# Awesome-List Parser (`heliotrope_partridge/awesome-list-parser`) Actor Parse any GitHub awesome-\* list (awesome-mcp-servers, awesome-rust, awesome-llm, etc.) into structured JSON entries with category, name, URL, and description. Optionally enrich with star count + freshness from GitHub API. One source of truth for any curated directory. - **URL**: https://apify.com/heliotrope\_partridge/awesome-list-parser.md - **Developed by:** [Roberto Espino](https://apify.com/heliotrope_partridge) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.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/platform/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 ## Awesome-List Parser **One source of truth for any GitHub awesome-* list.** Hundreds of awesome-* lists exist on GitHub — `awesome-mcp-servers`, `awesome-rust`, `awesome-llm`, `awesome-react`, etc. They're great for humans but a pain for tools: every list is a markdown README that needs custom parsing. This actor turns any awesome list into structured JSON in one call. Optionally enriches each GitHub-hosted entry with live metadata (stars, last push, language, license, archived state) so you can rank by signal instead of trusting the curator's order. ### What it does - Fetches the README of any awesome-* GitHub repo via the public API - Parses standard awesome-list markdown (headers as categories, bulleted `[name](url) - description` entries) - Optionally enriches each `github.com/...` entry with live repo metadata - Returns structured JSON you can immediately consume in your agent, dashboard, or analysis pipeline ### When to use it - You're building an AI agent and need to ingest a curated directory (MCP servers, Rust crates, LLM tools, etc.) - You're auditing an awesome list for stale or archived entries (run with `enrich: true` + check `pushed_at` and `archived` fields) - You're building a meta-directory that aggregates multiple awesome lists ### Input | Field | Type | Default | Notes | |-------|------|---------|-------| | `repo` | string | `punkpeye/awesome-mcp-servers` | Awesome-list repo in `owner/name` form. | | `enrich` | boolean | `false` | Fetch GitHub metadata for each `github.com/...` entry. Slower but useful for ranking. | | `limit` | int | `200` | Cap parsed entries (1-1000). Pair with `enrich=true` to control rate limit usage. | | `githubToken` | secret | `""` | Optional GitHub PAT — required if enriching a list with 60+ github entries (lifts rate limit 60→5,000 req/hr). | ### Output Each dataset record: ```json { "category": "Databases / SQL", "name": "postgres-mcp", "url": "https://github.com/example/postgres-mcp", "description": "Read-only Postgres MCP server with schema introspection.", "github_repo": "example/postgres-mcp", "stars": 234, "forks": 12, "pushed_at": "2026-04-15T...", "language": "TypeScript", "license": "MIT", "archived": false } ```` The `github_*` fields appear only when `enrich=true` and the entry's URL points at a GitHub repo. ### Example runs **Just parse a list, fast:** ```json { "repo": "punkpeye/awesome-mcp-servers" } ``` **Audit a list — find dead/archived entries:** ```json { "repo": "rust-unofficial/awesome-rust", "enrich": true, "limit": 500, "githubToken": "ghp_..." } ``` **Aggregate two lists in one workflow:** run this actor twice, merge the datasets in your downstream step. ### Limitations - Only standard awesome-list markdown shape (headers + bullets with link + description). Lists with custom rendering, tables, or HTML-only entries may parse incompletely. - Code fences are skipped (good — most lists put example commands in fences, not entries). - Non-GitHub URLs are returned as-is without enrichment (npm, gitlab, custom domains). - Lexical only. No semantic merge across categories that mean the same thing. ### Companion actors - `mcp-server-discovery` — search GitHub for MCP-tagged repos (no awesome-list dependency). ### License Apache-2.0. # Actor input Schema ## `repo` (type: `string`): Awesome-list repo in owner/name form. Examples: 'punkpeye/awesome-mcp-servers', 'rust-unofficial/awesome-rust', 'Hannibal046/Awesome-LLM'. ## `enrich` (type: `boolean`): For each parsed entry that links to a github.com repo, fetch stars/last-push/license. Slower but more useful. Costs 1 API call per entry. ## `limit` (type: `integer`): Cap parsed entries (1-1000). Useful with enrich=true to avoid rate limits. ## `githubToken` (type: `string`): Optional GitHub personal access token. Required for enrich=true on lists with 60+ github.com entries (raises rate limit 60->5000 req/hr). ## Actor input object example ```json { "repo": "punkpeye/awesome-mcp-servers", "enrich": false, "limit": 200 } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("heliotrope_partridge/awesome-list-parser").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 = {} # Run the Actor and wait for it to finish run = client.actor("heliotrope_partridge/awesome-list-parser").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 '{}' | apify call heliotrope_partridge/awesome-list-parser --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=heliotrope_partridge/awesome-list-parser", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Awesome-List Parser", "description": "Parse any GitHub awesome-* list (awesome-mcp-servers, awesome-rust, awesome-llm, etc.) into structured JSON entries with category, name, URL, and description. Optionally enrich with star count + freshness from GitHub API. One source of truth for any curated directory.", "version": "0.1", "x-build-id": "TQuzQ0E3LeYSDADmL" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/heliotrope_partridge~awesome-list-parser/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-heliotrope_partridge-awesome-list-parser", "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/heliotrope_partridge~awesome-list-parser/runs": { "post": { "operationId": "runs-sync-heliotrope_partridge-awesome-list-parser", "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/heliotrope_partridge~awesome-list-parser/run-sync": { "post": { "operationId": "run-sync-heliotrope_partridge-awesome-list-parser", "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", "required": [ "repo" ], "properties": { "repo": { "title": "GitHub repo (owner/name)", "type": "string", "description": "Awesome-list repo in owner/name form. Examples: 'punkpeye/awesome-mcp-servers', 'rust-unofficial/awesome-rust', 'Hannibal046/Awesome-LLM'.", "default": "punkpeye/awesome-mcp-servers" }, "enrich": { "title": "Enrich with GitHub metadata", "type": "boolean", "description": "For each parsed entry that links to a github.com repo, fetch stars/last-push/license. Slower but more useful. Costs 1 API call per entry.", "default": false }, "limit": { "title": "Max entries", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Cap parsed entries (1-1000). Useful with enrich=true to avoid rate limits.", "default": 200 }, "githubToken": { "title": "GitHub token (optional)", "type": "string", "description": "Optional GitHub personal access token. Required for enrich=true on lists with 60+ github.com entries (raises rate limit 60->5000 req/hr)." } } }, "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 } } } } } } } } } } ```