A
licenseA
qualityA
maintenanceResolves a company domain to its LinkedIn company page URL. Lightweight enrichment tool for sales and prospecting workflows.
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MIT
Company Identity Resolver MCP Server
by mambalabsdev
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., "@Company Identity Resolver MCP ServerResolve canonical identity for Stripe"
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.
Company Identity Resolver MCP Server
An MCP server that exposes the Mamba Labs Company Identity Resolver as a single tool. Install one package and give your MCP client a way to turn any combination of company name, domain, or LinkedIn URL into one canonical company identity tuple with confidence scores, wrapping the Mamba Labs actor on Apify and returning Clay-ready flat JSON.
What's Inside
Related MCP server: mcp-gtm-signals-aggregator
What it does
This server gives an AI client one tool:
resolve_company_identity: resolve any combination of company name, domain, or LinkedIn URL into the canonical name, primary domain, and LinkedIn company URL, each with a 0-100 confidence score plus an overall score and a match method. It cross-checks the inputs you give it, resolves the ones you do not, and flags conflicts (a domain and a LinkedIn slug that disagree) instead of merging them.
All of the work runs on Apify. This package is a thin client that routes the tool call to the actor and hands back the result.
Quick start
You need Node.js 18 or newer and an Apify account with an API token.
Add this to your Claude Desktop config:
{
"mcpServers": {
"company-identity-resolver": {
"command": "npx",
"args": ["-y", "@mambalabsdev/mcp-company-identity-resolver"],
"env": {
"APIFY_TOKEN": "your-apify-token"
}
}
}
}Get your token at https://console.apify.com/account/integrations, paste it in, and restart Claude Desktop. The tool will be available.
Prerequisites
Node.js 18 or newer
An Apify account with an API token
Example prompts
"Resolve the canonical identity for the company 'Stripe' and give me its domain and LinkedIn URL with confidence scores."
"I have the domain notion.so; what is the canonical company name and LinkedIn URL?"
"Do domain stripe.com and LinkedIn slug notion refer to the same company?"
"Resolve the company at linkedin.com/company/gitlab-com into its domain and name."
Tool and inputs
resolve_company_identity:
company_name(string): company name, e.g. Stripe. Provide at least one ofcompany_name,domain, orlinkedin_url.domain(string): bare company domain, e.g. stripe.com. The strongest canonical key when provided.linkedin_url(string): LinkedIn company URL (https://www.linkedin.com/company/stripe) or bare slug (stripe).skipCache(boolean): force a fresh resolution and ignore the 7 day result cache.
The output is one flat row: the echoed inputs, the canonical name, domain, and linkedin_url, the overall confidence_score, the match_method (exact_domain, search_resolved, linkedin_pattern, jsonld, conflict, or unresolved), the per-field domain_confidence, linkedin_confidence, and name_confidence, and a resolved boolean.
Full actor documentation
For the complete input and output reference, pricing, and run history, see the Company Identity Resolver actor on the Apify Store (canonical immutable Actor ID URL):
https://apify.com/mambalabs/lr8fTRAmZCBZmuwwh
Mamba Labs GTM Suite
This server is part of the Mamba Labs GTM Suite, a fleet of twelve specialized MCP servers for go-to-market signal intelligence, each backed by a dedicated Apify actor.
Actor | Immutable Actor ID |
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Built by Mamba Labs | npm | Apify Store
License
MIT
Built by Mamba Labs. https://apify.com/mambalabs
Available Tools
1 toolresolve_company_identityResolve Company IdentityARead-onlyIdempotent
Resolve any combination of company name, domain, or LinkedIn URL into one canonical company identity: the name, primary domain, and LinkedIn company URL, each with a 0-100 confidence score plus an overall score and a match method. Cross-checks the inputs you give it, resolves the ones you do not, and flags conflicts (a domain and a LinkedIn slug that disagree) instead of merging them. Login-free and public-data only. Returns flat Clay-ready JSON. Read-only; requires an APIFY_TOKEN and consumes Apify credits per call.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| company_name | No | Company name, e.g. Stripe. Provide at least one of company_name, domain, or linkedin_url. | |
| domain | No | Bare company domain, e.g. stripe.com. The strongest canonical key when provided. | |
| linkedin_url | No | LinkedIn company URL (https://www.linkedin.com/company/stripe) or bare slug (stripe). | |
| skipCache | No | Force a fresh resolution and ignore the 7 day result cache. |
TDQS
A4.5/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, etc. The description adds important behavioral details: login-free, public-data only, cross-checks inputs, flags conflicts, requires APIFY_TOKEN, and consumes credits. No contradictions with annotations.
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 two sentences but packs significant information. While the second sentence is long, it efficiently conveys critical behavioral details without being verbose.
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?
No output schema, so the description must explain return values, which it does: name, domain, LinkedIn URL with confidence scores, overall score, match method. It also mentions JSON format and cost. Some minor gaps (e.g., no mention of error handling) but adequate given other richness.
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 100%, so baseline is 3. The description adds value by clarifying domain is the strongest canonical key, explaining linkedin_url accepts URL or bare slug, and describing skipCache behavior. This justifies a score above baseline.
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 resolves any combination of company name, domain, or LinkedIn URL into a canonical identity. It specifies inputs and outputs, leaving no ambiguity about its function.
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 explains when to use the tool (to resolve company identity from partial info) and mentions cross-checking and conflict detection. However, no explicit when-not-to-use guidance is given, but this is acceptable given no sibling tools exist.
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.
1 tool update
v1.0.0- First observed
resolve_company_identity
TDQS
A4.7/5.0
Disambiguation5/5
Only one tool exists, so there is no ambiguity in tool selection.
Naming Consistency5/5
The single tool name follows a clear verb_noun pattern (resolve_company_identity), consistent with best practices.
Tool Count5/5
One tool is perfectly scoped for a resolver that combines all inputs (name, domain, LinkedIn) into a single operation, avoiding unnecessary complexity.
Completeness5/5
The tool covers the full lifecycle of identity resolution: it accepts any combination of inputs, cross-checks them, returns confidence scores, and flags conflicts. There are no missing features for its stated purpose.
Maintenance
ActivityMaintained
ResponsivenessNo issues
Resources
Unclaimed servers have limited discoverability.
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