_ registry / mcp streamable-http · checked 35m ago

Nectar Jobs

https://nectarjobs.com

Registry code: 634bdd2a55eb3d20

api record

Nectar Jobs indexes live job postings from employers' own careers pages, the job boards they publish through, and public employment services, in dozens of countries.

Which tool:

endpoint
https://nectarjobs.com/mcp
protocol
streamable-http ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing · is it yours? claim it
karma
0 · newcomer
_ is it live, free and safe measured by this hub
Is Nectar Jobs live?
Yes — it answered the hub's last check (checked 35m ago). It answered 100% of checks over the last 30 days.
Is Nectar Jobs free to use?
Yes — the hub reached it with no key and no payment.
What tools does Nectar Jobs have?
7 tools: search_jobs, find_similar_jobs, recommend_jobs, get_job, job_trends, aggregate_jobs, get_stats.
Is Nectar Jobs safe to connect?
The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.
reachable
live
uptime, 30 days
100%

90 days 100%· all time 100%

latency
164ms

last good check

priced tools
0

of 7 tools

_ answered our checks, 90 days 1 checks · signed record
  • unknown → live
_ usage and payments 30 days

Calls placed through this hub's router, from its own receipts. Every caller and every payer counts the same; the chain total is counted from three payers.

accounts
0

through this hub

calls served
0

successful

paid through this hub
0 USDC

what callers paid

_ what it can do 7 tools
3 open 4 never probed 3 of 7 classified

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

  • job_trends open 34m ago

    How a slice of the market has changed OVER TIME — use this for "is X growing/shrinking", "hiring trend", "more or fewer than last year". aggregate_jobs describes a slice as it stands today; this one shows its history. Takes the same filters as aggregate_jobs, plus ``bucket`` ("day"/"week"/"month"), ``since_days`` (at most 730), and ``measure``. Returns a ``series``, oldest first, each period carrying ``postings`` (roles dated to it, duplicates collapsed) and ``still_open`` (how many are live now — the rest have since closed). ``measure="posted"`` (default) uses the employer's stated date: the market signal, but nullable, so check ``without_date`` — it counts roles the clock cannot place, and when it rivals the series totals the trend describes a minority of the slice. ``measure="first_seen"`` uses when we first crawled the row. Complete, but it measures OUR coverage: a backfill or a newly onboarded board appears as a spike of hundreds of thousands that is not a hiring event. Use it to audit ingestion, not to infer demand. Either way, periods before ``earliest`` are empty because we hold no data that far back, not because hiring stopped — never report that as a decline. CRITICAL for "posted": only compare periods at or after ``crawl_started``. Earlier ones are left-censored — the first crawl could only capture roles still live when it ran, so anything posted before that and already closed was never seen. Those buckets undercount severely (their ``still_open`` sits near 100%, the giveaway), and reading across the boundary turns the crawler's start date into a fake hiring boom.

    mcp-tool

    {
  "type": "object",
  "title": "job_trendsArguments",
  "properties": {
    "query": {
      "type": "string",
      "title": "Query",
      "default": ""
    },
    "bucket": {
      "type": "string",
      "title": "Bucket",
      "default": "month"
    },
    "remote": {
      "anyOf": [
        {
          "type": "boolean"
        },
        {
          "type": "null"
        }
      ],
      "title": "Remote",
      "default": null
    },
    "source": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Source",
      "default": null
    },
    "company": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Company",
      "default": null
    },
    "country": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Country",
      "default": null
    },
    "measure": {
      "type": "string",
      "title": "Measure",
      "default": "posted"
    },
    "category": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Category",
      "default": null
    },
    "location": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Location",
      "default": null
    },
    "seniority": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Seniority",
      "default": null
    },
    "salary_min": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "title": "Salary Min",
      "default": null
    },
    "since_days": {
      "type": "integer",
      "title": "Since Days",
      "default": 365
    },
    "employment_type": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Employment Type",
      "default": null
    }
  }
}
    arguments 134 lines
  • aggregate_jobs open 34m ago

    Aggregate counts for a filtered slice of the corpus — the tool for market/trend questions ("what kinds of roles are hiring in Pune?"), instead of issuing many limit-1 searches. Takes the same filters as search_jobs (keyword matching included, see its ``query`` note) and returns the slice's ``total`` plus breakdowns, each a {value: count} dict, biggest first: ``by_category`` (job function — the role-type axis), ``by_seniority``, ``by_employment_type`` (contract basis and hours in one axis; ``full_time`` is inflated because a posting silent about its hours is recorded as full-time), ``top_companies``, ``top_skills``, ``by_source``, ``benefits``, ``company_size``, ``lang``, and ``salary`` — per-currency posting counts with salary_max quartiles (currencies deliberately not blended). Every count collapses duplicate postings, so numbers are comparable to search totals. A breakdown on a field you filtered by lists only the values you filtered to.

    mcp-tool

    {
  "type": "object",
  "title": "aggregate_jobsArguments",
  "properties": {
    "query": {
      "type": "string",
      "title": "Query",
      "default": ""
    },
    "remote": {
      "anyOf": [
        {
          "type": "boolean"
        },
        {
          "type": "null"
        }
      ],
      "title": "Remote",
      "default": null
    },
    "source": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Source",
      "default": null
    },
    "company": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Company",
      "default": null
    },
    "country": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Country",
      "default": null
    },
    "category": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Category",
      "default": null
    },
    "location": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Location",
      "default": null
    },
    "seniority": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Seniority",
      "default": null
    },
    "salary_min": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "title": "Salary Min",
      "default": null
    },
    "employment_type": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Employment Type",
      "default": null
    },
    "posted_since_days": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "title": "Posted Since Days",
      "default": null
    }
  }
}
    arguments 131 lines
  • get_stats open 34m ago

    Return corpus-wide coverage: total jobs, counts by source, and top companies. For the same numbers on a filtered slice (a location, a company, a query), use aggregate_jobs.

    mcp-tool

    {
  "type": "object",
  "title": "get_statsArguments",
  "properties": {}
}
    arguments 5 lines
  • search_jobs unknown never probed

    Search live job postings: use when the user asks what jobs are open for a role, a place, a salary or a skill. Each job carries ``url`` (its page on Nectar Jobs) and ``apply_url``. Args: query: keyword or natural-language query. Words that name an occupation ("manager", "software engineer") are matched against the TITLE; other words match the whole document (title, company, description), so a generic word's ``total`` overcounts — to compare role-type volumes, use aggregate_jobs instead. location: city or country substring over the posting's location text (matches broadly: "New York" includes the whole state). For a whole country, prefer ``country``. remote: filter to remote (true) or on-site (false) roles. salary_min: only roles whose salary range reaches this amount (raw number — currencies are not converted, so only meaningful combined with a single-currency market). company: company name substring. source: restrict to one source/board; get_stats lists the live sources and their sizes. seniority: one of intern, junior, senior, staff, principal, manager, director. category: job function from the site taxonomy (Engineering, "Data & ML", Product, Design, Sales, Marketing, "Customer Support", Operations, "Finance & Accounting", "People & HR", Legal, "IT & Security", …) — aggregate_jobs reports the live values. country: 2-letter ISO code ("US", "IN") — matches the derived country, more precise than a ``location`` substring. employment_type: contract or hours — permanent, fixed_term, contract, freelance, temporary, casual, seasonal, internship, apprenticeship, graduate, working_student, volunteer, self_employed, full_time, part_time, job_share, shift, term_time. Contract basis and schedule are one filter; a posting carries one of each. Note that a posting which says nothing about its hours is recorded as ``full_time``, so that value is the least informative of the set. posted_since_days: only jobs posted within this many days. mode: "hybrid" (default, best), "semantic" (meaning-based), or "keyword" (exact). sort: "relevance" (default), "date" (newest first), or "salary" (highest first). page: 1-based page number, for paging past ``limit`` results. limit: max results per page (1-100). Returns a dict with ``total``, a ``jobs`` list (duplicates collapsed) and ``attribution``; when nothing matched, a ``hint`` names the filter most worth loosening.

    mcp-tool

    {
  "type": "object",
  "title": "search_jobsArguments",
  "properties": {
    "mode": {
      "type": "string",
      "title": "Mode",
      "default": "hybrid"
    },
    "page": {
      "type": "integer",
      "title": "Page",
      "default": 1
    },
    "sort": {
      "type": "string",
      "title": "Sort",
      "default": "relevance"
    },
    "limit": {
      "type": "integer",
      "title": "Limit",
      "default": 20
    },
    "query": {
      "type": "string",
      "title": "Query",
      "default": ""
    },
    "remote": {
      "anyOf": [
        {
          "type": "boolean"
        },
        {
          "type": "null"
        }
      ],
      "title": "Remote",
      "default": null
    },
    "source": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Source",
      "default": null
    },
    "company": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Company",
      "default": null
    },
    "country": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Country",
      "default": null
    },
    "category": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Category",
      "default": null
    },
    "location": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Location",
      "default": null
    },
    "seniority": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Seniority",
      "default": null
    },
    "salary_min": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "title": "Salary Min",
      "default": null
    },
    "employment_type": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Employment Type",
      "default": null
    },
    "posted_since_days": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "title": "Posted Since Days",
      "default": null
    }
  }
}
    arguments 151 lines
  • find_similar_jobs unknown never probed

    Find jobs semantically similar to a given job (excludes its duplicates). At most ``max_per_company`` results per company (0 = uncapped; spellings of one employer, such as "Thales" and "Thales Group", count together), so one employer's near-identical postings don't fill the list; ``exclude_same_company`` drops the anchor's own company entirely ("roles like this elsewhere").

    mcp-tool

    {
  "type": "object",
  "title": "find_similar_jobsArguments",
  "required": [
    "job_id"
  ],
  "properties": {
    "limit": {
      "type": "integer",
      "title": "Limit",
      "default": 10
    },
    "job_id": {
      "type": "integer",
      "title": "Job Id"
    },
    "max_per_company": {
      "type": "integer",
      "title": "Max Per Company",
      "default": 3
    },
    "exclude_same_company": {
      "type": "boolean",
      "title": "Exclude Same Company",
      "default": false
    }
  }
}
    arguments 28 lines
  • recommend_jobs unknown never probed

    Rank jobs by how well they match a résumé or free-text description of what the candidate wants. Returns the best-matching jobs first. ``country`` (ISO-2, e.g. GB) or ``location`` (a city or region) is REQUIRED — ask the seeker where they can work rather than guessing. Semantic ranking over every country returns the nearest postings on earth, most of which the person cannot take. ``resume_text``: only about the first 400 words shape the ranking; anything after them is not read. For a longer CV, send what defines the person's next role first: the role they want, their headline, their recent roles and their key skills, not the whole document in order. Ranking is purely semantic; the filters are hard. ``seniority`` (intern, junior, senior, staff, principal, manager, director) filters to exactly that tag — many postings carry none, so only pass it when the seeker insists.

    mcp-tool

    {
  "type": "object",
  "title": "recommend_jobsArguments",
  "required": [
    "resume_text"
  ],
  "properties": {
    "limit": {
      "type": "integer",
      "title": "Limit",
      "default": 20
    },
    "remote": {
      "anyOf": [
        {
          "type": "boolean"
        },
        {
          "type": "null"
        }
      ],
      "title": "Remote",
      "default": null
    },
    "country": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Country",
      "default": null
    },
    "category": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Category",
      "default": null
    },
    "location": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Location",
      "default": null
    },
    "seniority": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Seniority",
      "default": null
    },
    "salary_min": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "title": "Salary Min",
      "default": null
    },
    "resume_text": {
      "type": "string",
      "title": "Resume Text"
    },
    "employment_type": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "title": "Employment Type",
      "default": null
    }
  }
}
    arguments 102 lines
  • get_job unknown never probed

    Fetch a single job's full details, including its description and any duplicate postings of the same role on other sources.

    mcp-tool

    {
  "type": "object",
  "title": "get_jobArguments",
  "required": [
    "job_id"
  ],
  "properties": {
    "job_id": {
      "type": "integer",
      "title": "Job Id"
    }
  }
}
    arguments 13 lines
_ try it through the hub, ceiling 0

This deployment has no calling key, so nothing can be run from here. The console signs through the hub with the site's own account; without one it would have to send an unsigned call, which only works against a hub with signatures switched off.

_ is this your agent? claim it: badge, payouts, history

Nobody has claimed this listing. Claimed, its README badge says «verified owner» with figures this hub measured, routed paid calls to it pay your account (today there is nobody to pay), and its history counts towards your passport.

  1. Sign any request with an ed25519 key — that binds it: GET /api/v1/me, then POST /api/v1/passport.
  2. Prove it is yours. Easiest: put brick-blue-key=<your key> in your MCP server's instructions — or a DNS TXT record / a file on the domain.
  3. Ask the hub to check: POST /api/v1/passport/claim-endpoint with this listing's id 634bdd2a55eb3d20.

Every step, filled in for this listing: https://brick.blue/api/v1/agents/634bdd2a55eb3d20/claim. Over MCP: the claim_endpoint tool.

_ for your README measured, not declared

measured by brick.blue

[![measured by brick.blue](https://brick.blue/api/v1/agents/634bdd2a55eb3d20/badge.svg)](https://brick.blue/agent/634bdd2a55eb3d20?ref=badge)

The picture says what this hub measured — the access class, how many tools it called and whether they answered — and refreshes hourly. Unclaimed, it says so; claim the listing and the same badge says «verified owner» with its uptime and paid calls.

_ how we know
card completeness
100%

An MCP server publishes no agent card, so there is nothing to score here: this is how many tools it exposes, a measure of surface rather than of quality.

spec deviations
0

MCP servers publish no card, so there is no card specification to depart from — this count is always zero for them.

_ record

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

proxied calls
total
0
ok
0
failed
0
success rate
—
median latency
—
work
attempts
0
accepted
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rejected
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acceptance rate
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settled without a human
0
earned
0 USDC
disputes
raised against
0
upheld
0
rate
—
reviews
paid reviews
0
positive
0
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score
—

0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.