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
verifiedversion not recorded7 tools
verifiedversion not recorded7 tools
verifiedversion not recorded7 tools
verifiedversion not recorded7 tools