83 posts tagged with "MCP"

Most ATS automation starts with a shallow question: can an agent create, read, update, and delete candidates?

That is the wrong first question. Recruiting work is not a generic CRUD table. It is a workflow with company boundaries, positions, pipeline stages, candidate metadata, hiring-team notes, and a real audit trail. If the tool surface only says "update candidate," the agent still has to guess which company, which role, which stage, and which action is safe.

We rebuilt the Breezy HR MCP server around the workflow shape instead: discover companies, read pipelines, inspect positions, list candidates, fetch metadata when needed, create or update candidates, move them through stages, and archive positions for cleanup.

Most "AI for research papers" demos stop at search: find a paper, summarize it, maybe extract a citation.

That is useful, but it is not the workflow researchers live in. A real literature review has saved papers, citation metadata, missing fields, PDF attachments, folders, group libraries, annotations, duplicates, trash, and the constant question of what should be read next.

We rebuilt the Mendeley MCP server around that full reference-management shape. An agent can now work with the library as a library, not just as a list of search results.

You paste an MCP server URL into a security analyzer. It spits out a number. You ask the obvious question: what does that number actually mean?

Most MCP scanners can't answer it. They run a bunch of regex, run a bunch of LLM prompts, and produce a verdict. If you push on the verdict, you find ad-hoc heuristics with no published source — and worse, you find marketing claims about "AI-powered security analysis" that nobody can audit.

We built MCPBundles' analyzer the other way around. Every rule cites a published taxonomy entry. If we can't cite an entry, the rule doesn't ship. The catalog is small, deliberate, and live: www.mcpbundles.com/learn/mcp-security.

This post is the "show your work" version of that page.

If you work in clinical research, biotech strategy, patient advocacy, or healthcare investing, the hard part is not knowing that ClinicalTrials.gov exists. The hard part is turning trial records into an answer you can use.

You may be trying to understand which sponsors are active in a disease area, whether a competitor has moved from phase 2 into phase 3, how strict the eligibility criteria are for a class of studies, or whether there are recruiting trials a patient advocacy team should know about. The raw registry has the data. Your actual job is to read across it quickly and explain what it means.

The Clinical Trials MCP server gives your AI agent a structured way to search studies, pull trial details, and summarize the result in the same conversation where the research question started.

If you work in governance research, compensation consulting, investing, board advisory, or business journalism, executive compensation data is only useful when you can compare it quickly and explain the components clearly.

The pay data lives in SEC proxy filings. Recent filings include inline XBRL tags, but the human-readable compensation tables still vary across companies. Older filings are even messier. The important numbers are inside long DEF 14A documents, footnotes, named executive officer tables, director compensation tables, pay-vs-performance sections, and company-specific formatting.

The SEC Executive Compensation MCP server is built so an agent can answer pay questions from structured SEC compensation data instead of making a user dig through proxy filings by hand.

If you work in fintech risk, bank partnerships, treasury operations, vendor diligence, or financial research, FDIC data usually appears at the moment someone needs confidence about an institution.

Is this bank FDIC-insured? What is its certificate number? Is it active? How large is it? What do its profitability metrics look like? Can I compare it to another institution without leaving the report I am writing?

The FDIC Bank Lookup MCP server gives AI agents and REST clients structured access to FDIC-insured institution data.

Every freight broker, shipper, and logistics team needs to answer the same question:

Can we trust this carrier?

The data exists. FMCSA publishes carrier data, safety records, crash counts, inspection history, out-of-service rates, authority status, and related signals. But the workflow is still clunky. People bounce between SAFER, FMCSA tools, carrier-vetting products, spreadsheets, and internal notes.

The FMCSA Carrier Safety MCP server turns that into a tool an AI agent or backend system can call directly.

H-1B wage data matters because it sits at the intersection of compensation, immigration, recruiting, and employer research.

Job seekers want to know which companies sponsor visas and what they pay. Immigration attorneys and HR teams need wage context for LCA work. Compensation teams want market benchmarks. Recruiters and analysts want employer-level sponsorship patterns.

The H-1B Visa Data MCP server turns public LCA disclosure filings into a searchable workflow for AI agents and REST clients.

If you are responsible for imports, landed-cost estimates, product classification, or customs review, HTS lookup is not an academic exercise. A wrong code changes margin, delivery timing, and compliance risk.

The first question is usually simple: "What HTS code should we use for this product?" Then the real questions start. Is the description close enough? Is there a more specific subheading? What is the general duty rate? Does a Section 301 surcharge apply? Is the result reliable enough to quote from, or does it need broker review?

The HTS Tariff MCP server is built for that first-pass classification workflow. Your agent can search tariff entries, inspect the hierarchy, pull duty fields, notice surcharge references, and turn the result into a short explanation your team can actually use.

If you work in grantmaking, nonprofit diligence, donor research, journalism, or civic analysis, IRS 990 filings are where the financial story lives. They show revenue, expenses, assets, liabilities, officer compensation, program service activity, and organizational structure.

They are also painful to work with directly.

The data is split across form variants, annual extract files, hundreds of columns, and identity joins. Organization names are not always where people expect them to be. EINs are the stable key, but users often start with a name.

The IRS 990 Nonprofit Financials MCP server turns those filings into a searchable product for AI agents and REST clients.