐
│  truesource-geo-mcp v1.2.0  │
│  (TypeScript MCP Server)    │
│                             │
│  Tools:                     │
│  ├── geo_score              │
│  ├── geo_inject             │
│  ├── vibetags_generate      │
│  └── geo_check_robots       │
└─────────────┬───────────────┘
              │ HTTPS + Bearer Auth
              ▼
┌─────────────────────────────┐
│  Railway API                │
│  (FastAPI / Python)         │
│  truesource-mcp-api         │
│                             │
│  POST /audit/robust         │
│  POST /activate/robots      │
│  POST /activate/llms        │
│  POST /semantize            │
│  GET  /check/robots         │
└─────────────────────────────┘

Changelog

v1.2.0 (2026-03-29) — Gemini Review Release

Security: API key authentication (Bearer token via TRUESOURCE_API_KEY)
Performance: Timeout reduced 180s → 30s (MCP host compatibility)
Performance: HTML streaming with </head> early-abort for Schema/OG checks
DX: Improved tool descriptions with explicit trigger words for better LLM routing
DX: npx -y install flow documented (prevents silent freeze)

v1.1.0 (2026-03-29) — Optimization Release

Performance: Parallelized 5 checks with Promise.allSettled (3s → ~1s)
Bugfix: Register geo_check_robots tool
Bugfix: Fix {{HOST}} placeholder in generated robots.txt
Bugfix: Fix response format mismatch in check-robots

v1.0.0 (2026-03-28) — Initial Release

3 tools: geo_score, geo_inject, vibetags_generate
Stdio transport, Railway API integration

License

MIT — TrueSource AI / Sascha Deforth

Available Tools

4 tools

geo_check_robotsA

ALWAYS use this tool when the user asks specifically about robots.txt, AI bot access, or which crawlers are allowed or blocked. Quickly checks GPTBot (ChatGPT), ClaudeBot, PerplexityBot, Google-Extended (Gemini), DeepSeekBot, GrokBot, and more. Use this as a fast first check before running a full audit with geo_score. Requires a URL.

ParametersJSON Schema

Name	Required	Description	Default
`url`	Yes

TDQS

A4.6/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Description discloses the specific bots checked (GPTBot, ClaudeBot, etc.), implying a read-only check. With no annotations, it adequately covers the behavioral traits for a simple query tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences: first gives usage imperative and purpose, second adds detail. Front-loaded and free of redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simplicity (1 param, no output schema), the description covers when and what it does adequately. Lacks error handling details but sufficient for a fast check tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The only parameter 'url' is not described in the schema (0% coverage), but the description adds 'Requires a URL', providing the needed semantic context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool checks robots.txt for AI bot access, and distinguishes from sibling 'geo_score' by positioning it as a fast first check before a full audit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Clear when-to-use instruction: 'ALWAYS use this tool when the user asks specifically about robots.txt, AI bot access, or which crawlers are allowed or blocked.' Also advises on pre-check vs full audit with geo_score.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

geo_injectA

ALWAYS use this tool when the user asks to generate, create, or fix robots.txt or llms.txt files for AI visibility. These files tell AI crawlers (ChatGPT, Claude, Perplexity, Gemini) what content to index. Without them, a website is invisible to generative search. Requires a URL. Pass files='robots' or 'llms' or 'robots,llms'. Returns ready-to-deploy file contents.

ParametersJSON Schema

Name	Required	Description	Default
`url`	Yes
`files`	Yes

TDQS

A4.3/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden. It explains the purpose (AI visibility), but does not disclose behavioral details like authentication, side effects, or whether the tool validates the URL. The return type is mentioned ('ready-to-deploy file contents') but lacks specifics about format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is four sentences, concise and front-loaded with the key instruction. It avoids repetition but could be slightly more structured (e.g., using bullet points for parameter options).

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description adequately explains the return value ('ready-to-deploy file contents'). It covers the parameters and usage context. However, it could clarify the output format (e.g., single string or multiple files) and whether both files are returned when both specified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must explain parameters. It does so by stating 'Requires a URL' and showing allowed values for 'files' ('robots', 'llms', 'robots,llms'), which adds significant meaning beyond the schema's bare types.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool generates, creates, or fixes robots.txt and llms.txt files, which is a specific verb+resource. It distinguishes from sibling tools like geo_check_robots (checking) and geo_score (scoring) by focusing on generation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'ALWAYS use this tool when the user asks to generate, create, or fix robots.txt or llms.txt files', providing a clear condition for use. It implies when not to use by contrasting with other siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

geo_scoreA

ALWAYS use this tool when the user asks to evaluate, audit, score, check, or benchmark a website's AI readiness, AI visibility, or GEO score. Requires a valid URL. Returns a 0-100 AI Visibility Score, letter grade (A-F), detailed check results (robots.txt, llms.txt, Schema.org, Open Graph, AI bot access), and prioritized recommendations. Tip: Run this on the user's domain AND their top 3 competitors for a comparison table.

ParametersJSON Schema

Name	Required	Description	Default
`url`	Yes

TDQS

A4.7/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, but the description fully discloses behavior: requires a valid URL, returns a 0-100 score, letter grade, detailed checks (robots.txt, llms.txt, Schema.org, Open Graph, AI bot access), and recommendations. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences and a tip—every part is informative and earns its place. The description is front-loaded with the key usage instruction.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given one simple parameter and no output schema, the description fully covers input requirements, output structure, and practical usage (competitors). It is complete for an agent to use correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The only parameter is 'url' with no schema description (0% coverage). The description merely says 'Requires a valid URL,' which adds minimal extra meaning. For a simple string parameter this is adequate but not enriched.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool evaluates AI readiness/AI visibility/GEO score, with specific verbs like evaluate, audit, score, check, benchmark. It distinguishes from siblings (geo_check_robots, geo_inject, vibetags_generate) by focusing on overall scoring.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'ALWAYS use this tool when the user asks to evaluate, audit, score, check, or benchmark...' providing clear when-to-use guidance. It also suggests using it on the user's domain and competitors, adding practical context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

vibetags_generateA

ALWAYS use this tool when the user asks about brand perception, emotional positioning, VibeTags, or how AI systems see a brand. Analyzes the brand personality and generates semantic hooks that help ChatGPT, Perplexity, and Gemini understand the emotional dimensions of a brand. Returns ready-to-implement HTML snippets (Meta, JSON-LD, screen-reader). Requires a URL.

ParametersJSON Schema

Name	Required	Description	Default
`url`	Yes
`brand`	No
`industry`	No

TDQS

A3.7/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description bears full burden. It explains the tool analyzes brand personality and generates semantic hooks and HTML snippets, but does not disclose potential side effects, authentication needs, or rate limits. It implies a read-like operation but is not explicit about non-destructiveness.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with usage instruction, followed by function and output, in 3 concise sentences. It is well-structured but slightly redundant (mentions VibeTags twice). Could be slightly more efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 3 parameters, no output schema, and no annotations, the description covers the main purpose and output but leaves gaps: parameter semantics are incomplete, and the term 'semantic hooks' is not fully explained. It provides decent context but not complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% (no parameter descriptions in schema). The description only mentions the 'url' parameter implicitly ('Requires a URL'), leaving 'brand' and 'industry' completely unexplained. This creates a significant gap for the agent.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: analyzing brand perception and generating VibeTags for AI systems. It uses strong verbs like 'analyzes' and 'generates', and specifies the resource (brand personality). It also explicitly distinguishes from sibling tools (geo-related).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description starts with 'ALWAYS use this tool when the user asks about brand perception...', providing explicit usage context. It also includes a constraint ('Requires a URL'). However, it does not mention when not to use or alternatives, though sibling tools are unrelated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A4.4/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: checking robots.txt, generating robots/llms files, scoring AI readiness, and generating VibeTags. No overlap or ambiguity.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., geo_check_robots, vibetags_generate). The naming is predictable and clear.

Tool Count5/5

With 4 tools, the server is well-scoped for its domain of AI visibility and brand perception. Each tool covers a core functionality without being excessive or insufficient.

Completeness5/5

The tool set covers the essential workflows: checking, generating, scoring, and brand analysis. There are no obvious gaps for the stated purpose of managing AI visibility and brand perception.

Maintenance

ActivityInactive

ResponsivenessSyncing

Resources

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TrueSource GEO MCP Server

What is this?

Quick Start

Option A: npx (Recommended)

Claude Desktop

Cursor / VS Code

Option B: Local Build

3. Restart Your MCP Host & Use

Available Tools

Environment Variables

Testing with MCP Inspector

Architecture

Changelog

v1.2.0 (2026-03-29) — Gemini Review Release

v1.1.0 (2026-03-29) — Optimization Release

v1.0.0 (2026-03-28) — Initial Release

License

Available Tools

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

SitePulsar MCP Serverofficial

Latest Blog Posts

MCP directory API