Skip to main content
Glama
machinegrade

machinegrade-validate

by machinegrade

machinegrade validate

Validate AI-generated artifacts against a contract before you act on them:

  • json_schema — validate artifact against a JSON Schema (all errors collected).

  • openapi_response — validate a response body against the response schema for a given path + method + status in an OpenAPI spec.

  • sql — check a SQL string for syntax errors in a given dialect.

Every check returns a verdict, not an error: {valid, errors, latency_ms}, HTTP 200 whether the artifact is valid or not. Only genuinely wrong requests (bad key, unsupported type, malformed body, over your limit) get typed HTTP errors.

Live at https://api.machinegrade.dev — free tier, self-service key, try it in 30 seconds (first example below). Built on Hono; the same codebase runs on Cloudflare Workers (production) and plain Node (local dev), and is MIT-licensed if you'd rather self-host.

Why

Agents that generate JSON, API responses, or SQL need a fast, cheap, machine-checkable pass/fail before they ship the result — cheaper than a full LLM-as-judge call, and deterministic.

Related MCP server: Fixzi MCP server

Run it locally

npm install
npm run dev
# machinegrade validate listening on http://localhost:8787

3 runnable examples

1. curl

# Get an API key (live service — works as-is)
curl -s -X POST https://api.machinegrade.dev/keys \
  -H 'content-type: application/json' \
  -d '{"email": "you@example.com"}'
# => {"key":"sk_..."}

# Validate a JSON artifact against a JSON Schema
curl -s -X POST https://api.machinegrade.dev/v1/validate \
  -H 'content-type: application/json' \
  -H 'X-Api-Key: sk_...' \
  -d '{
    "type": "json_schema",
    "artifact": {"name": "Ada", "age": 30},
    "contract": {
      "schema": {
        "type": "object",
        "required": ["name", "age"],
        "properties": {"name": {"type": "string"}, "age": {"type": "number"}}
      }
    }
  }'
# => {"valid":true,"errors":[],"latency_ms":1}

2. Python (requests)

import requests

base = "http://localhost:8787"

key = requests.post(f"{base}/keys", json={"email": "you@example.com"}).json()["key"]

resp = requests.post(
    f"{base}/v1/validate",
    headers={"X-Api-Key": key},
    json={
        "type": "sql",
        "artifact": "SELECT id, name FROM users WHERE id = 1",
        "contract": {"dialect": "mysql"},
    },
)
print(resp.status_code, resp.headers.get("X-Calls-Remaining"), resp.json())

3. MCP config snippet

mcp/server.ts exposes a single tool, validate, that forwards to POST /v1/validate. Point an MCP-compatible client at it:

{
  "mcpServers": {
    "machinegrade-validate": {
      "command": "npx",
      "args": ["tsx", "mcp/server.ts"],
      "cwd": "/path/to/validate",
      "env": {
        "SANDBOX_URL": "http://localhost:8787",
        "SANDBOX_API_KEY": "sk_..."
      }
    }
  }
}

Connect remotely

The production service also exposes an MCP endpoint directly — no local process, no npm install — via streamable HTTP at:

POST https://api.machinegrade.dev/mcp

It's the same single validate tool as the stdio adapter above. initialize and tools/list work without a key (discovery is anonymous); tools/call requires X-Api-Key (issue one via POST /keys, same as the REST API — the free tier and limits are shared).

With Claude Code:

claude mcp add --transport http validate https://api.machinegrade.dev/mcp --header "X-Api-Key: sk_..."

The stdio adapter via npm (@machinegrade/validate, see above) remains available for local/offline use or clients without HTTP transport support.

Claude Desktop: one-click install

Download the latest .mcpb bundle from Releases and double-click it. Claude Desktop asks for an API key during install and stores it in the OS keychain (macOS Keychain, Windows Credential Manager) rather than in a config file — nothing lands in claude_desktop_config.json.

Issue a free key (500 calls/month) first:

curl -s -X POST https://api.machinegrade.dev/keys \
  -H 'content-type: application/json' \
  -d '{"email": "you@example.com"}'

The bundle is a thin stdio client over the hosted API: ~34 KB, zero bundled dependencies. If you self-host, point the extension's API base URL setting at your own deployment and the tool talks to that instead.

API

See public/openapi.yaml for the full contract, or /v1/manifest for a machine-readable summary (types, limits, pricing, error codes) once the service is running. /llms.txt is a short pointer for LLM agents.

Endpoint

In

Out

POST /keys

{email}

{key}

POST /v1/validate

header X-Api-Key, body {type, artifact, contract?}

verdict, header X-Calls-Remaining

GET /v1/manifest

capability manifest

GET /stats

header X-Admin-Token

funnel: keys_issued, active_callers, repeat_callers_7d, limit_hits, paid_requests

POST /v1/paid-request

header X-Api-Key

records interest in paid access

GET /openapi.yaml, GET /llms.txt

static docs

POST /mcp

MCP streamable HTTP, header X-Api-Key for tools/call

see "Connect remotely" above

Pricing

  • Free tier: 500 calls/month per key, 60 calls/minute rate limit.

  • Paid tier: EUR 0.002/call beyond the free tier — opens soon. Request paid access via POST /v1/paid-request (requires X-Api-Key); you'll be notified when it's live.

Errors

Every error is typed JSON — {code, message, hint, docs_url} — never a free-form string:

Code

HTTP status

When

INVALID_KEY

401

X-Api-Key missing or unknown

LIMIT_EXCEEDED

402

Free-tier monthly limit (500 calls) exceeded

UNSUPPORTED_TYPE

400

type is not json_schema, openapi_response, or sql

MALFORMED_INPUT

400

Request body doesn't match the documented shape

RATE_LIMITED

429

More than 60 calls/minute for a key

A verdict ({valid, errors, latency_ms}) is never an error — an invalid artifact is a normal, expected outcome and returns HTTP 200.

Sending the artifact

artifact must be a JSON value, not a JSON-encoded string:

{"type": "json_schema", "artifact": {"name": "Ada"}, "contract": {"schema": {"type": "object"}}}   // correct
{"type": "json_schema", "artifact": "{\"name\": \"Ada\"}", "contract": {"schema": {"type": "object"}}}  // wrong

For type: "sql" the artifact is a string — the statement itself.

Because MCP callers stringify values often enough (and did so through Claude Desktop until the tool schema declared artifact's types), the service tolerates the wrong form narrowly: if artifact is a string, the type is json_schema or openapi_response, and the contract's top-level schema declares types that exclude string, the string is JSON-decoded before validation and the verdict carries "decoded_from_string": true. The field is additive; {valid, errors, latency_ms} is unchanged.

It deliberately does not decode otherwise, because a string artifact is often legitimate:

Sent

Contract schema

Result

"42"

{"type": "number"}

decoded to 42, valid: true, decoded_from_string: true

"42"

{"type": "string"}

left alone, valid: true

"{\"a\":1}"

{"type": "string"}

left alone, valid: true

"{\"a\":1}"

{"required": ["a"]}

left alone — no top-level type, intent unknown

Storage

src/storage.ts defines a Storage interface with two implementations:

  • MemoryStorage — full in-memory implementation, used for npm run dev and the test suite.

  • D1Storage — real Cloudflare D1 binding, backed by schema.sql (keys, events tables). Used in production; the Workers entry point in src/index.ts builds it from the DB binding on first request.

Apply schema.sql to a new D1 database with:

wrangler d1 execute machinegrade-validate-db --file=schema.sql          # local
wrangler d1 execute machinegrade-validate-db --file=schema.sql --remote # production

Testing

npm test          # vitest run, in-process via app.request(), MemoryStorage
npm run typecheck # tsc --noEmit

Tests cover: key issuance, happy + fail cases for each validator, typed 401/400/402/429 errors, the metering limits (both injectable in tests so they don't require looping hundreds of real requests), and /stats funnel counts.

Deploy

The service runs on Cloudflare Workers (Hono + D1 + Workers Static Assets). To self-host on a fresh Cloudflare account:

wrangler d1 create machinegrade-validate-db   # copy the returned database_id into wrangler.toml
wrangler d1 execute machinegrade-validate-db --file=schema.sql --remote
wrangler secret put ADMIN_TOKEN
wrangler deploy

Then bind a custom domain (e.g. api.machinegrade.dev) to the Worker via the Cloudflare dashboard or wrangler. CI can deploy on push to main once CLOUDFLARE_API_TOKEN and CLOUDFLARE_ACCOUNT_ID repo secrets are set and the deploy job in .github/workflows/ci.yml is uncommented.

Two things worth knowing about the Workers port:

  • GET /openapi.yaml and GET /llms.txt are served by the ASSETS binding ([assets] in wrangler.toml, pointing at public/) — Cloudflare serves them directly, without invoking the Worker. The routes in src/index.ts are a fallback for local Node dev/tests, where there's no ASSETS binding.

  • The json_schema and openapi_response validators use @cfworker/json-schema, not ajv: ajv compiles schemas via new Function(...), which the Workers runtime disallows, and schemas here arrive dynamically per request (from the caller), so they can't be precompiled at build time either.

Status

Early stage, honestly so: this service is live and free-tier usage is real, and we're measuring whether it earns a paid tier. What you can rely on:

  • The API contract (/v1/validate request/response shapes, typed error codes, verdict semantics) is stable — breaking changes only with a versioned path (/v2/...), never silently.

  • The free tier (500 calls/month) stays.

  • If we ever sunset the service, keys keep working for 90 days after the announcement, and the validators are open source in this repo — you can self-host the same behavior.

Feedback and integration stories are the most valuable thing you can give us right now: open an issue or use POST /v1/paid-request if you need more than the free tier.

Available Tools

1 tool
validateBInspect

Validate an artifact against a contract (json_schema | openapi_response | sql) via the machinegrade validate API.

ParametersJSON Schema
NameRequiredDescriptionDefault
typeYesWhich validator to run.
artifactYesThe artifact to validate, as a JSON value — not a JSON-encoded string. json_schema/openapi_response: the object/array/value itself. sql: the SQL statement as a string.
contractNoValidator-specific contract. json_schema: { schema }. openapi_response: { spec, path, method, status }. sql: { dialect }.

TDQS

B3.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It only states the action ('validate') without disclosing any behavioral traits such as side effects, rate limits, authentication needs, or return format. The mention of 'via the machinegrade validate API' adds little transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that efficiently communicates the core purpose. Every part contributes meaning; no unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given three parameters, no output schema, and no annotations, the description is incomplete. It does not explain the return value, error handling, or provide usage examples. A validation tool typically benefits from clarifying output (e.g., boolean, error details) to guide the agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema descriptions cover 100% of parameters, so baseline is 3. The description adds no extra meaning beyond the schema. It reiterates the enum values but does not provide additional context or examples.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool validates an artifact against a contract, listing the three supported validator types (json_schema, openapi_response, sql). It uses a specific verb 'validate' and resource 'artifact', making the purpose unambiguous even without a title.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool vs alternatives, but no siblings exist to confuse. The description implicitly covers usage by listing the three validation types. However, it does not explain when each type is appropriate or provide any exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool updatev0.1.2
    • Changedvalidate3 fields changed
      • changedInput schema / properties / artifact / description
        Previous value: -"The artifact to validate (object for json_schema/openapi_response, SQL string for sql)."New value: +"The artifact to validate, as a JSON value — not a JSON-encoded string. json_schema/openapi_response: the object/array/value itself. sql: the SQL statement as a string."
      • addedInput schema / properties / artifact / type
        Added value: +[
        +  "object",
        +  "array",
        +  "string",
        +  "number",
        +  "boolean",
        +  "null"
        +]
      • addedInput schema / properties / contract / type
        Added value: +"object"
  2. 1 tool updatev0.1.1
    • First observedvalidate

TDQS

A3.7/5.0
Disambiguation5/5

With only one tool, there is no possibility of confusion or overlap. The single tool's purpose is clearly and uniquely defined.

Naming Consistency5/5

The single tool name 'validate' is concise, follows a verb-action pattern expected for a validation server, and is fully consistent within the set.

Tool Count3/5

Having only one tool feels thin for a server that could potentially offer multiple validation operations or contract management. While the tool covers the core validation task, the scope seems limited.

Completeness5/5

The tool comprehensively addresses the server's stated purpose of validating artifacts against contracts, supporting three common validation types (json_schema, openapi_response, sql) through a single API.

Maintenance

ActivitySlowing
ResponsivenessSyncing

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Related MCP Servers

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/machinegrade/validate'

If you have feedback or need assistance with the MCP directory API, please join our Discord server