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OrcaRouter provider support for Sentry MCP

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Valutazione

Difficoltà
5/5
Tempo stimato
Più di una settimana
Idoneità per principianti
30/100
Tipo di issue
Funzionalità
Chiarezza
Da chiarire
Stato di attività
Attiva
Stack tecnologico
typescript
Ambito
ai, backend-api-design

Direzione di ricerca

Inizia da packages/mcp-core/src/internal/agents/provider-factory.ts e dalla registrazione di AgentProviderType descritta nell’issue. Confronta la voce OrcaRouter proposta con la configurazione OpenRouter esistente, inclusa la relativa variabile API-key e la base URL opzionale. Il lavoro è completato quando i maintainer concordano sulla struttura del provider e sulla direzione dell’implementazione conforme ai requisiti di disclosure prima che venga scritto il codice.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

OrcaRouter provider support for Sentry MCP

Hi Sentry MCP maintainers,

sentry-mcp wraps Sentry's error tracking and performance monitoring into 19 MCP tools built for human-in-the-loop coding agents like Cursor and Claude Code. I especially respect how you've handled the genuinely hard part: the natural-language tools (search_events, search_issues, search_issue_events) depend on an LLM provider, so provider selection is explicit and conflict-proof via EMBEDDED_AGENT_PROVIDER and per-provider API-key variables, backed by a small provider factory.

That design is also why adding one more option is useful rather than redundant: teams that standardize Sentry debugging across many developers, or people who self-host the stdio transport, get the AI search tools working against whichever model endpoint fits their stack. A dedicated OrcaRouter provider would slot alongside OpenRouter as a new explicit choice, without changing any existing configuration.

Proposal

I'd like to propose OrcaRouter as an optional embedded-agent provider for sentry-mcp. It would only add a new choice for the AI-powered search tools; it would not replace or alter OpenAI, Azure OpenAI, Anthropic, or OpenRouter support.

A few capabilities seem most relevant to your users:

  • Many models through one endpoint — chat, reasoning, image, and video models from multiple upstream providers behind a single endpoint, so users get model variety for query translation without juggling several keys.
  • Automatic routing and provider failover — if an upstream provider or model is down or rate-limited, requests route to an alternative, which matters when your search tools depend on a provider being reachable.
  • Usage tracking and budgets — per-key usage records and spend caps fit the cost awareness visible in the repo (tool-definition token-cost tracking, the token-cost CI workflow).

Expected integration point

OrcaRouter exposes an OpenAI-compatible API and uses standard API-key authentication. Embedded-agent providers are registered through AgentProviderType and the factory in packages/mcp-core/src/internal/agents/provider-factory.ts, each with its own key variable (e.g. OPENROUTER_API_KEY). My assumption — open to your preference — is an orcarouter entry alongside openrouter with an ORCAROUTER_API_KEY variable, or documenting it as an OpenAI-compatible base URL. I haven't written any code; this issue is to agree on the shape first.

Ecosystem

OrcaRouter is already shipped by open-source tools, including developer-facing projects such as goose and OpenCode; more examples are at https://www.orcarouter.ai/built-with.

Disclosure

Transparently: OrcaRouter runs an optional open-source partner program through which approved OSS projects can receive a 5% revenue share from OrcaRouter usage attributed to their integration. Participation is not a precondition for support, and I'll follow this repository's disclosure and governance requirements either way.

I'm an engineer on the OrcaRouter team. Would an optional OrcaRouter provider be of interest to sentry-mcp? I'm happy to refine the design and submit an implementation PR once you've approved the direction.

Lingua principale
TypeScript
Stelle
938
Fork
166
Merge medio
1g 5h
PR unite (30g)
172

Preparare l'ambiente

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Come iniziare

  1. Leggi tutta la issue e poi la guida ai contributi del progetto.
  2. Commenta sulla issue per dire che te ne occupi tu — evita che due persone facciano lo stesso lavoro.
  3. Fai un fork del repository e lavora su un branch.
  4. Apri una pull request che faccia riferimento al numero della issue.

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