Skip to main content
Glama
BorgDiven

research-workflow-studio

by BorgDiven

Related Servers

Alternatives to research-workflow-studio

No user-submitted related servers found.

    Related Servers

    • A
      license
      A
      quality
      A
      maintenance
      Enables AI agents and MCP clients to run research as an explicit scientific process: forming hypotheses, designing and executing sealed experiments, capturing observations and artifacts, linking claims to supporting evidence, and reviewing provenance. Exposes coordinated MCP services so users can inspect, reproduce, and audit every step from question to conclusion rather than trusting an opaque final answer.
      46
      484 PyPI
      21
      Apache 2.0
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI-assisted scientific research workflow management through MCP, including project creation, ideation, experiment execution, and artifact handling, with integration for ChatGPT, Codex, and Claude Code.
      Apache 2.0
    • A
      license
      C
      quality
      A
      maintenance
      An append-only research operations framework and read-only MCP that tracks research plans, approvals, observations, claims, failures, revisions, and contributions with source-grounded evidence, providing search, evidence fetch, and audit capabilities without direct ledger writes.
      30
      40 PyPI
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables humans and AI agents to share canonical, versioned knowledge through MCP, with auditable provenance, review workflows, and reconciliation.
      1
      Apache 2.0

    TDQS

    C2.8/5.0

    Scored across 28 tools

    Disambiguation4/5

    Most tools target distinct research-workflow objects and lifecycle actions, with parallel propose/apply/dismiss and save/register families that remain mostly separable through their descriptions. The main ambiguity is between read-oriented tools such as record_get, get_context, result_query, and code_find, and between save/register/import semantics, but these are usually clarified by the tool text.

    Naming Consistency4/5

    All tools share the rws_ prefix and generally follow a resource_action pattern such as project_list, experiment_save, and artifact_register. A few deviate, notably rws_get_context and the longer rws_report_create_revision, but the overall convention is predictable.

    Tool Count3/5

    The 28-tool surface is heavy, but it reflects a broad research lifecycle with projects, experiments, code, runs, artifacts, datasets, protocols, reports, and review workflows. Most tools appear to earn their place, yet the count is large enough to increase selection burden and is above the rubric's typical well-scoped range.

    Completeness4/5

    The surface covers a wide CRUD-like and audited lifecycle: project creation/listing, context and record reads, experiment/code/run/artifact/dataset/protocol registration, result querying, analysis and report creation, usage tracking, and amendment/overview review. Minor gaps exist around explicit listing or browsing of some entity types, but read tools and context retrieval provide workarounds.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues