Skip to main content
Glama
playidea-lab

PCQ

by playidea-lab
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Related Servers

Alternatives to PCQ

No user-submitted related servers found.

    Related Servers

    • ml-party

      paul-krug
      A
      license
      Not graded
      quality
      A
      maintenance
      Enables agents to track ML experiments with a reproducible write contract, internal git snapshots, and a queryable knowledge graph, plus a live web UI for humans.
      Apache 2.0

    • MCP Bridgelement

      drdeeks
      A
      license
      Not graded
      quality
      B
      maintenance
      Enables provider-neutral policy enforcement, identity, persistent storage, and telemetry collection for reinforcement learning, evaluation, analytics, replay, and downstream dataset generation across standalone applications and agent systems.
      0
      MIT

    • vibescience-mcp

      AntonIXO
      A
      license
      A
      quality
      B
      maintenance
      A scientific experiment log MCP server for AI agents that stores predictions, causal claims, and verdicts, enabling queryable causal maps and calibration of intuition over diagnostics.
      17
      MIT

    • spark-mcp

      Kizo07
      A
      license
      Not graded
      quality
      B
      maintenance
      Enables quant research organizations to log hypotheses and outcomes, count trials for honest statistical verdicts, explore novel methods, run significance gates, and evolve reusable skills from validated trajectories, all backed by SQLite.
      MIT

    • Open Train MCP

      yidong72
      A
      license
      A
      quality
      C
      maintenance
      Enables AI agents to browse and filter experiment runs, retrieve metrics, logs, tables, and artifacts, plot comparisons as PNG images, and download full-fidelity training history for local analysis against a self-hosted Open Train server. It also provides diagnostic tooling that surfaces metric evidence and investigation leads without asserting causality.
      16
      MIT

    • Inficy

      artnames
      A
      license
      Not graded
      quality
      C
      maintenance
      Captures AI agent runs and turns them into tamper-evident execution records showing tool use, timing, failures, recoveries, and human interventions. Records can be inspected, exported, and verified offline.
      MIT
    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

    A3.7/5.0

    Scored across 14 tools

    Disambiguation5/5

    Each tool targets a distinct phase or action: plan application, scaffolding, execution, finalization/validation, and run analysis. The read-only analysis tools (describe_run, compare_runs, lineage_chain) and project inspection tools (resolve_project, inspect_project, validate_project) are separated by clear semantic boundaries. There are no pairs that appear to do the same thing.

    Naming Consistency4/5

    Most tools follow an imperative verb_noun pattern (run_experiment, validate_project, describe_run). A few deviate: agent_install and agent_status are noun-first, lineage_chain is a noun phrase, and apply_planset compresses plan_set into one word. These are minor and do not prevent readability.

    Tool Count5/5

    At 14 tools, the surface is within the well-scoped range and each tool covers a discrete step in the CQ lifecycle. The grouping of scaffold/configure/run/finalize/validate/analyze plus agent asset helpers feels intentional, with no obvious dead weight.

    Completeness4/5

    The main lifecycle is well covered: init, plan application, run, finalize, validate, and analyze. Minor gaps exist around explicit discovery/cleanup (e.g., listing or removing plans/runs), but agents can work around these using inspect_project, finalize_run artifacts, or output directories.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources