Point your own AI at your hiring system.
Pitch N Hire speaks MCP. Connect Claude, ChatGPT, Gemini or an agent your team wrote, and it can read requisitions, rank candidates, schedule interviews and post jobs — under exactly the permissions the human whose key it uses already has. Nothing about your AI strategy has to run through us.
The same interfaces reach the whole record: the applicant tracking system and the HRMS, not a read-only slice of either.
Free sandbox, no card. The same endpoints in sandbox and in production.
{
"mcpServers": {
"pitchnhire": {
"command": "npx",
"args": ["-y", "@pitchnhire/mcp-server"],
"env": {
"PNH_API_KEY": "pnh_live_…",
"PNH_WORKSPACE": "acme-talent"
}
}
}
}
# then, in the assistant:
> "Rank the Bengaluru backend pipeline against
> the approved scorecard and tell me which two
> candidates the panel disagreed on."
called pnh.requisitions.list
called pnh.candidates.rank (812 -> 34)
called pnh.scorecards.get
-> 34 clear the bar. Panel dissent on 2:
REQ-4471/c_88213 — design depth
REQ-4471/c_90114 — systems depth
Reasoning and evidence attached per score. curl -X POST https://api.pitchnhire.com/v1/requisitions/REQ-4471/rank \
-H "Authorization: Bearer $PNH_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"scorecard_id": "sc_backend_sr",
"explain": true,
"limit": 25
}'
# 200 OK
{
"requisition": "REQ-4471",
"ranked": 25,
"pool": 812,
"results": [
{
"candidate_id": "c_88213",
"score": 91,
"evidence": [
{ "criterion": "distributed systems",
"met": true,
"source": "resume:p2:l14" },
{ "criterion": "team leadership",
"met": "partial",
"source": "interview:i_5521:14:22" }
],
"decision_required_by": "human"
}
]
} // Verify the signature, then do whatever you like.
import { verify } from "@pitchnhire/webhooks";
export async function POST(req) {
const raw = await req.text();
const event = verify(raw, req.headers.get("pnh-signature"),
process.env.PNH_WEBHOOK_SECRET);
switch (event.type) {
case "candidate.ranked":
await ourAgent.review(event.data); // your model
break;
case "interview.completed":
await slack.post(summarise(event.data)); // your tools
break;
case "offer.accepted":
await payroll.createEmployee(event.data);
break;
}
return new Response("ok");
} Use our AI, bring your own, or run us headless.
Built-in agents source, screen, schedule, draft and grade. Nothing to configure, and evidence is attached to every score.
Most teams start here
Claude, ChatGPT or Gemini operates your workspace directly — reading pipelines, ranking, scheduling and drafting — inside your permissions.
Teams with an AI habit already
Call your own model on our data, or ours on yours. Bring a fine-tuned screening model and we will run the record around it.
Engineering-led talent teams
Keep us as the system of record and build your own interface or orchestration on top. MCP, REST and webhooks cover everything the interface does.
Platform and GCC teams
Every tool a recruiter has, exposed as a tool an agent can call.
The MCP server covers the whole record. An agent cannot reach anything the key's owner cannot, and every call is written to the same audit log as a human action.
An agent with your permissions, not a back door.
An agent key is issued to a user and inherits their role, workspace and field-level permissions. Revoke the person, revoke the agent.
Agent actions land in the same immutable log as human ones, tagged with the model and the key that made them.
Advance, reject, offer and employee creation always need a named person to confirm — by design, not by policy.
Your data never trains a model used by anyone else, and model providers operate under zero-retention terms.
Every key has a published per-minute and per-day limit with a burst allowance, and a 429 response tells you exactly when to retry.
A full-fidelity sandbox workspace with seeded data, so you can build against it before you buy anything.
Data residency, retention and the rest of the controls are covered on the security page.
Four patterns the open platform is built for.
An agent reads every open requisition overnight, flags the ones that stalled and drafts the nudge to each hiring manager.
MCP · webhooks · Slack
A platform team runs a fine-tuned model for niche engineering roles and writes its scores back onto the candidate record with evidence.
REST · candidates.rank
Headless: the site is yours, applications and status live in Pitch N Hire, and candidates never see two systems.
REST · headless
An agent assembles the full decision trail for any requisition — scores, evidence, who decided, when — as one export.
MCP · audit.export
Questions about the open platform.
What is MCP, in one sentence?
The Model Context Protocol is an open standard that lets an AI assistant call tools in another system. Because Pitch N Hire ships an MCP server, an assistant like Claude or ChatGPT can work inside your hiring data directly, rather than you copying text between windows.
Can an agent do something a recruiter could not?
No. An agent authenticates with a key issued to a specific user and inherits that user's role and workspace permissions exactly. If the person cannot see salary bands or delete a candidate, neither can their agent.
Is an agent allowed to reject candidates?
Not on its own. Advance and reject are the two actions that always require a named human, whether they come from the interface, the API or an agent. An agent can recommend and queue a decision; a person confirms it, and the audit log records who.
Do you train on our data if we use our own model?
We never train shared models on your data, whichever route you use. When you bring your own model, prompts and responses go straight between your provider and your workspace; we store the request metadata for the audit log and nothing else.
What are the rate limits, and are they enough for an agent?
Every key has a published per-minute and per-day limit with a burst allowance, and a 429 response tells you exactly when to retry. Agentic workloads are mostly reads, so the default ceiling is sized for continuous operation by a large team, and enterprise plans lift it on request. Ask us for the current figures for your plan.
Can we run Pitch N Hire headless?
Yes. Everything that matters is reachable through MCP, REST and webhooks, so you can keep us as the system of record while your own interface or agents do the work. Running your own career site and internal tooling on top of it is a supported pattern.
Which assistants does it work with?
Any client that speaks MCP or supports function calling — Claude, ChatGPT connectors, Gemini, editor clients such as Cursor and Continue, and orchestration frameworks such as LangGraph and CrewAI. Because the interface is an open protocol rather than a per-vendor integration, a new client that speaks MCP works without anything changing here.
The data your assistant reads comes from the rest of the platform: the ATS for pipelines and candidates, interview intelligence for scores and evidence, vendor management for agency submissions, and HRMS once someone is hired. See also integrations and how we handle access and security.
Give your AI a hiring system it can actually operate.
Sandbox keys are free. Or bring your CTO to a call and we will connect your own agent to a live workspace while you watch.