Field-by-field comparison

Compare the evidence before choosing an MCP.

Each signal stays separate, missing facts remain visible, and the column order follows your selection.

MCPs in this comparison

Uncheck an MCP to remove it. Comparing two or three keeps each signal in its own row.

Open canonical comparison link

Comparable alternatives

Shown only when capability or industry context overlaps.

Side-by-side MCP evidence comparison
Evidence fieldAsana MCPMCP100 RecommendedHugging Face MCPMCP100 Recommended
Task fit
Best for
  • Read and manage Asana workspaces, tasks, projects, portfolios, and reports.
  • Teams already using Asana that can test with non-sensitive data and least privilege.
  • Searching models, datasets, Spaces, papers, and Hugging Face documentation
  • Exploring Hub metadata from an MCP-compatible assistant
  • Running explicitly selected Hugging Face tools in controlled research workflows
Not ideal for
  • Users who need an MCP100-recommended recommendation today
  • Unattended production workflows before permissions and failure behavior are reviewed
  • Highly regulated data or confidential prompts sent to unreviewed community Spaces
  • Unattended Jobs, sandbox execution, or repository writes
Avoid when
  • The connected data or account cannot be safely exposed to the selected AI client
  • You cannot verify least-privilege access and review write-capable tool calls
  • Dynamic community Space discovery is enabled without tool-by-tool review
  • The Hugging Face token grants more repository or compute access than needed
  • Model, dataset, or Space content can directly trigger privileged tools
Provenance
Provenance details

First-party MCP

Asana · Publisher source ↗

First-party MCP

Hugging Face · Publisher source ↗

Maintenance
Maintenance details

Repo: Not documented

Package: Not documented

Repo: Sep 23, 2026

Package: Not documented

Popularity evidence
GitHub starsNot documented

296

GitHub stars · checked 2026-09-24T04:14:16.295000Z

Client coverage
Client coverage details
Claude Code✓ Streamable HTTP
Claude Desktop✓ Streamable HTTP
Codex✓ Streamable HTTP
VS Code✓ Streamable HTTP
Cursor✓ Streamable HTTP
OpenCode✓ Streamable HTTP
Claude Code✓ Streamable HTTP
Claude Desktop✓ Streamable HTTP
Codex✓ Streamable HTTP
VS Code✓ Streamable HTTP
Cursor✓ Streamable HTTP
OpenCode✓ Streamable HTTP
Client coverage noteLocal stdio runs on your computer. Streamable HTTP connects to a remotely hosted MCP server.
Access and data
AuthenticationUse Asana's documented MCP-token and connected-user authorization path. The token is specific to the MCP server, while available actions remain bounded by the connected user's existing Asana permissions; OAuth-scope and revocation details should not be inferred beyond the reviewed publisher documentation.The hosted server supports the current documented Hugging Face OAuth or bearer-token paths. Tool advertisement or discovery configuration is not a server-side authorization boundary for a caller that already knows a tool name; actual authority remains bounded by the authenticated Hugging Face account and token permissions.
CostNo MCP100 cost conclusion is published yet. The publisher's account plan, API usage, infrastructure, or per-action charges may apply.Hub search and the open-source server have no separately documented MCP fee. Hugging Face Jobs, sandboxes, inference, storage, hardware-backed Spaces, and the AI client can incur normal account charges.
PermissionsThe MCP token is specific to Asana's MCP server, but it does not provide per-tool OAuth scopes. Available MCP tools operate within the connected user's existing Asana permissions; any workspace restriction belongs to the documented app or distribution configuration rather than being intrinsic to every issued MCP token.Base tools search and navigate Hugging Face Hub resources and documentation. Optional tools can create and write repositories, manage sandbox files, run or schedule Jobs, and invoke selected or dynamically discovered community Spaces, bounded by the authenticated Hugging Face token and configured tool set.
Data handlingPublisher-accessible account or application data may be returned to the MCP client and its selected model. Retention and subprocess behavior require candidate-specific review.Queries and results pass through Hugging Face's hosted MCP service. Community Space calls send their arguments to the selected Space and return its outputs to the client. Jobs and sandboxes can process files and compute inputs on Hugging Face infrastructure.
Limitations
Tradeoffs
  • First-party provenance and a clear task improve confidence, but do not replace an independent permissions and usability review.
  • Client transport compatibility does not prove that authentication, scopes, failure handling, and every tool behave safely.
  • The hosted endpoint makes Hub discovery easy, while optional community Gradio tools have independent publishers, behavior, and data handling.
  • Built-in Jobs, sandboxes, and repository contribution tools add useful compute and write capabilities but materially increase cost and impact.
  • Tool selection is configurable in Hugging Face MCP settings and should remain minimal.
Risk contextUse a test workspace or non-production account, least privilege, read-only controls where available, explicit approval for mutations, and credential revocation after evaluation.Begin with search-only tools and a least-privilege token. Disable dynamic Spaces, repository writes, Jobs, and sandboxes unless separately approved; review every community Space as third-party code and never send secrets or regulated data. This documentary review does not claim MCP100 execution testing.
Evidence date
Editorial review2026-07-242026-07-24
Candidate evidence2026-09-24T04:25:05.554391Z2026-09-24T04:25:05.554391Z

Popularity, maintenance, fit, permissions, and client support are independent evidence fields.

Read the evidence method →