MCP MeasureMCP Measure

index observed

MDataAccess

Paid business, people, company, lead, and web intelligence for autonomous AI agents.

Should I use this

Quality & Safety

A
Description quality
87%
Schema completeness
77%
Naming quality
94%
Poisoning risk
100%
Permission match
100%
Protocol compliance
100%

Findings (3)

  • LOWTool 'enrich_company' description lacks action verbin enrich_company
  • LOWTool 'enrich_person' description lacks action verbin enrich_person
  • LOWTool 'find_decision_maker' description lacks action verbin find_decision_maker

Based on automated analysis of tool definitions and protocol compliance.

Context Cost

~654Tokens (tool definitions)
~807 BTypical response size
Moderate attention impact (0.51% of 128k context)

This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.

Install

One-Click Install

Add this to your `claude_desktop_config.json` file:

{
  "mcpServers": {
    "mdataaccess": {
      "url": "https://api.mdataaccess.com/mcp/"
    }
  }
}

Remote endpoints

https://api.mdataaccess.com/mcp/streamable-http

What it can do

Tool inventory

Tools (7)

🟢 Read-only🟡 Write🔴 Delete⚪ Unknown
🟢read_webpage(url)

Prepare a paid request to read clean text from a known public webpage URL.

Input Schema

{
  "type": "object",
  "properties": {
    "url": {
      "title": "Url",
      "type": "string"
    }
  },
  "required": [
    "url"
  ],
  "title": "read_webpageArguments"
}

Output Schema

{
  "type": "object",
  "additionalProperties": {
    "type": "string"
  },
  "title": "read_webpageDictOutput"
}
⚪enrich_company(name, website)

Prepare a paid company enrichment request when a company name or domain is known.

Input Schema

{
  "type": "object",
  "properties": {
    "name": {
      "default": "",
      "title": "Name",
      "type": "string"
    },
    "website": {
      "default": "",
      "title": "Website",
      "type": "string"
    }
  },
  "title": "enrich_companyArguments"
}

Output Schema

{
  "type": "object",
  "additionalProperties": {
    "type": "string"
  },
  "title": "enrich_companyDictOutput"
}
⚪enrich_person(email, linkedin_url, name, company)

Prepare a paid person enrichment request when an email, LinkedIn URL, or name is known.

Input Schema

{
  "type": "object",
  "properties": {
    "email": {
      "default": "",
      "title": "Email",
      "type": "string"
    },
    "linkedin_url": {
      "default": "",
      "title": "Linkedin Url",
      "type": "string"
    },
    "name": {
      "default": "",
      "title": "Name",
      "type": "string"
    },
    "company": {
      "default": "",
      "title": "Company",
      "type": "string"
    }
  },
  "title": "enrich_personArguments"
}

Output Schema

{
  "type": "object",
  "additionalProperties": {
    "type": "string"
  },
  "title": "enrich_personDictOutput"
}
🟢search_people(sql)

Prepare a paid PDL SQL person-search request. Exactly one profile is returned by the API.

Input Schema

{
  "type": "object",
  "properties": {
    "sql": {
      "title": "Sql",
      "type": "string"
    }
  },
  "required": [
    "sql"
  ],
  "title": "search_peopleArguments"
}

Output Schema

{
  "type": "object",
  "additionalProperties": {
    "type": "string"
  },
  "title": "search_peopleDictOutput"
}
🟢search_companies(sql)

Prepare a paid PDL SQL company-search request. Exactly one company is returned by the API.

Input Schema

{
  "type": "object",
  "properties": {
    "sql": {
      "title": "Sql",
      "type": "string"
    }
  },
  "required": [
    "sql"
  ],
  "title": "search_companiesArguments"
}

Output Schema

{
  "type": "object",
  "additionalProperties": {
    "type": "string"
  },
  "title": "search_companiesDictOutput"
}
🟢find_decision_maker(company, function)

Prepare a paid request for one senior decision-maker at a known company and function.

Input Schema

{
  "type": "object",
  "properties": {
    "company": {
      "title": "Company",
      "type": "string"
    },
    "function": {
      "title": "Function",
      "type": "string"
    }
  },
  "required": [
    "company",
    "function"
  ],
  "title": "find_decision_makerArguments"
}

Output Schema

{
  "type": "object",
  "additionalProperties": {
    "type": "string"
  },
  "title": "find_decision_makerDictOutput"
}
🟢find_lead(company, function)

Prepare a paid request for one compact sales-ready lead at a known company and function.

Input Schema

{
  "type": "object",
  "properties": {
    "company": {
      "title": "Company",
      "type": "string"
    },
    "function": {
      "title": "Function",
      "type": "string"
    }
  },
  "required": [
    "company",
    "function"
  ],
  "title": "find_leadArguments"
}

Output Schema

{
  "type": "object",
  "additionalProperties": {
    "type": "string"
  },
  "title": "find_leadDictOutput"
}

Recommended Prompts

search_research
Search for information about [topic] using MDataAccess
Expected tools: search_people
find_specific
Find [specific item] using MDataAccess
Expected tools: search_people
retrieve_data
Get details about [item] from MDataAccess
Expected tools: read_webpage
fetch_info
Fetch [information type] using MDataAccess
Expected tools: read_webpage
research_workflow
Search for [topic], then get detailed information about the top results using MDataAccess
Expected tools: search_peopleread_webpage

Community

Rate this Server

Evidence

Recent observations

verifiedversion not recorded7 tools
verifiedversion not recorded7 tools