# GLiNER Extraction MCP (`irreplaceable_chevrotain/gliner-extraction-mcp`) Actor Zero-shot entity extraction and PII redaction for AI agents — pass any label set ("invoice number", "dosage", "deadline"), get spans back. GLiNER model + deterministic patterns for emails, SSNs, cards. Priced per 10k characters, no subscription. - **URL**: https://apify.com/irreplaceable\_chevrotain/gliner-extraction-mcp.md - **Developed by:** [Gad](https://apify.com/irreplaceable_chevrotain) (community) - **Categories:** MCP servers, AI, Agents - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 entity extraction (per 10k chars)s This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## GLiNER Extraction MCP **Extract anything. Redact everything.** A hosted MCP server for zero-shot entity extraction and PII redaction, powered by [GLiNER](https://github.com/urchade/GLiNER) — no training, no label taxonomy, no per-field setup. Point any MCP-capable agent (Claude, ChatGPT, Cursor, custom agents) at this server and it gets structured extraction and compliance-grade redaction as native tool calls. ### Tools #### `extract_entities` Pass text plus **any label set you can phrase in words**: ```json { "text": "Per our call, ACME will deliver 40 units by March 3rd. Contact Sarah Lin (s.lin@acme.io).", "labels": ["organization", "quantity", "deadline", "person", "email"] } ```` Returns each match with exact character spans, label, and confidence. Invoice fields, contract clauses, medication dosages, job titles, product SKUs — if you can name it, it extracts it. That's the zero-shot difference: classic NER models know ~18 fixed types; GLiNER matches your labels at inference time. #### `redact_pii` Two detection layers, one merged result: - **Model layer** — contextual PII a regex can't see: names, addresses, organizations, dates of birth - **Pattern layer** — structured PII a model can miss: emails, phone numbers, SSNs, credit cards (Luhn-validated), IP addresses Three redaction styles: `label` → `[PERSON]`, `mask` → `█████`, `remove` → deleted. Pass custom labels to redact domain-specific fields (e.g. `["patient name", "diagnosis"]`) instead of the default PII set. **Privacy:** text is processed in memory only — never stored, never logged. ### Usage (MCP) ```json { "mcpServers": { "gliner-extraction": { "url": "https:///mcp" } } } ``` ### Pricing Pay per event, metered by document size (1 unit = 10,000 characters, minimum 1): | Event | Tool | Price per unit | |---|---|---| | `entity-extraction` | `extract_entities` | $0.005 | | `pii-redaction` | `redact_pii` | $0.01 | A 3-page contract ≈ 1 unit. Cheap enough to run on every document in a pipeline — that's the point. ### Batch mode Run the Actor directly with `{ "mode": "redact", "texts": [...], "labels": [...] }` to process a corpus into the dataset. ### Model Default: `urchade/gliner_small-v2.1` (Apache 2.0), baked into the image — no cold-start download. The build arg `GLINER_MODEL` swaps in any GLiNER checkpoint (e.g. `urchade/gliner_multi-v2.1` for multilingual). ### Limits - Max 200,000 characters per call, 25 labels per request. - Detection is probabilistic: for regulated use, treat redaction output as a strong first pass with human review, not a guarantee. # Actor input Schema ## `mode` (type: `string`): extract = pull entities out; redact = return text with PII removed ## `texts` (type: `array`): Documents to process (one dataset item per text) ## `labels` (type: `array`): Any label set, no training needed — e.g. \["person", "invoice number", "deadline"]. For redact mode, leave empty to use the built-in PII set. ## `threshold` (type: `string`): Detection threshold 0-1 (default 0.45) ## `style` (type: `string`): Only used in redact mode ## Actor input object example ```json { "mode": "extract", "texts": [ "Contact Jane Doe at jane@example.com or +1 (555) 010-4477 about invoice #2231." ], "labels": [ "person", "email", "phone number" ], "threshold": "0.45", "style": "label" } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "texts": [ "Contact Jane Doe at jane@example.com or +1 (555) 010-4477 about invoice #2231." ], "labels": [ "person", "email", "phone number" ] }; // Run the Actor and wait for it to finish const run = await client.actor("irreplaceable_chevrotain/gliner-extraction-mcp").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "texts": ["Contact Jane Doe at jane@example.com or +1 (555) 010-4477 about invoice #2231."], "labels": [ "person", "email", "phone number", ], } # Run the Actor and wait for it to finish run = client.actor("irreplaceable_chevrotain/gliner-extraction-mcp").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "texts": [ "Contact Jane Doe at jane@example.com or +1 (555) 010-4477 about invoice #2231." ], "labels": [ "person", "email", "phone number" ] }' | apify call irreplaceable_chevrotain/gliner-extraction-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=irreplaceable_chevrotain/gliner-extraction-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "GLiNER Extraction MCP", "description": "Zero-shot entity extraction and PII redaction for AI agents — pass any label set (\"invoice number\", \"dosage\", \"deadline\"), get spans back. GLiNER model + deterministic patterns for emails, SSNs, cards. Priced per 10k characters, no subscription.", "version": "0.1", "x-build-id": "SPVtehnPKkGCsKhWn" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/irreplaceable_chevrotain~gliner-extraction-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-irreplaceable_chevrotain-gliner-extraction-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/irreplaceable_chevrotain~gliner-extraction-mcp/runs": { "post": { "operationId": "runs-sync-irreplaceable_chevrotain-gliner-extraction-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/irreplaceable_chevrotain~gliner-extraction-mcp/run-sync": { "post": { "operationId": "run-sync-irreplaceable_chevrotain-gliner-extraction-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "mode": { "title": "Mode", "enum": [ "extract", "redact" ], "type": "string", "description": "extract = pull entities out; redact = return text with PII removed", "default": "extract" }, "texts": { "title": "Texts", "type": "array", "description": "Documents to process (one dataset item per text)", "items": { "type": "string" } }, "labels": { "title": "Entity labels", "type": "array", "description": "Any label set, no training needed — e.g. [\"person\", \"invoice number\", \"deadline\"]. For redact mode, leave empty to use the built-in PII set.", "items": { "type": "string" } }, "threshold": { "title": "Confidence threshold", "type": "string", "description": "Detection threshold 0-1 (default 0.45)", "default": "0.45" }, "style": { "title": "Redaction style", "enum": [ "label", "mask", "remove" ], "type": "string", "description": "Only used in redact mode", "default": "label" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```