_ registry / mcp streamable-http · checked 2h ago

icp-intelligence-mcp

https://icp-intelligence.gtmhelix.com

Registry code: 5fe677bb8012641e

api record

ICP Intelligence: 9 ideal customer profile tools by Helix GTM Consulting

from a public catalogue that lists it, not from the operator

endpoint
https://icp-intelligence.gtmhelix.com/mcp
protocol
streamable-http ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing · is it yours? claim it
karma
0 · newcomer
_ is it live, free and safe measured by this hub
Is icp-intelligence-mcp live?
Yes — it answered the hub's last check (checked 2h ago). It answered 100% of checks over the last 30 days.
Is icp-intelligence-mcp free to use?
Yes — the hub reached it with no key and no payment.
What tools does icp-intelligence-mcp have?
9 tools: icp_scoring_model, buyer_group_analyzer, tam_sam_som_calculator, lookalike_signal_generator, icp_gap_analysis, icp_evolution_tracker, account_prioritization, icp_deep_dive, ….
Is icp-intelligence-mcp safe to connect?
The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.
reachable
live
uptime, 30 days
100%

90 days 100%· all time 100%

latency
935ms

last good check

priced tools
0

of 9 tools

_ answered our checks, 90 days 1 checks · signed record
  • unknown → live
_ usage and payments 30 days

Calls placed through this hub's router, from its own receipts. Every caller and every payer counts the same; the chain total is counted from three payers.

accounts
0

through this hub

calls served
0

successful

paid through this hub
0 USDC

what callers paid

_ what it can do 9 tools
3 open 6 never probed 3 of 9 classified

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

  • account_prioritization open 2h ago

    Rank and prioritize accounts by a weighted score of fit, intent, relationship and timing; each account shows the points its timing earned and the factor that added the most points

    mcp-tool

    {
  "type": "object",
  "properties": {
    "company": {
      "type": "string",
      "description": "Optional: your company or product name, so the answer can name it"
    },
    "accounts": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "name": {
            "type": "string"
          },
          "timing": {
            "type": "string",
            "description": "now, soon, later or unknown (now 100 points, soon 70, later 40; unknown, left out or any other word 50)"
          },
          "fit_score": {
            "type": "number",
            "maximum": 100,
            "minimum": 0,
            "description": "0 to 100. Left out, 50 is used"
          },
          "relationship": {
            "type": "number",
            "maximum": 100,
            "minimum": 0,
            "description": "0 to 100, based on existing connections. Left out, 50 is used"
          },
          "intent_signals": {
            "type": "number",
            "maximum": 100,
            "minimum": 0,
            "description": "0 to 100 (0 scores zero). Left out, 50 is used"
          }
        }
      },
      "description": "List of accounts to prioritize. Each account: name, fit_score (0 to 100), intent_signals (0 to 100), relationship (0 to 100), timing (now, soon, later or unknown)"
    },
    "product_category": {
      "type": "string",
      "description": "Optional: what you sell (for example \"spend management software\"), used for sector notes. The arithmetic does not use it"
    },
    "prioritization_weights": {
      "type": "object",
      "properties": {
        "fit": {
          "type": "number"
        },
        "intent": {
          "type": "number"
        },
        "timing": {
          "type": "number"
        },
        "relationship": {
          "type": "number"
        }
      },
      "description": "Optional custom weights in percent for fit, intent, relationship and timing. A missing weight uses its default (40, 30, 15, 15); the tool does not check that the weights sum to 100"
    }
  }
}
    arguments 65 lines
  • icp_deep_dive open 2h ago

    Analyze customer data to detect ICP patterns (industry, size, deal size, sales cycle, tech stack, triggers, champion roles). Ties are named as ties, the budget is the ACV range seen in your customers, and sector notes are added when your inputs name one of the supported sectors

    mcp-tool

    {
  "type": "object",
  "properties": {
    "company": {
      "type": "string",
      "description": "Optional: your company or product name, so the answer can name it"
    },
    "customers": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "acv": {
            "type": "number",
            "minimum": 0
          },
          "name": {
            "type": "string"
          },
          "size": {
            "type": "string"
          },
          "industry": {
            "type": "string"
          },
          "tech_stack": {
            "type": "array",
            "items": {
              "type": "string"
            }
          },
          "buying_trigger": {
            "type": "string"
          },
          "champion_title": {
            "type": "string"
          },
          "sales_cycle_days": {
            "type": "number",
            "minimum": 0
          }
        }
      },
      "description": "List of customer objects with available attributes"
    },
    "business_model": {
      "enum": [
        "saas",
        "services",
        "connectivity",
        "transactions",
        "marketplace",
        "hardware_software",
        "investment"
      ],
      "type": "string",
      "description": "Optional: how you charge (software subscription, services, connectivity, per transaction, marketplace, hardware plus software, or investment management). Read from your other inputs when left out"
    },
    "product_category": {
      "type": "string",
      "description": "What type of product you sell"
    },
    "customer_descriptions": {
      "type": "string",
      "description": "Alternative: Describe your best customers in text format"
    }
  }
}
    arguments 68 lines
  • icp_interview_synthesizer open 2h ago

    Extract ICP patterns from customer interview notes: pain points, objections, buying triggers, value realized, champion roles and quotes kept word for word, with discovery questions and sector notes. Pasted notes are shown back (shortened) with a template to structure them; only structured notes are analyzed.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "company": {
      "type": "string",
      "description": "Optional: your company or product name, so the answer can name it"
    },
    "analysis_focus": {
      "type": "string",
      "description": "Accepted but not used yet: every run gives the complete analysis (pain_points, buying_journey, value_props, all)"
    },
    "interview_notes": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "role": {
            "type": "string"
          },
          "customer": {
            "type": "string"
          },
          "key_quotes": {
            "type": "array",
            "items": {
              "type": "string"
            }
          },
          "pain_points": {
            "type": "array",
            "items": {
              "type": "string"
            }
          },
          "value_realized": {
            "type": "array",
            "items": {
              "type": "string"
            }
          },
          "buying_triggers": {
            "type": "array",
            "items": {
              "type": "string"
            }
          }
        }
      },
      "description": "Structured interview notes"
    },
    "raw_transcripts": {
      "type": "string",
      "description": "Alternative: paste interview notes or transcripts. This tool does not analyze pasted text: it shows up to 500 characters back with a template to structure them as interview_notes, which it does analyze"
    },
    "product_category": {
      "type": "string",
      "description": "Optional: what you sell, used for sector notes"
    }
  }
}
    arguments 60 lines
  • icp_scoring_model unknown never probed

    Create a lead qualification scoring template: criteria with example point weights set by importance level, example points for each value you list, a scorecard and tier bands to adjust. Your success pattern is matched to your criteria; it does not set the weights. Sector notes are added when your inputs name one of the supported sectors

    mcp-tool

    {
  "type": "object",
  "properties": {
    "company": {
      "type": "string",
      "description": "Optional: your company or product name, so the answer can name it"
    },
    "product_category": {
      "type": "string",
      "description": "Optional: what you sell, as a product category, so the scoring notes use your sector"
    },
    "scoring_criteria": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "values": {
            "type": "array",
            "items": {
              "type": "string"
            }
          },
          "criterion": {
            "type": "string"
          },
          "importance": {
            "enum": [
              "critical",
              "important",
              "nice_to_have"
            ],
            "type": "string"
          }
        }
      },
      "description": "Criteria for scoring with importance levels"
    },
    "success_correlation": {
      "type": "string",
      "description": "What correlates with success? (e.g., \"deals with VP Sales champion close 2x faster\")"
    }
  }
}
    arguments 43 lines
  • buyer_group_analyzer unknown never probed

    Map the buyer group for a deal: the champion you name, your known stakeholders placed by their titles (economic buyer, technical evaluator, reviewers, users), each role's concern and message prompts, an influence map and discovery questions. Sector roles and questions are added when your inputs name one of the supported sectors

    mcp-tool

    {
  "type": "object",
  "required": [
    "product_category"
  ],
  "properties": {
    "company": {
      "type": "string",
      "description": "Optional: your company or product name, so the answer can name it"
    },
    "deal_size": {
      "type": "string",
      "description": "ACV range (e.g., \"$50K-100K\")"
    },
    "business_model": {
      "enum": [
        "saas",
        "services",
        "connectivity",
        "transactions",
        "marketplace",
        "hardware_software",
        "investment"
      ],
      "type": "string",
      "description": "Optional: how you charge (software subscription, services, connectivity, per transaction, marketplace, hardware plus software, or investment management). Read from your other inputs when left out"
    },
    "product_category": {
      "type": "string",
      "description": "What you sell"
    },
    "typical_champion": {
      "type": "string",
      "description": "Your typical champion role"
    },
    "known_stakeholders": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Roles you know are involved"
    },
    "target_company_size": {
      "type": "string",
      "description": "Company size (e.g., \"500-1000 employees\")"
    }
  }
}
    arguments 48 lines
  • tam_sam_som_calculator unknown never probed

    Calculate TAM/SAM/SOM bottom-up from your company count, ACV, ICP match rate and Year 1 share (calculation framework, not a data source). Sector notes are added when your inputs name one of the supported sectors

    mcp-tool

    {
  "type": "object",
  "required": [
    "total_potential_companies",
    "average_contract_value"
  ],
  "properties": {
    "company": {
      "type": "string",
      "description": "Optional: your company or product name, so the answer can name it"
    },
    "data_sources": {
      "type": "string",
      "description": "Where you got your numbers (for documentation)"
    },
    "segment_name": {
      "type": "string",
      "description": "Name of the market segment"
    },
    "icp_percentage": {
      "type": "number",
      "maximum": 100,
      "minimum": 0,
      "description": "Percentage of those companies that match your ICP, from 0 to 100. Left out, 30 is used and marked as an example"
    },
    "product_category": {
      "type": "string",
      "description": "Optional: what you sell (for example \"spend management software\"), used for sector notes. The arithmetic does not use it"
    },
    "average_contract_value": {
      "type": "number",
      "description": "Your average ACV in dollars",
      "exclusiveMinimum": 0
    },
    "total_potential_companies": {
      "type": "number",
      "minimum": 0,
      "description": "Estimated total companies that could buy (from LinkedIn, industry reports)"
    },
    "year1_market_share_target": {
      "type": "number",
      "maximum": 100,
      "minimum": 0,
      "description": "Realistic Year 1 market share percentage, from 0 to 100 (typically 1-5%). Left out, 3 is used and marked as an example"
    }
  }
}
    arguments 47 lines
  • lookalike_signal_generator unknown never probed

    Generate platform-specific targeting criteria and search queries from your firmographics, technographics, champion titles and buying triggers (generates criteria, not data). Each trigger gets its own signal; search keywords come from what you sell and the sector; platforms limits the sections

    mcp-tool

    {
  "type": "object",
  "required": [
    "champion_titles"
  ],
  "properties": {
    "company": {
      "type": "string",
      "description": "Optional: your company or product name, so the answer can name it"
    },
    "platforms": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Optional: the sections to include (linkedin, google_ads, 6sense, zoominfo). Left out, every section is included"
    },
    "buying_triggers": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Events that trigger buying"
    },
    "champion_titles": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Job titles of your champions"
    },
    "product_category": {
      "type": "string",
      "description": "Optional: what you sell (for example \"spend management software\"), used for search keywords and sector notes"
    },
    "icp_firmographics": {
      "type": "object",
      "properties": {
        "locations": {
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        "industries": {
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        "company_sizes": {
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        "funding_stages": {
          "type": "array",
          "items": {
            "type": "string"
          }
        }
      },
      "description": "Firmographic criteria"
    },
    "icp_technographics": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Technologies your ICP typically uses"
    }
  }
}
    arguments 74 lines
  • icp_gap_analysis unknown never probed

    Analyze gaps between your current customer base and your ideal ICP: what the ideal profile has that the current base lacks, metric gaps from your current and target figures, causes to check and actions that fit your business model. Sector notes are added when your inputs name one of the supported sectors

    mcp-tool

    {
  "type": "object",
  "required": [
    "current_customers",
    "ideal_icp"
  ],
  "properties": {
    "company": {
      "type": "string",
      "description": "Optional: your company or product name, so the answer can name it"
    },
    "ideal_icp": {
      "type": "string",
      "description": "Description of your ideal customer profile"
    },
    "business_model": {
      "enum": [
        "saas",
        "services",
        "connectivity",
        "transactions",
        "marketplace",
        "hardware_software",
        "investment"
      ],
      "type": "string",
      "description": "Optional: how you charge (software subscription, services, connectivity, per transaction, marketplace, hardware plus software, or investment management). Read from your other inputs when left out"
    },
    "target_metrics": {
      "type": "object",
      "properties": {
        "nps": {
          "type": "number"
        },
        "avg_acv": {
          "type": "number",
          "minimum": 0
        },
        "win_rate": {
          "type": "number"
        },
        "churn_rate": {
          "type": "number"
        },
        "avg_sales_cycle": {
          "type": "number",
          "minimum": 0
        }
      },
      "description": "Target performance metrics"
    },
    "current_metrics": {
      "type": "object",
      "properties": {
        "nps": {
          "type": "number"
        },
        "avg_acv": {
          "type": "number",
          "minimum": 0
        },
        "win_rate": {
          "type": "number"
        },
        "churn_rate": {
          "type": "number"
        },
        "avg_sales_cycle": {
          "type": "number",
          "minimum": 0
        }
      },
      "description": "Current performance metrics"
    },
    "product_category": {
      "type": "string",
      "description": "Optional: what you sell, used for sector notes"
    },
    "current_customers": {
      "type": "string",
      "description": "Description of your current customer base"
    }
  }
}
    arguments 84 lines
  • icp_evolution_tracker unknown never probed

    Review how your ICP should evolve: reads your recent wins, losses and market changes against your current ICP, states a candidate change for each (an addition to test, a disqualifier to test, an implication to check) and writes a draft of the updated ICP, with the roles your ICP does not name, the loss reasons to tag and what to pull from your CRM. It does not compute win rates; check each candidate against your CRM

    mcp-tool

    {
  "type": "object",
  "required": [
    "current_icp"
  ],
  "properties": {
    "company": {
      "type": "string",
      "description": "Optional: your company or product name, so the answer can name it"
    },
    "current_icp": {
      "type": "string",
      "description": "Your current ICP definition"
    },
    "recent_wins": {
      "type": "string",
      "description": "Description of recent successful customers"
    },
    "time_period": {
      "type": "string",
      "description": "Time period you are reviewing, in your own words (for example \"last quarter\")"
    },
    "recent_losses": {
      "type": "string",
      "description": "Description of recent lost deals"
    },
    "market_changes": {
      "type": "string",
      "description": "Recent market or competitive changes"
    },
    "product_category": {
      "type": "string",
      "description": "Optional: what you sell, used for sector notes"
    }
  }
}
    arguments 36 lines
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100%

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median latency
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settled without a human
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_ also on gtmhelix.com 5 entries

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