What can you do with this server?

The Clado MCP Server provides comprehensive tools for LinkedIn data retrieval, enrichment, and analysis with credit-based usage: * Search for LinkedIn users using natural language queries with filters for school, company, match thresholds, and result limits * Retrieve detailed profile information for specific LinkedIn URLs (1 credit) * Enrich profiles with email addresses and phone numbers (1 credit) * Scrape detailed profile data including posts and comments (2 credits) * Analyze reactions and engagement data for specific LinkedIn posts * Conduct comprehensive deep research jobs combining search variations with optional email enrichment (1 credit per result) * Monitor the status of ongoing deep research jobs using unique job IDs

Which integrations are available for this server?

Enables Windsurf (part of Codeium) to leverage LinkedIn data tools for user search, profile enrichment, and contact information retrieval.

How do I use Clado 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., "@Clado MCP Server search for product managers at Google with Stanford backgrounds" 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.

Clado MCP Server

by freesolo-co

Overview Schema Related Servers Score Discussions

TypeScript

Remote

Clado MCP Server

This is an official Model Context Protocol (MCP) Server for Clado.

For detailed API documentation and usage examples, visit the official Clado documentation.

More information about the Model Context Protocol can be found here.

Related MCP server: EnrichB2B MCP Server

Installation

Manual Installation

To install the server, run:

npx @clado-ai/mcp <YOUR-CLADO-API-KEY>

Running on Cursor

Add to ~/.cursor/mcp.json like this:

{
  "mcpServers": {
    "clado": {
      "command": "npx",
      "args": ["-y", "@clado-ai/mcp"],
      "env": {
        "CLADO_API_KEY": "YOUR-API-KEY"
      }
    }
  }
}

Running on Windsurf

Add to your ./codeium/windsurf/model_config.json like this:

{
  "mcpServers": {
    "clado": {
      "command": "npx",
      "args": ["-y", "@clado-ai/mcp"],
      "env": {
        "CLADO_API_KEY": "YOUR-API-KEY"
      }
    }
  }
}

Claude Desktop app

This is an example config for the Clado MCP server for the Claude Desktop client.

{
  "mcpServers": {
    "clado": {
      "command": "npx",
      "args": ["--yes", "@clado-ai/mcp"],
      "env": {
        "CLADO_API_KEY": "your-api-key"
      }
    }
  }
}

Tools

search_for_users - Search for LinkedIn users with filters like query, school, and match threshold
enrich_linkedin - Retrieves detailed profile information for a specific LinkedIn URL (1 credit per lookup)
retrieve_contacts - Retrieves email addresses and phone numbers for LinkedIn profiles, supports reverse lookup via email/phone (4 credits for email, 10 credits for phone if found)
scrape_linkedin - Retrieves detailed profile data and posts with comments from a LinkedIn profile URL (2 credits per request)
linkedin_post_reactions - Retrieves reactions and engagement data for a specific LinkedIn post URL

License

This project is licensed under the MIT License. See the LICENSE file for details.

Available Tools

5 tools

enrich_linkedinA

Retrieves detailed profile information for a specific LinkedIn URL. Each successful lookup costs 1 credit.

ParametersJSON Schema

Name	Required	Description	Default
`linkedin_url`	Yes	The LinkedIn profile URL to look up.

TDQS

A4/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively adds value beyond the input schema by stating 'Each successful lookup costs 1 credit,' which is crucial context about cost implications. However, it doesn't cover other behavioral traits like rate limits, error handling, or response format, leaving some gaps in transparency.

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?

The description is appropriately sized with two sentences: the first states the purpose, and the second adds critical behavioral context (cost). It's front-loaded with the core functionality and avoids unnecessary details, making every sentence earn its place without waste.

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 tool's complexity (a single-parameter lookup with cost implications) and no annotations or output schema, the description is fairly complete. It covers the purpose and cost, but lacks details on output format or error cases. For a tool with no structured output documentation, it could be more comprehensive, but it provides enough context for basic usage.

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 input schema has 100% description coverage, with the parameter 'linkedin_url' clearly documented. The description doesn't add any additional meaning beyond what the schema provides (e.g., it doesn't specify URL format or validation rules). According to the rules, with high schema coverage, the baseline is 3, and the description doesn't compensate further, so this score is appropriate.

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 specific action ('Retrieves detailed profile information') and resource ('for a specific LinkedIn URL'), distinguishing it from sibling tools like 'linkedin_post_reactions' (which deals with reactions) and 'scrape_linkedin' (which might be more general). It precisely communicates what the tool does without being vague or tautological.

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

Usage Guidelines3/5

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

The description implies usage by mentioning 'for a specific LinkedIn URL,' suggesting it's for individual profile lookups, but it doesn't explicitly state when to use this tool versus alternatives like 'search_for_users' (which might search by name) or 'retrieve_contacts' (which might handle bulk data). There's no guidance on exclusions or prerequisites, leaving usage context somewhat inferred.

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

linkedin_post_reactionsC

Retrieves reactions and engagement data for a specific LinkedIn post URL.

ParametersJSON Schema

Name	Required	Description	Default
`post_url`	Yes	The LinkedIn post URL to analyze for reactions.

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'retrieves' data, implying a read-only operation, but doesn't disclose any behavioral traits such as rate limits, authentication requirements, data freshness, or what specific engagement metrics are included. This leaves significant gaps for a tool that likely interacts with external APIs.

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?

The description is a single, well-structured sentence that efficiently conveys the core purpose without any redundant information. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly.

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

Completeness2/5

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

Given the complexity of retrieving engagement data from LinkedIn (likely involving API calls), the lack of annotations, and no output schema, the description is insufficient. It doesn't explain what data is returned (e.g., types of reactions, metrics), potential limitations, or error handling, leaving the agent with incomplete context for effective tool use.

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 input schema has 100% description coverage, with the single parameter 'post_url' clearly documented as 'The LinkedIn post URL to analyze for reactions.' The description adds no additional semantic meaning beyond this, so it meets the baseline score of 3 where the schema does the heavy lifting.

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

Purpose4/5

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

The description clearly states the verb 'retrieves' and the resource 'reactions and engagement data for a specific LinkedIn post URL', making the purpose unambiguous. However, it doesn't explicitly differentiate this tool from sibling tools like 'scrape_linkedin' or 'enrich_linkedin', which might also involve LinkedIn data retrieval, so it doesn't reach the highest score.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or compare it to sibling tools like 'scrape_linkedin' or 'enrich_linkedin', leaving the agent to infer usage context independently.

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

retrieve_contactsA

Retrieves email addresses and phone numbers for LinkedIn profiles. Supports reverse lookup via email or phone. Email enrichment costs 4 credits if found, phone enrichment costs 10 credits if found.

ParametersJSON Schema

Name	Required	Description
`linkedin_url`	No	LinkedIn profile URL to get contact information for (e.g., 'https://www.linkedin.com/in/username')
`email`	No	Email address to find profile and get contact information (e.g., 'john.doe@example.com')
`phone`	No	Phone number to find profile and get contact information (e.g., '+1234567890')
`email_enrichment`	No	If true, returns emails (costs 4 credits if email found). Can be combined with phone_enrichment
`phone_enrichment`	No	If true, returns phone numbers (costs 10 credits if phone found). Can be combined with email_enrichment

TDQS

A3.8/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively adds valuable context beyond what the input schema provides: it discloses credit costs (4 credits for email enrichment if found, 10 credits for phone enrichment if found), which is crucial for understanding resource usage and potential expenses. This information isn't captured in the schema, making the description transparent about behavioral traits.

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?

The description is appropriately sized and front-loaded, consisting of two concise sentences that efficiently convey the tool's purpose, capabilities, and cost implications. Every sentence earns its place by providing essential information without redundancy or unnecessary details, making it easy for an AI agent to parse and understand quickly.

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 complexity of a tool with 5 parameters, no annotations, and no output schema, the description does a good job of providing context. It explains the tool's purpose, reverse lookup support, and credit costs, which are critical for usage. However, it doesn't describe the return format or what happens when no data is found, leaving some gaps in completeness for a tool without an output schema.

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 schema description coverage is 100%, meaning all parameters are well-documented in the schema itself. The description adds some semantic context by mentioning credit costs associated with 'email_enrichment' and 'phone_enrichment', but it doesn't provide additional meaning beyond what the schema already covers for parameters like 'linkedin_url', 'email', or 'phone'. Given the high schema coverage, a baseline score of 3 is appropriate.

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

Purpose4/5

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

The description clearly states the tool's purpose: retrieving email addresses and phone numbers for LinkedIn profiles, with support for reverse lookup via email or phone. It specifies the resource (LinkedIn profiles) and the action (retrieving contact information). However, it doesn't explicitly differentiate this tool from sibling tools like 'enrich_linkedin' or 'search_for_users', which might have overlapping functionality.

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

Usage Guidelines3/5

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

The description implies usage by mentioning reverse lookup capabilities and credit costs, but it doesn't provide explicit guidance on when to use this tool versus alternatives like 'enrich_linkedin' or 'search_for_users'. It mentions cost implications, which helps inform usage decisions, but lacks direct comparisons or exclusions for sibling tools.

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

scrape_linkedinA

Retrieves detailed profile data and posts with comments from a LinkedIn profile URL using RapidAPI. Each request costs 2 credits.

ParametersJSON Schema

Name	Required	Description	Default
`linkedin_url`	Yes	The LinkedIn profile URL to scrape.

TDQS

A3.9/5.0

Behavior4/5

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

With no annotations provided, the description carries full burden and adds valuable behavioral context: it discloses the cost per request ('2 credits'), which is a rate-limiting consideration not evident from the schema. However, it does not mention authentication requirements, rate limits beyond cost, or error handling.

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?

The description is appropriately sized and front-loaded: the first sentence states the core purpose and scope, and the second adds critical cost information. Both sentences earn their place with no wasted words or redundancy.

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's complexity (data scraping with cost implications), no annotations, and no output schema, the description is minimally adequate. It covers purpose and cost but lacks details on output structure, error cases, or prerequisites like API keys, leaving gaps for the agent to handle.

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?

Schema description coverage is 100%, so the schema already documents the 'linkedin_url' parameter fully. The description does not add any parameter-specific details beyond what the schema provides, such as URL format examples or validation rules, meeting the baseline for high schema coverage.

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 specific action ('Retrieves detailed profile data and posts with comments') and resource ('from a LinkedIn profile URL'), distinguishing it from siblings like 'enrich_linkedin' or 'linkedin_post_reactions' by specifying the comprehensive data retrieval scope.

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

Usage Guidelines3/5

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

The description implies usage context by mentioning the data source ('LinkedIn profile URL') and API provider ('RapidAPI'), but does not explicitly state when to use this tool versus alternatives like 'enrich_linkedin' or 'search_for_users', leaving the agent to infer based on the data types mentioned.

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

search_for_usersC

Search for users on Clado using filters like query, school, and match threshold.

ParametersJSON Schema

Name	Required	Description
`query`	Yes	Natural language search query to find LinkedIn profiles
`limit`	No	Maximum number of profiles to return (1–100)
`schools`	No	List of school names to filter results by, e.g., ['Stanford', 'MIT']
`companies`	No	List of company names to filter results by
`advanced_filtering`	No	Enable AI agent-based filtering to improve result quality
`search_id`	No	ID from previous search for pagination
`offset`	No	Number of results to skip for pagination

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions 'filters like query, school, and match threshold' but doesn't disclose critical traits: whether this is a read-only operation, rate limits, authentication needs, result format, or what 'match threshold' entails. For a search tool with 7 parameters, this is inadequate.

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?

The description is a single, efficient sentence with zero waste. It's front-loaded with the core purpose and includes specific examples of filters. Every word earns its place, making it easy to parse quickly.

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

Completeness2/5

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

Given the tool's complexity (7 parameters, no annotations, no output schema), the description is insufficient. It lacks details on behavioral traits, result format, pagination behavior (implied by 'search_id' and 'offset' but not explained), and differentiation from siblings. For a search tool with multiple filters, more context is needed to guide effective use.

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?

Schema description coverage is 100%, so the schema fully documents all 7 parameters. The description adds marginal value by highlighting 'query, school, and match threshold' as key filters, but 'match threshold' isn't in the schema, creating confusion. Baseline 3 is appropriate as the schema does heavy lifting, though the description's mention of an undefined parameter slightly detracts.

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

Purpose4/5

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

The description clearly states the action ('Search for users') and resource ('on Clado'), with specific filters mentioned. It distinguishes from siblings by focusing on user search rather than enrichment, posting, or scraping. However, it doesn't explicitly differentiate from 'retrieve_contacts' which might overlap in retrieving user information.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives like 'enrich_linkedin' or 'retrieve_contacts'. The description mentions filters but doesn't specify use cases, prerequisites, or exclusions. This leaves the agent without context for tool selection among siblings.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

5 tool updatesv1.0.0Jan 2, 2026

Removedcheck_deep_research_status

Changedenrich_linkedin3 fields changed

addedInput schema / properties / linkedin_url

Added value: +{
+  "description": "The LinkedIn profile URL to look up.",
+  "type": "string"
+}

removedInput schema / properties / url

Removed value: -{
-  "description": "The LinkedIn profile URL to look up.",
-  "type": "string"
-}

changedInput schema / required

Previous value: -[
-  "url"
-]New value: +[
+  "linkedin_url"
+]

Removedinitiate_deep_research

Changedretrieve_contacts6 fields changed

addedInput schema / properties / email

Added value: +{
+  "description": "Email address to find profile and get contact information (e.g., 'john.doe@example.com')",
+  "type": "string"
+}

addedInput schema / properties / email_enrichment

Added value: +{
+  "default": false,
+  "description": "If true, returns emails (costs 4 credits if email found). Can be combined with phone_enrichment",
+  "type": "boolean"
+}

changedInput schema / properties / linkedin_url / description

Previous value: -"The LinkedIn profile URL to look up."New value: +"LinkedIn profile URL to get contact information for (e.g., 'https://www.linkedin.com/in/username')"

addedInput schema / properties / phone

Added value: +{
+  "description": "Phone number to find profile and get contact information (e.g., '+1234567890')",
+  "type": "string"
+}

addedInput schema / properties / phone_enrichment

Added value: +{
+  "default": false,
+  "description": "If true, returns phone numbers (costs 10 credits if phone found). Can be combined with email_enrichment",
+  "type": "boolean"
+}

removedInput schema / required
```
Removed value: -[
-  "linkedin_url"
-]
```

Changedsearch_for_users11 fields changed

removedInput schema / properties / acceptance_threshold

Removed value: -{
-  "default": 73,
-  "description": "Minimum match score threshold (0-100). Defaults to 73.",
-  "maximum": 100,
-  "minimum": 0,
-  "type": "number"
-}

addedInput schema / properties / advanced_filtering

Added value: +{
+  "default": true,
+  "description": "Enable AI agent-based filtering to improve result quality",
+  "type": "boolean"
+}

addedInput schema / properties / companies

Added value: +{
+  "description": "List of company names to filter results by",
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}

removedInput schema / properties / company

Removed value: -{
-  "description": "Filter by company names (current or past employers)",
-  "items": {
-    "type": "string"
-  },
-  "type": "array"
-}

changedInput schema / properties / limit / default
```
Previous value: -10New value: +30
```

changedInput schema / properties / limit / description

Previous value: -"Maximum number of results to return (1–100)"New value: +"Maximum number of profiles to return (1–100)"

addedInput schema / properties / offset

Added value: +{
+  "default": 0,
+  "description": "Number of results to skip for pagination",
+  "minimum": 0,
+  "type": "number"
+}

changedInput schema / properties / query / description

Previous value: -"Natural language search query"New value: +"Natural language search query to find LinkedIn profiles"

removedInput schema / properties / school

Removed value: -{
-  "description": "Filter by school name(s), e.g., ['Stanford', 'MIT']",
-  "items": {
-    "type": "string"
-  },
-  "type": "array"
-}

addedInput schema / properties / schools

Added value: +{
+  "description": "List of school names to filter results by, e.g., ['Stanford', 'MIT']",
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}

addedInput schema / properties / search_id

Added value: +{
+  "description": "ID from previous search for pagination",
+  "format": "uuid",
+  "type": "string"
+}

7 tool updatesAug 15, 2025
- First observedcheck_deep_research_status
- First observedenrich_linkedin
- First observedinitiate_deep_research
- First observedlinkedin_post_reactions
- First observedretrieve_contacts
- First observedscrape_linkedin
- First observedsearch_for_users

Install Server

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

TDQS

A3.5/5.0

Disambiguation3/5

The tools have some overlap in purpose, particularly between 'enrich_linkedin' and 'scrape_linkedin', which both retrieve LinkedIn profile data, though 'scrape_linkedin' includes posts and comments. 'retrieve_contacts' focuses on contact information, while 'linkedin_post_reactions' and 'search_for_users' are more distinct. Descriptions help differentiate them, but confusion could arise between the profile-focused tools.

Naming Consistency4/5

Most tools follow a consistent verb_noun pattern (e.g., 'enrich_linkedin', 'scrape_linkedin', 'search_for_users'), which is clear and predictable. However, 'retrieve_contacts' uses 'retrieve' instead of a more aligned verb like 'get' or 'enrich', causing a minor deviation. Overall, the naming is mostly consistent and readable.

Tool Count5/5

With 5 tools, the count is well-scoped for a LinkedIn-focused server, covering key operations like profile enrichment, contact retrieval, post analysis, and user search. Each tool appears to serve a distinct purpose, and the number is neither too sparse nor overwhelming for the domain.

Completeness4/5

The tool set covers core LinkedIn data retrieval functions, including profile enrichment, contact information, post engagement, and user search. Minor gaps exist, such as the lack of tools for updating or managing data (e.g., no 'create_contact' or 'delete_profile'), but agents can likely work around these for typical lookup and analysis tasks.

Maintenance

ActivityInactive

ResponsivenessSyncing

Resources

Need Help?

Reddit Discussion

Related Servers

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

GTM API: LinkedIn MCP Server
com.gtm-api
Managed LinkedIn MCP server for AI agents: search, connect, message and enrich on accounts you own.
Social Media Communication
1100
LinkMCPOAuth
io.linkmcp
Hosted MCP server for LinkedIn: 31 tools for profiles, search, messaging, posts, enrichment.
Social Media Communication Search
2
LeadDelta MCP ServerOAuth
io.github.LeadDelta
MCP server for LeadDelta — manage LinkedIn connections and CRM data via AI assistants.
Customer Data Platforms Social Media Marketing
2
Fintalio LinkedIn MCP
com.fintalio
LinkedIn outreach MCP server — 19 tools for AI agents to prospect, sequence, and manage contacts.
Communication CRM Social Media
2

Related MCP Servers

LinkedIn MCP Server
Communication Search
Hritik003
F
license
Not graded
quality
D
maintenance
A Model Context Protocol server that enables seamless interaction with LinkedIn for job applications, profile retrieval, feed browsing, and resume analysis through natural language commands.
Updated a year ago (2025-04-21 05:49 UTC)
31
-
EnrichB2B MCP Server
Communication Search Developer Tools
moonlabsai
F
license
Not graded
quality
D
maintenance
A server implementing the Model Context Protocol that enables users to retrieve LinkedIn profile information and activity data via EnrichB2B API, and generate text using OpenAI GPT-4 or Anthropic Claude models.
Updated a year ago (2025-03-17 12:25 UTC)
1
-
Linkd MCP Server
Web Scraping Search Social Media
automcp-app
A
license
A
quality
D
maintenance
An unofficial Model Context Protocol server that enables programmatic access to LinkedIn data through tools like user search, company search, profile enrichment, and contact retrieval.
Updated a year ago (2025-05-28 18:41 UTC)
7
20
3
ISC
LinkedIn MCP Server
Communication Social Media
baptitse-jn
F
license
Not graded
quality
D
maintenance
A comprehensive Model Context Protocol server that enables AI assistants to interact with LinkedIn APIs for profile management, content creation, networking, messaging, and analytics.
Updated a year ago (2025-06-02 18:05 UTC)
2
-

Appeared in Searches

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/freesolo-co/search-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server

Clado MCP Server

Table of Contents

Installation

Manual Installation

Running on Cursor

Running on Windsurf

Claude Desktop app

Tools

License

Available Tools

Tool Schema Changelog

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

Appeared in Searches

Latest Blog Posts

MCP directory API