47 posts tagged with "AI Agents"

The easiest way to make an AI agent dangerous in a CRM is to let it act from a search result.

Search results feel like context. They have names, ids, owners, timestamps, and sometimes a stage. That is enough to produce a confident paragraph. It is not enough to change a customer record.

Copper made this obvious during the rebuild because the useful questions all started vague: the account in this Gmail thread, the renewal in proposal, the customer-success handoff, the stale task nobody owns. The Copper MCP server now treats those questions as account work, not table lookups.

Most "AI for call centers" demos stop at call history: fetch a recent call, summarize the transcript, and move on. That is useful for a screenshot. It does not help a support or sales team run the queue.

Picture this instead. Ten calls were missed while the team was in a meeting. Two came from existing customers. One came through a number that should have been assigned to the sales queue. Three agents are marked unavailable. The tags are inconsistent, so the weekly report undercounts escalations. A manager wants the follow-up list now, not a CSV export.

We see the same pattern across support teams: the hard part is rarely one missing field. It is the scattered context around the call.

We rebuilt the Aircall MCP server around that operations loop: validate the connection, read the account shape, list and inspect calls, match contacts, understand teams and numbers, then make narrow updates only where Aircall supports them.

Housing research questions are easy to ask and easy to answer badly.

"Is this county affordable?" "What does HUD say about rent here?" "Which income limit should I use?" "How much cost burden shows up in CHAS?"

An agent can only answer those questions well if it works from source data: HUD Fair Market Rents, standard income limits, MTSP income limits, geography identifiers, and CHAS affordability tables. A language model alone will blur those concepts together.

We rebuilt the HUD Housing Data MCP server around that evidence path, so agents can retrieve the HUD rows first and then explain what they mean.

Most AI valuation demos make the same mistake. A user types an address, the model returns a number, and everyone pretends the answer came from evidence.

That is backwards. UK property questions are only useful when the agent can show its working: nearby sold prices, EPC floor area, property type, transfer dates, postcode geography, match confidence, and the gaps where public data is thin.

We built the UK Property Intelligence MCP server around that evidence loop. The goal is not to manufacture certainty. The goal is to turn public house price data, EPC records, and postcode context into a bounded report an analyst can inspect.

Most "AI for e-commerce" demos stop at the same trick: ask for a product, get a row back. That demos well. It does not run a store.

Picture this instead. Your summer collection went live yesterday. Half the size and colour combos are silently set to hidden. Two of the categories on the homepage are empty because every product underneath is active=0. A "Mother's Day -20%" cart rule expired last week but is still showing on the storefront. Customers are checking out, but the carrier for one shipping zone has been disabled. Nobody noticed.

That is not a screenshot question. That is what a useful agent has to walk: products, variants, stock, categories, prices, cart rules, carriers, zones, and the customer messages people are leaving when checkout breaks.

We rebuilt the PrestaShop MCP server around that shape. Catalog, order book, and localization get separate tools per resource, not one flat JSON blob.

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. Useful for a screenshot, useless for a real review.

Picture this instead. You have 240 papers saved in Mendeley for a RAG-evaluation review. Forty are missing DOIs. Eighteen have a citation record but no attached PDF. Six are duplicates from earlier exploratory searches. Your shared group library has 30 newer papers your collaborator added last week that you have not seen yet. None of that shows up in a "search the web" demo.

We rebuilt the Mendeley MCP server around that mess. An agent now works with your library as a library — saved papers, missing metadata, PDF files, folders, annotations, groups, trash, and all.

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.

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.