Skip to main content
Glama
ahmed-khalil-hafsi

P2predict-mcp

by ahmed-khalil-hafsi
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Related Servers

Alternatives to P2predict-mcp

No user-submitted related servers found.

    Related Servers

    • SocratiCode

      giancarloerra
      A
      license
      A
      quality
      A
      maintenance
      Enterprise-grade (40m+ lines) codebase intelligence in a zero-setup, private and local MCP: managed indexing, hybrid semantic search, polyglot code dependency graphs, and DB/API/infra knowledge. Benchmark: 61% less tokens, 84% fewer calls, 37x faster than standard AI grep.
      26
      2,001 npm
      3,354
      AGPL 3.0
    • OptimToken MCP by OptimNow

      OptimToken MCPofficial

      OptimNow
      A
      license
      Not graded
      quality
      A
      maintenance
      Enables AI assistants to fetch live, dated prices for LLM models and cloud compute instances across providers, compare and recommend models, and estimate monthly costs based on workload-specific token shapes and constraints.
      MIT

    • SolidWorks-MCP

      hjbaard
      A
      license
      B
      quality
      A
      maintenance
      Let AI agents build, measure and verify parametric parts and assemblies in your own running SolidWorks (Windows, COM). 46 tools, every step measured.
      86
      674 PyPI
      15
      MIT

    • whichapiapi

      uriruhman
      A
      license
      Not graded
      quality
      B
      maintenance
      Enables agents to rank model variants and hunt down API offers for a specific task, estimate real billed cost, audit n8n/Make automations for overpay, missing fallbacks and deprecated models, and plan or run evaluation and canary suites on the user's own examples. It also supports price comparison, custom reseller channels with measured per-call cost, and non-LLM capabilities such as web search and speech-to-text.
      AGPL 3.0
    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A4.3/5.0

    Scored across 12 tools

    Disambiguation4/5

    The prediction tools (predict, predict_batch, predict_from_csv, predict_interval, explain, what_if) have overlapping surface areas but each has a clearly distinct input mode or output focus. The one genuinely close pair is get_model_quality and generate_report; their descriptions do distinguish structured JSON from PDF generation, but an agent could still hesitate.

    Naming Consistency4/5

    Most tools follow a clear snake_case verb or verb_noun pattern, and the predict_* family is consistently named. Minor deviations like 'explain' and 'what_if' break the pattern slightly but remain readable and predictable.

    Tool Count5/5

    Twelve tools is well within the ideal range and each tool covers a distinct stage of the model workflow: discovery, training, prediction variants, explanation, quality assessment, and reporting. No tool feels redundant enough to remove.

    Completeness4/5

    The set covers model discovery, training, single/batch prediction, intervals, what-if analysis, explanation, quality review, and PDF reporting—very complete for a prediction-focused server. The main gaps are missing model deletion/update operations and a direct model-comparison tool, but these are not core to the stated purpose.

    Maintenance

    ActivityActive
    ResponsivenessNo issues

    Resources