mindbridge-mcp

mindbridge-mcp is a Model Context Protocol (MCP) server/bridge that routes tool calls to multiple LLM providers (e.g., OpenAI, Anthropic, DeepSeek, Google, OpenRouter, Ollama) and exposes MCP tools such as getSecondOpinion plus listing tools like listProviders and listReasoningModels. It also supports an OpenAI-compatible API layer according to the README.

Evaluated Mar 30, 2026 (21d ago)
Repo ↗ Ai Ml mcp llm llm-routing agent-tools orchestration typescript openai-compatible multi-provider
⚙ Agent Friendliness
48
/ 100
Can an agent use this?
🔒 Security
39
/ 100
Is it safe for agents?
⚡ Reliability
25
/ 100
Does it work consistently?

Score Breakdown

⚙ Agent Friendliness

MCP Quality
55
Documentation
70
Error Messages
0
Auth Simplicity
80
Rate Limits
10

🔒 Security

TLS Enforcement
40
Auth Strength
35
Scope Granularity
10
Dep. Hygiene
55
Secret Handling
60

The README indicates API keys are provided via environment variables. It does not describe server-side authn/authz, fine-grained scopes, or audit logging for the MCP server. It also does not document TLS requirements or rate-limit controls for the MCP interface. Since it proxies requests to third-party LLM providers, prompts may be sent to upstream services; review configuration and threat model accordingly.

⚡ Reliability

Uptime/SLA
0
Version Stability
40
Breaking Changes
40
Error Recovery
20
AF Security Reliability

Best When

You want an MCP tool surface (and optionally OpenAI-compatible compatibility) to unify multiple LLM providers and run multi-model workflows.

Avoid When

You cannot afford to expose/relay user prompts to multiple upstream provider APIs or you need clear, contract-level guarantees around security controls and reliability/error semantics.

Use Cases

  • Multi-LLM orchestration for agents and workflow engines
  • Requesting multiple model responses for comparison (second opinions)
  • Routing questions to “reasoning” optimized models
  • Building a single integration surface across heterogeneous LLM providers
  • Using OpenAI-compatible endpoints as a compatibility layer for other apps/providers

Not For

  • Production environments that require strong, documented operational controls (authn/authz, rate limits, audit logs) without reviewing the code
  • Use cases needing guaranteed data residency/compliance commitments
  • Scenarios where you must strictly avoid proxying/relaying prompts to third-party model providers without explicit review

Interface

REST API
Yes
GraphQL
No
gRPC
No
MCP Server
Yes
SDK
No
Webhooks
No

Authentication

Methods: Environment variables for provider API keys (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.) Optional API key for OpenAI-compatible providers (OPENAI_COMPATIBLE_API_KEY)
OAuth: No Scopes: No

Authentication appears to be provider-key based configured via env vars or MCP config. The README does not mention per-user auth, scopes, or authorization controls on the MCP server itself.

Pricing

Free tier: No
Requires CC: No

No pricing information is provided in the README content.

Agent Metadata

Pagination
none
Idempotent
False
Retry Guidance
Not documented

Known Gotchas

  • Behavior depends on upstream provider responses/limits; routing failures may surface as provider-specific errors.
  • Since the project is a proxy/router, misconfiguration of provider keys/model names in env/MCP config can lead to tool failures.
  • No documented guidance here on retries/timeouts/idempotency for multi-call orchestration tools like getSecondOpinion.

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Scores are editorial opinions as of 2026-03-30.

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