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    • A
      license
      A
      quality
      A
      maintenance
      GTM signal intelligence suite for AI agents. Six tools: hiring signals, tech stack detection, company-to-LinkedIn resolution, ICP scoring, job board scanning, and a combined signals aggregator. Built for outbound sales workflows.
      21
      140 npm
      3
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Battle cards and scored practice calls against an AI buyer, for any company vs any competitor. From Battlecard: score every call, rehearse every gap.
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI agents to discover companies showing buying signals, enrich them with public homepage data, score them against custom rules, and draft review-ready briefs.
      1
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Enables B2B prospecting from natural language: detect buying signals, score leads against ICP, enrich decision-makers, and draft personalized outreach messages via Claude.
      2
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      A comprehensive B2B intelligence server providing over 48 tools for lead generation, company research, and sales automation via the Model Context Protocol. It enables AI-powered prospect discovery, enrichment, and competitive analysis through seamless integrations with Make.com, Claude Desktop, and Apify.
      2
      MIT

    TDQS

    A3.7/5.0

    Scored across 34 tools

    Disambiguation3/5

    Most tools have distinct purposes, but the set contains several overlapping assessment tools (get_icp_fit_score, batch_fit_score, classify_opportunity, get_disqualification_signals, get_evaluation_criteria, assess_company_readiness) and several memory/context tools (get_founder_context, get_revenue_memory, get_memory_history). The descriptions cross-reference alternatives well, but with 34 tools an agent can still plausibly misselect among the fit/scoring and memory families.

    Naming Consistency4/5

    Most names are snake_case and follow a verb_noun pattern (get_, list_, generate_, assess_, classify_). A few deviations exist, such as whoami, quote, signup_start/verify/status, and batch_fit_score, but they are grouped and readable rather than chaotic.

    Tool Count2/5

    The set is large at 34 tools, above the practical range for a single MCP surface even for a broad sales/revenue platform. Many tools are distinct, but the surface would benefit from consolidation or splitting into narrower modules.

    Completeness3/5

    Coverage is broad across ICP, prospecting, meeting prep, scoring, memory, portfolio, assets, billing, and fundraising. However, lifecycle management is thin: there is no clear listing or management of saved assets/account plans/prospects, and no update/delete operations for most saved objects beyond keeping memory history.

    Maintenance

    ActivityMaintained
    ResponsivenessSlow