Logflare MCP Code Mode
Enables agents to interact with the Logflare API by writing JavaScript functions that run in a Vercel Sandbox, using three tools: search, execute_read, and execute_write.
README
Logflare MCP Code Mode
A Model Context Protocol server that gives an agent three tools instead of dozens for driving the Logflare Management API. The agent writes a short JavaScript function that runs inside a Vercel Sandbox and calls the API through a thin api.request() client — looping, filtering, and chaining calls in one round-trip.
This applies Cloudflare's Code Mode pattern to Logflare:
- LLMs write API-calling code more reliably than long chains of individual tool calls.
- Intermediate data stays in the sandbox, out of the model's context.
- The agent discovers endpoints with
search(the OpenAPI spec), then calls them withexecute_read/execute_write.
This is a local stdio server — one process per MCP client, communicating over stdin/stdout, the same way you'd run any locally-installed MCP server (npx, or a command/args entry in your client config). Logflare has no OAuth 2.1 authorization server, so there's no per-request bearer token to broker the way a remote streamable-HTTP server would; the API key is just an environment variable for the whole process.
Quickstart
Prerequisites: Node.js ≥ 20.12, pnpm (corepack enable provides it), a Vercel account with Sandbox access, and a Logflare API key (Logflare account → API Keys).
pnpm install
pnpm build
Copy .env.example to .env and fill in VERCEL_TOKEN/VERCEL_TEAM_ID/VERCEL_PROJECT_ID and LOGFLARE_API_KEY — the local .env is only for pnpm dev/pnpm test; a real MCP client sets these as env entries in its own config instead (see below).
Connect an MCP client — this repo ships a .mcp.json:
{
"mcpServers": {
"logflare-code-mode": {
"command": "node",
"args": ["dist/index.js"],
"env": {
"LOGFLARE_API_KEY": "${LOGFLARE_API_KEY}",
"VERCEL_TOKEN": "${VERCEL_TOKEN}",
"VERCEL_TEAM_ID": "${VERCEL_TEAM_ID}",
"VERCEL_PROJECT_ID": "${VERCEL_PROJECT_ID}"
}
}
}
}
Once published, the same thing works via npx:
{
"mcpServers": {
"logflare-code-mode": {
"command": "npx",
"args": ["-y", "logflare-mcp-code-mode", "--read-only"],
"env": { "LOGFLARE_API_KEY": "...", "VERCEL_TOKEN": "...", "VERCEL_TEAM_ID": "...", "VERCEL_PROJECT_ID": "..." }
}
}
}
Run node dist/index.js --help for the full option/env-var reference.
Try it — call execute_read with an async arrow function:
async () => {
const res = await api.request({ method: 'GET', path: '/api/sources' })
return { status: res.status, count: res.data.length }
}
You get back e.g. { "status": 200, "count": 3 } — produced by code the agent wrote, run in a sandbox that can reach only the Logflare API.
The three tools
Each takes a single code string — an async arrow function that returns a value. console.log output comes back separately as stdout.
| Tool | What it does | In scope | Annotation |
|---|---|---|---|
search |
Query the Logflare OpenAPI spec (all $refs resolved) to find endpoints and their shapes. Network-isolated. |
spec |
read-only |
execute_read |
Call the Logflare API via api.request() — GET only. |
api |
read-only |
execute_write |
Call the Logflare API via api.request() — any method (POST/PUT/PATCH/DELETE). |
api |
destructive |
The workflow is: search to find an endpoint's path and request shape, then execute_read/execute_write to call it. The client is api.request({ method, path, query?, body?, contentType?, rawBody? }), which returns { status, ok, data }.
Run with --read-only to lock the whole server instance into read-only mode: search's spec only contains GET operations (so the agent can't even discover write endpoints), and execute_write isn't registered at all — it's absent from tools/list, and calling it anyway fails as an unknown tool. This is a startup flag (a property of the process), not a per-call one.
How it works
MCP client ──stdio (newline-delimited JSON-RPC)──▶ McpServer (2 or 3 tools)
│
┌───────────────────────────────┼───────────────────────────────┐
search execute_read execute_write
│ └───────────────┬───────────────┘
▼ ▼
fresh Vercel Sandbox fresh Vercel Sandbox
(networkPolicy: deny-all) (networkPolicy: allow logflare.app;
embeds `spec` LOGFLARE_API_KEY injected;
`api.request()` client)
- Transport —
StdioServerTransportfrom the MCP SDK: reads newline-delimited JSON-RPC from stdin, writes responses to stdout. stdout is exclusively the protocol channel — all logging goes to stderr (seesrc/log.ts). - Per call — a fresh Vercel Sandbox (
@vercel/sandbox), stopped infinally. No SDK is installed inside it, so there's nothing to amortize with a warm pool. searchsandbox —networkPolicy: "deny-all"; the slimmed OpenAPI spec (fetched fromLOGFLARE_OPENAPI_URL, defaulthttps://api.logflare.app/api/openapi) is embedded asspecfor the agent's read-only code to query. In--read-onlymode the spec is filtered to GET-only operations first.execute_*sandbox —networkPolicy: { allow: ["logflare.app"] }(the Management API's actual host);LOGFLARE_API_KEYis injected as an env var and sent asAuthorization: Bearer <key>byapi.request(). The read/write split and the credential/backend/team block are enforced by the providedapi.request()client:execute_readthrows on any non-GET before the fetch, and both tools refuse the paths inBLOCKED_PATH. This is a guard against accidental or confused agent behavior, not a hard sandbox capability restriction — agent code is plain Node with the globalfetchin scope, so it could in principle calllogflare.appdirectly and bypass both checks. Safety for a genuinely adversarial caller rests on the fact that they're using their own API key against an API they already have full access to, not on this guard.- Runner — agent code is written to a file and run with
node(nevereval); output returns via a result file. The code sits on its own lines in the invoke scaffold, so a trailing// commentcan't swallow the closing tokens. - Vercel credentials — explicit
VERCEL_TOKEN/VERCEL_TEAM_ID/VERCEL_PROJECT_IDare required (this process never runs on Vercel itself, so there's no ambient OIDC fallback in practice).
The source is small and layered by responsibility:
| File | Responsibility |
|---|---|
src/index.ts |
Entry point: parses --read-only/--help, loads .env, starts the stdio transport. |
src/server.ts |
The MCP surface: the two or three tools (depending on read-only mode), response formatting, the untrusted-data boundary. |
src/sandbox.ts |
Sandbox plumbing: runner construction, the two sandbox shapes, the injected api.request() client, output caps, the credential/backend guard. |
src/spec.ts |
Fetches, $ref-resolves, slims, caches, and (optionally) GET-filters the Logflare OpenAPI spec for search. |
src/log.ts |
pino logger (stderr only) + credential redaction. |
Security notes
Agent code is untrusted but runs inside a Vercel Sandbox microVM and authenticates with the caller's own Logflare key — so by design it can use that key against the Logflare API. Beyond the sandbox isolation, per-tool egress, and read/write split above:
- Untrusted-data boundary — all execute output (
result/stdout/stderr) may embed API content (log events, source metadata), so it's wrapped in a<untrusted-data-{uuid}>…</>envelope (error path too), with the model told not to follow instructions inside. Truncation happens before wrapping, so a size cap can't sever the closing tag. - Credential/backend/team block — paths containing
access-tokens(mints/lists/reveals full API tokens),backends(backendconfigcan embed DB passwords, service-account keys, and other connection secrets), orteams(the Team response embeds the fullUserobject, whoseapi_keyis the account's master Logflare API key) are refused, matched anywhere in the path (e.g./api/sources/{id}/backends/{id}). The path is first normalized to a fixpoint (percent-decode, re-resolve dot-segments, collapse slashes, lowercase), so encoded spellings (/%61ccess-tokens,//backends,/x%2F..%2Fbackends,/TEAMS) still match; it only over-blocks. This guard covers the intendedapi.request()path; see the caveat above about rawfetchin the sandbox. - Known gap — some
Sourcefields (slack_hook_url,webhook_notification_url) embed secrets the caller configured (their own Slack webhook, etc.) rather than Logflare-minted credentials. These are not blocked, the same way reading your own resource's other fields isn't blocked — but a prompt-injected agent could still surface them into its context viaexecute_read/execute_write, same as any other resource field. - Output caps — bounded inside the sandbox before reaching the server: runner result cap (400k chars),
head -creads (500k file / 20k per stream), final server truncate (100k result / 10k logs). - Token location — like Vercel Sandbox generally, there's no outbound-fetch proxy hook to keep the token out of the isolate, so
LOGFLARE_API_KEYlives in the sandbox env (agent code can read it). Safety rests on isolation + egress — the key only reacheslogflare.app— not token secrecy. - Single-tenant by design — there's no bearer-token auth gate because there's nothing to gate: this process is spawned per-caller by an MCP client, already scoped to whoever's shell/config started it, unlike the remote-HTTP shape this was originally built as (see
git log— the streamable-HTTP version is preserved in history if a remote deployment is ever needed again).
Configuration
| Env var / flag | Default | Meaning |
|---|---|---|
LOGFLARE_API_KEY (env) |
— | Required. The Logflare API key used for every execute_read/execute_write call. |
VERCEL_TOKEN (env) |
— | Required. Vercel API token for sandbox creation. |
VERCEL_TEAM_ID (env) |
— | Required. Vercel team ID for sandbox creation. |
VERCEL_PROJECT_ID (env) |
— | Required. Vercel project ID for sandbox creation. |
--read-only (CLI flag) |
off | Hide execute_write; restrict search's spec to GET operations. |
LOGFLARE_SANDBOX_RUNTIME (env) |
node24 |
Vercel Sandbox runtime the sandboxes boot from |
LOGFLARE_EXEC_TIMEOUT_MS (env) |
120000 |
Per-call budget for the agent's code |
LOGFLARE_OPENAPI_URL (env) |
https://api.logflare.app/api/openapi |
Override the OpenAPI spec URL the search tool loads |
LOG_LEVEL (env) |
info |
trace/debug/info/warn/error/fatal/silent |
LOG_FORMAT (env) |
json |
json (structured) or pretty (dev) |
Observability
Structured logs via pino to stderr only — stdout is reserved for JSON-RPC. One line per tool invocation/completion (tool name, code length, duration, calledEndpoints count for execute calls) plus a startup line noting read-only mode. Use LOG_FORMAT=pretty locally. Credentials are never logged (see redactToken in src/log.ts).
Testing
pnpm test
test/server.test.ts— the MCP surface via an in-memory client/server pair (tool list, annotations, error shapes,readOnly: truehidingexecute_write). No key.test/stdio.test.ts— end-to-end over the real stdio transport: spawns the built binary as a child process and speaks newline-delimited JSON-RPC over its stdin/stdout, exactly like a real MCP client. Covers--help,--read-onlyover the wire, and that stdout only ever carries JSON-RPC (logs land on stderr). No key. Requirespnpm buildfirst.test/sandbox.test.ts— offline unit tests for the credential/backend/team guard (path normalization + block regex). No key, no sandbox.test/spec.test.ts— offline unit tests for the read-only spec filter (GET-only operations, paths with no GET dropped entirely). No key, no network.test/live.test.ts— end-to-end against real Logflare + Vercel Sandbox: spec search,execute_readGET plus non-GET rejection, trailing-comment tolerance, error/SyntaxErrorsurfacing inside the boundary, the credential/backend/team guard (including encoded bypasses), anexecute_writecreate+delete round-trip, egress denial, and the stdout size cap. Runs only whenLOGFLARE_API_KEYand theVERCEL_*credentials are set.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.