Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@LLM Pulse MCP Server show my brand mentions for last 7 days"
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.
LLM Pulse is an AI visibility analytics platform for monitoring brand mentions, citations, sentiment, competitor share of voice, and GEO performance across AI search engines.
This repository contains a small runnable wrapper for the hosted LLM Pulse MCP server. It does not contain the private LLM Pulse application source code.
https://api.llmpulse.ai/api/v1/mcpTransport: Streamable HTTP
Authentication: Bearer token
Create an LLM Pulse API key in the app, then send it as:
Authorization: Bearer llmpulse_your_key_hereAPI keys are available on Scale plans and above.
Related MCP server: ai-visibility-mcp
Run the wrapper with an API key to expose the hosted LLM Pulse MCP tools through stdio:
npm install
LLMPULSE_API_KEY=llmpulse_your_key_here npm startWithout LLMPULSE_API_KEY, the wrapper still starts and exposes a read-only setup/status tool. This lets registries verify that the server starts and responds to MCP introspection without requiring a secret.
docker build -t llmpulse-mcp .
docker run --rm -i -e LLMPULSE_API_KEY=llmpulse_your_key_here llmpulse-mcpProject and competitor dimensions
AI visibility, mention rate, citation rate, and weighted visibility metrics
Brand mentions, citations, sources, sentiments, and prompt execution data
Share of voice and top source analytics
Recommendation, GEO Writer, Search Console, and AI traffic data where plan access allows
API docs: https://api.llmpulse.ai/api-docs
OpenAPI: https://api.llmpulse.ai/openapi.json
Product site: https://llmpulse.ai
{
"mcpServers": {
"llm-pulse": {
"type": "streamable-http",
"url": "https://api.llmpulse.ai/api/v1/mcp",
"headers": {
"Authorization": "Bearer llmpulse_your_key_here"
}
}
}
}Questions? Contact info@llmpulse.ai.
Check whether this wrapper is configured with an LLM Pulse API key and show setup details for the hosted MCP endpoint.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool is readOnly, idempotent, and non-destructive. The description adds that it checks configuration and shows details, which aligns with annotations. No additional behavioral traits are disclosed beyond what annotations provide, so the description does not add significant transparency beyond the structured fields.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is front-loaded with the action 'Check whether...' and contains no redundant information. Every word contributes meaning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters, no output schema, and comprehensive annotations, the description fully informs the agent about the tool's purpose and output. No additional information is needed for a simple status check.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, and schema description coverage is 100% (trivially). The description does not need to add parameter details. Baseline for zero parameters is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'check' and clearly identifies the resource: 'whether this wrapper is configured with an LLM Pulse API key and show setup details'. The tool has no siblings, so distinction is implicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied by the description: it is for verifying the API key configuration and viewing setup details. Since there are no sibling tools, explicit when-not or alternative guidance is not needed. The context is clear but lacks an explicit directive like 'Use this to verify setup'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0llmpulse_mcp_statusWith only one tool, there is no possibility of confusion between tools. The agent can clearly identify this single tool's purpose without ambiguity.
A single tool trivially follows a consistent naming pattern with itself. No inconsistencies or mixed conventions exist.
The server provides only one tool, which is too few for a meaningful integration. A status check alone does not justify an MCP server; typical servers offer multiple operations for interaction.
The server is severely incomplete: it only checks configuration status and offers no tools to actually use the LLM Pulse API. This creates a dead end for agents seeking to perform real tasks.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Monitor brand visibility, citations, competitors, sentiment, and GEO performance in AI search.
Measure how AI engines cite your brand. Cross-engine GEO visibility, as agent tools.
Brand visibility auditing across LLMs, AI search, and answer engines with GEO reports and scores.
AI-visibility monitoring for your brand across ChatGPT, Claude, Perplexity & Gemini.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/LLM-Pulse/llmpulse-mcp'
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