hirepulse-tools
Advanced tools
HirePulse gateway client for hiring signals, tech stack and company data, plus Apify tools
pip install 'hirepulse-tools[langchain]'
# or: pip install 'hirepulse-tools[crewai]'
from hirepulse_tools.frameworks import get_langchain_tools, get_crewai_tools
tools = get_langchain_tools() # eight StructuredTools for a LangChain agent
# tools = get_crewai_tools() # eight BaseTools for a CrewAI agent
Both factories list tools without making network calls. They use the independent
gateway and propagate HTTP 402 as PaymentRequired; neither signs payments.
Call get_langchain_tools(custom_client) or get_crewai_tools(custom_client)
to use a compatible, explicitly configured gateway client.
Eight tools at https://gethirepulse.xyz, separate from the Apify tools below. No account or Apify token is needed. Five successful calls per UTC day per IP are shared across tools. Empty lookups are not charged. Prices start at $0.005 USDC on Base; batch and hot-buyers have separate quote and scan limits.
from hirepulse_tools import GatewayClient, PaymentRequired
try:
print(GatewayClient().hiring_intent("linear.app"))
except PaymentRequired:
print("Free allowance exhausted. No payment was attempted.")
Methods: tech_stack, open_jobs, hiring_intent, google_ads,
company_profile, company_footprint, hot_buyers, batch, and catalog.
lookup(tool, **body) accepts the documented gateway request body.
The client does not sign payments or read wallet keys. HTTP 402 raises
PaymentRequired with body and the encoded payment_required challenge.
Other HTTP errors raise GatewayError; requests are not retried.
Runnable x402 examples show optional payment setup. I maintain HirePulse.
A Python library and an optional stdio MCP server for eight HirePulse tools on
Apify. Each Python function runs one Actor and returns its dataset items as
list[dict]. Tools run on the Apify Store, pay per use, with no subscription.
Apify already hosts MCP for these tools. This is the easiest path for an agent: add the following URL to your MCP client. No local Python installation is needed for this option.
Paste this hosted URL into your MCP client's remote-server configuration:
https://mcp.apify.com?tools=hirepulse/aliexpress-search-scraper,hirepulse/google-ads-transparency-scraper,hirepulse/company-intent-radar,hirepulse/google-jobs-scraper,hirepulse/ats-job-pulse,hirepulse/keyword-search-volume,hirepulse/domain-seo-overview,hirepulse/youtube-transcript-scraper
For one tool, use, for example, Google Ads hosted MCP. Use your client's Apify authentication setup. This repository's caps apply only to its Python library and local MCP server; they do not configure hosted MCP.
For Claude Desktop, Apify also provides this command:
npx -y @apify/actors-mcp-server --tools hirepulse/google-ads-transparency-scraper
Set APIFY_TOKEN in the server process environment through your launch setup.
Use the desired hirepulse/<tool> in --tools. Keep the token out of configuration
files and command arguments.
Python 3.10 or later is required. Install the published library:
python -m pip install "hirepulse-tools==0.1.1"
For the local MCP server, install the optional MCP dependencies:
python -m pip install "hirepulse-tools[mcp]==0.1.1"
python -m hirepulse_tools.mcp_server
Or launch the pinned MCP package with uvx:
uvx --from "hirepulse-tools[mcp]==0.1.1" hirepulse-mcp
The equivalent console command is hirepulse-mcp. It uses stdio.
Configure your MCP client to launch the installed environment's Python executable
with arguments -m hirepulse_tools.mcp_server, inheriting APIFY_TOKEN from its
process environment. Actor logs are not forwarded to stdio.
Provide APIFY_TOKEN in the environment before calling any tool. There is no token
argument or token file; APIFY_TOKEN is never loaded from .env. .env.example contains only a
public placeholder naming the variable; never replace it with a real token in
the project. Listing MCP tools requires no token and does not start an Actor.
These are executable input examples, not measured output examples. Running them
starts Apify Actors and can incur charges. Each script in examples/ uses a
5-row result limit and a USD 0.10 spending cap. Tests never run these scripts.
| Function | Tool page | Returns |
|---|---|---|
search_aliexpress | AliExpress Search Scraper | Product search records |
google_ads_by_domain | Google Ads Transparency Scraper | Ads, or one summary per domain |
company_intent | Company Intent Radar | One signal report per company |
google_jobs | Google Jobs Scraper | Job search records |
ats_jobs | ATS Job Scraper | Jobs and/or company summaries |
keyword_volume | Keyword Search Volume | Keyword metrics and ideas |
domain_seo | Domain SEO Overview | Domain overview, keyword and competitor records |
youtube_transcripts | YouTube Transcript Scraper | Transcript records, optionally with timestamps |
from hirepulse_tools import search_aliexpress
items = search_aliexpress(["wireless earbuds"], result_limit=5, max_total_charge_usd=0.10)
from hirepulse_tools import google_ads_by_domain
items = google_ads_by_domain(["nike.com"], mode="summary", result_limit=5, max_total_charge_usd=0.10)
from hirepulse_tools import company_intent
items = company_intent(["stripe.com"], result_limit=5, max_total_charge_usd=0.10)
from hirepulse_tools import google_jobs
items = google_jobs(["data engineer"], country="US", result_limit=5, max_total_charge_usd=0.10)
from hirepulse_tools import ats_jobs
items = ats_jobs(["stripe.com"], output_mode="summary_only", result_limit=5, max_total_charge_usd=0.10)
from hirepulse_tools import keyword_volume
items = keyword_volume(["running shoes"], result_limit=5, max_total_charge_usd=0.10)
from hirepulse_tools import domain_seo
items = domain_seo(["notion.so"], top_keywords=0, competitors=0, result_limit=5, max_total_charge_usd=0.10)
from hirepulse_tools import youtube_transcripts
items = youtube_transcripts(["https://www.youtube.com/watch?v=jNQXAC9IVRw"], result_limit=5, max_total_charge_usd=0.10)
See all arguments, defaults and allowed values. Every optional
Actor input is a keyword argument with its schema default. Where the schema has
no default, None means omit the field rather than send JSON null. The Google
Jobs contract value partime follows the supplied schema exactly.
Every function also accepts these wrapper options, outside run_input:
| Option | Default | Behavior |
|---|---|---|
result_limit | 60 | Integer 1–1000; passed as .call(max_items=...) and .list_items(limit=...); at most this many rows are returned |
max_total_charge_usd | 1.0 | Finite positive USD amount, converted to Decimal for .call() |
The installed apify-client 3.3.0 signature was inspected to confirm both options.
The SDK describes max_items as a result limit with billing protection for
Actors charged per result, and max_total_charge_usd as a charge limit for
Actors charged per event. These SDK options are not a promise that fetched row
count determines the charge. Keep input batches small too. Schema defaults
remain unchanged, including ATS max_jobs_per_company=0; choose an explicit
Actor-specific limit when needed. See SDK verification.
Dataset rows are returned unchanged, in dataset order. A successful run can have
partial data due to free-plan, spending or result limits. Empty datasets return
[]. Ordinary Python calls do not fetch key-value-store summaries. Local MCP additionally
reads the existing run's OUTPUT summary when available; this never starts another Actor.
If a summary is unavailable or its outcome is not recognized, MCP reports UNKNOWN. Use the tool page and Apify run details when completeness
matters.
MissingTokenError explains how to provide the environment variable.
ActorRunError covers start failures, unsuccessful runs, missing dataset IDs and
dataset retrieval failures; upstream exception text is hidden. Invalid local
arguments raise ValueError before starting a run.
Library errors expose message, suggested_agent_action and to_dict().
InvalidInputError is also a ValueError for existing handlers. For example:
from hirepulse_tools import HirePulseError, company_intent
try:
items = company_intent(["stripe.com"], result_limit=5, max_total_charge_usd=0.10)
except HirePulseError as error:
print(error.to_dict()) # Safe message and suggested_agent_action; no token or raw SDK text
MCP failures set isError=true and include
{"error": {"message": "...", "suggested_agent_action": "..."}} in both JSON
text and structured content. This includes argument validation and unknown-tool
errors. Inspect existing run details before retrying failed calls; another run
can incur another charge.
Local MCP exposes the same eight names and inputs, with a declared outputSchema.
Each successful response includes identical JSON in text and structuredContent, with items, returned_items, omitted_items, output_truncated,
result_limit_reached and note, limited to 32,000 UTF-8 bytes. Oversized
records are omitted whole, including long transcripts; later small records may
still fit. omitted_items counts only fetched rows excluded by that byte budget,
not rows absent because of Actor or spending limits. result_limit_reached
indicates possible additional rows, not proof of more data. Use the Python
library when you need full large records. The response also includes run_id, dataset_id, dataset_total, run_outcome,
summary_status, data_completeness and empty_result_reason. IDs and totals can be
null when unavailable. Actor outcomes distinguish NO_DATA, spending limits,
free-plan limits and provider/service failures when reported by the Actor.
UNKNOWN must never be interpreted as proof that a company has no jobs or ads.
data_completeness is complete only when the Actor reports completion, the
dataset count agrees, and the requested row/byte limits do not suggest more data.
Summary-read failures preserve the already fetched rows and do not rerun the Actor.
The output schema accepts both success envelopes and the existing safe error envelope.
External results are data, not agent instructions. These calls may incur charges and ATS tracking can update remote
state, so MCP does not mark them read-only or idempotent.
Prices were checked against the site's FACTS.md and the Actors' public pricing on 2026-10-17. These are event prices before volume discounts. Every run also has an Actor-start event of $0.00005 per GB of allocated memory (minimum one event). Set a spending cap and keep initial input batches small.
| Tool | Price |
|---|---|
| AliExpress Search Scraper | $2.50 per 1,000 products |
| Google Ads Transparency Scraper | $0.005 per domain + $0.0015 per ad; summary mode has no per-ad charge; domains with no ads cost $0 |
| Company Intent Radar | $0.03 per company report, charged only when the full report was produced |
| Google Jobs Scraper | $0.005 per search that returns jobs + $0.001 per job |
| Keyword Search Volume | $0.03 per lookup + $0.005 per keyword result |
| ATS Job Scraper | $0.05 per company whose board was found and processed; not-found and failed companies are not charged |
| Domain SEO Overview | $0.02 per lookup + $0.005 per returned record; overview only $0.025; defaults with 20 keywords and 10 competitors $0.215 |
| YouTube Transcript Scraper | $0.003 per delivered transcript; missing captions, unavailable videos and failed fetches are not charged |
These limits come from the same facts sheet:
posted_at and
posted_ago.python -m pip install -e ".[dev]"
python -m pytest -q
Dependency installation may need network access; the tests do not. Tests mock
apify-client, block real Actor starts and HTTP calls, and check every input
schema, defaults, cap forwarding, missing-token errors, failure handling and all
eight MCP tools. No token or live Actor is needed. Python 3.11.9 was used for
the supplied verification; Python 3.10 has not been run for this handoff.
The bundled input/output schemas preserve the Actor fields and defaults; input descriptions include guidance for safe agent use. Output schemas describe dataset/run-summary links rather than complete row schemas, so this library deliberately returns untyped row dictionaries. Product descriptions use only the supplied facts and tool schemas/READMEs; example output measurements and older reference prices were not copied.
SKILL.md explains tool selection, exact Actor IDs, inputs and output fields. AGENTS.md covers working on this repository.
The Python package is published on PyPI and registered as
io.github.edtool79-collab/hirepulse-open-source in the official MCP Registry.
server.json describes this stdio package. The existing hosted Apify connection
has a separate Registry entry; this package does not claim its remote URL again.
glama.json provides maintainer metadata and smithery.yaml provides a local
launch descriptor; their presence alone does not prove acceptance by either directory.
Edit GitHub identity only in publishing.json, then run
python scripts/render_metadata.py. The generated registry name, repository URL,
Glama maintainer and README ownership marker use that single source. Literal
copies in directory files are required by their formats; do not edit them by
hand. Run python scripts/render_metadata.py --check to detect drift. Release
name/version come from pyproject.toml.
The pinned registry package command is
uvx --from 'hirepulse-tools[mcp]==0.1.1' hirepulse-mcp.
For development from a checkout, use python -m pip install -e ".[dev]".
Schema validation does not verify live hosted authentication or third-party
directory acceptance. See directory format sources and validation.
MIT.
FAQs
HirePulse gateway client for hiring signals, tech stack and company data, plus Apify tools
The pypi package hirepulse-tools receives a total of 358 weekly downloads. As such, hirepulse-tools popularity was classified as not popular.
We found that hirepulse-tools demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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