{"id":35191,"date":"2026-04-24T11:33:47","date_gmt":"2026-04-24T09:33:47","guid":{"rendered":"https:\/\/www.decidalo.com\/?page_id=35191"},"modified":"2026-04-24T11:40:58","modified_gmt":"2026-04-24T09:40:58","slug":"mcp-server","status":"publish","type":"page","link":"https:\/\/www.decidalo.com\/en\/mcp-server\/","title":{"rendered":"dec\u00eddalo MCP Server \u2014 Resource management that gets done for you"},"content":{"rendered":"


\n<\/p>\n

\n

\u25cf dec\u00eddalo MCP Server \u00b7 The first AI-agent-native staffing platform<\/p>\n

Resource management
\nthat gets done for you<\/span><\/h1>\n

Every staffing question \u2014 who’s available, who has the skills, who to move where, which resume to send \u2014 used to mean stitching together data from five places. dec\u00eddalo is the proven staffing platform for professional services firms; its MCP server lets Claude (or any MCP client) reason over that data and do<\/em> the work \u2014 re-plan, research, draft, update. In minutes, not days.<\/p>\n

<\/p>\n\n\n\n
\n
Days\u00a0\u2192\u00a0Minutes<\/div>\n
for typical staffing work<\/div>\n<\/td>\n
\n
One system<\/div>\n
of record for skills, utilization, resumes<\/div>\n<\/td>\n
\n
Any MCP client<\/div>\n
Claude Desktop, Claude Code, Cursor<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Book a 30-min demo<\/a><\/p>\n

\u2193 See sample use cases<\/a><\/div>\n<\/div>\n

<\/p>\n

\n

THE REALITY<\/p>\n

What your resource manager actually spends their week on<\/h2>\n

Three scenarios every staffing lead in professional services recognizes \u2014 and where time quietly disappears.<\/p>\n\n\n\n
\n
\n
\"\"<\/div>\n

“The plan just broke \u2014 now what?”<\/h3>\n

John is unavailable, which cascades into three reshuffles. Figuring out the right re-allocation means checking five projects, three calendars, and everybody’s skill profile. Usually takes a day.<\/p>\n<\/div>\n<\/td>\n

\n
\n
\"\"<\/div>\n

“Nobody can answer how many people do we have on X<\/em> in under 24 hours.”<\/h3>\n

Skill questions, bench reports, demand trends \u2014 the data exists, but pulling it together is an ad-hoc BI job every single time.<\/p>\n<\/div>\n<\/td>\n

\n
\n
\"\"<\/div>\n

“Profiles are outdated, and there’s no time to chase them.”<\/h3>\n

Consultants gain skills on projects that never make it back into the system. Outreach is manual, inconsistent, and easy to drop.<\/p>\n<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

<\/p>\n

\n
\n

WHAT THE AGENT DOES<\/p>\n

dec\u00eddalo + an agent that does the work<\/h2>\n

dec\u00eddalo<\/strong> gives you the structured bench \u2014 skills, utilization, projects, profiles, resumes. The MCP server gives an AI agent the ability to operate on it.<\/p>\n

<\/p>\n

\n\n\n\n
\n
1<\/div>\n<\/td>\n
\n

Re-plans the plan.<\/h3>\n

The agent reads active assignments, availability, skill-fit, and project-criticality, then proposes multi-hop reshuffles \u2014 move John off Alpha, Anna onto Alpha, split Beta 50\/50 between two colleagues<\/em> \u2014 with the trade-offs spelled out. You approve or adjust in chat.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

<\/p>\n

\n\n\n\n
\n
2<\/div>\n<\/td>\n
\n

Answers skill & bench questions in seconds.<\/h3>\n

“How many Azure Security people do we have? Who’s on the bench next quarter? Which skills are heating up in demand vs. cooling off?”<\/em> Structured queries, real staffing data, one prompt.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

<\/p>\n

\n\n\n\n
\n
3<\/div>\n<\/td>\n
\n

Closes the loop on profile quality.<\/h3>\n

When a skill is missing or uncertain, the agent drafts a Teams or Outlook message to the consultant \u2014 you review, send, and the reply is written straight back into the profile. No more stale data.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

<\/p>\n

Also handles:<\/strong>\u00a0\u00a0RFP resume generation<\/span>Hybrid AI + human staffing<\/span>Real-time availability checks<\/span>Compliance gap detection<\/span><\/div>\n<\/div>\n<\/div>\n

<\/p>\n

\n

USE CASES<\/p>\n

Flagship scenarios, built on real reasoning<\/h2>\n

Each one runs against live dec\u00eddalo data \u2014 not a simulated demo. Lead with the ones where agentic reasoning pays for itself first.<\/p>\n<\/p>\n\n\n
\n
\n\n\n\n
\n
\"\"<\/div>\n<\/td>\n
Complex re-planning (“ring swap”)<\/strong><\/p>\n

Multi-hop reassignments proposed in one prompt.<\/strong><\/p>\n

“Need an industry-Y senior. John fits but is on Alpha; his experience isn’t load-bearing there. Anna can take Alpha; split her current project 50\/50 across two colleagues.”<\/em><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/td>\n

\n
\n\n\n\n\n
\n
\"\"<\/div>\n<\/td>\n
Skill & bench intelligence<\/strong><\/p>\n

Instant answers to questions BI used to answer in days.<\/strong><\/p>\n

“How many Azure Security people do we have? What’s trending up in demand vs. down on the bench?”<\/em><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/td>\n<\/tr>\n

<\/p>\n

\n
\n\n\n\n
\n
\"\"<\/div>\n<\/td>\n
Closed-loop profile maintenance<\/strong><\/p>\n

Identify the gap, ask the person, write the answer back.<\/strong><\/p>\n

“Emma probably knows this but it’s not on her profile. Draft Teams message \u2192 confirm send \u2192 Emma replies \u2192 update profile.”<\/em><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/td>\n

\n
\n\n\n\n\n
\n
\"\"<\/div>\n<\/td>\n
RFP candidate matching & resume generation<\/strong><\/p>\n

Boolean search \u2192 fit matrix \u2192 5 branded PDFs in 20 minutes.<\/strong><\/p>\n

Tight submission windows, custom compliance annexes, multiple tailored resumes \u2014 exported straight from dec\u00eddalo with your template. RFP workflow \u2192<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/td>\n<\/tr>\n

<\/p>\n

\n
\n\n\n\n
\n
\"\"<\/div>\n<\/td>\n
Hybrid AI + human staffing<\/strong><\/p>\n

Skills your bench doesn’t cover? Propose an AI-assisted split.<\/strong><\/p>\n

“Project needs 2 COBOL specialists; bench has 0 \u2192 1 AI agent for legacy analysis + 3 human architects, with a cost delta.”<\/em><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/td>\n

\n
\n\n\n\n
\n
\"\"<\/div>\n<\/td>\n
Real-time availability & capacity<\/strong><\/p>\n

Availability and project-criticality checked in the same prompt.<\/strong><\/p>\n

“60% available in June, assigned to Project Omega through August \u2014 feasible part-time.”<\/em><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

<\/p>\n

\n
\n

HOW IT WORKS<\/p>\n

From sign-on to shipping decisions in one day<\/h2>\n

New customers go live on dec\u00eddalo and the MCP integration in the same onboarding.<\/p>\n\n\n\n
\n
1<\/div>\n

Roll out dec\u00eddalo with the MCP server included<\/h3>\n

Standard onboarding, SSO, tenant-scoped OAuth. You pick the platform that’s agent-native out of the box.<\/p>\n<\/td>\n

\n
2<\/div>\n

Point your AI agent at it<\/h3>\n

Add the MCP server to Claude Desktop, Claude Code, Cursor, or any MCP client. About 15 minutes.<\/p>\n<\/td>\n

\n
3<\/div>\n

Ask the question, or drop in the document<\/h3>\n

The agent returns a reasoned answer, candidate list, draft resumes, or outreach drafts. You review, edit, approve \u2014 writes happen on your say-so.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

<\/p>\n

\n

TECHNICAL CREDIBILITY<\/p>\n

Built on open standards<\/h2>\n

For the evaluators on your team who want to know what’s under the hood before they trust the story.<\/p>\n

<\/p>\n

\"Architecture<\/div>\n

<\/p>\n\n\n\n
\n\n\n\n\n
\n
\"\"<\/div>\n<\/td>\n
Based on the Model Context Protocol (MCP)<\/strong><\/p>\n

The open standard for connecting AI agents to real systems. If your team already uses Claude, Cursor, or any MCP-compatible client, dec\u00eddalo fits in natively.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/td>\n<\/tr>\n

\n\n\n\n\n
\n
\"\"<\/div>\n<\/td>\n
Covers the full staffing surface<\/strong><\/p>\n

Candidate search, skill and certification lookups, availability and utilization, project history, resume generation and export, profile updates \u2014 every tool a resource manager needs, exposed through a single, permission-aware interface.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/td>\n<\/tr>\n

\n\n\n\n\n
\n
\"\"<\/div>\n<\/td>\n
Security model enterprise teams expect<\/strong><\/p>\n

OAuth with tenant-scoped access; the agent inherits the user’s permissions, nothing more. Read by default; writes are opt-in per capability.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/td>\n<\/tr>\n

\n\n\n\n\n
\n
\"\"<\/div>\n<\/td>\n
Outlook & Teams integration, human in the loop<\/strong><\/p>\n

Optional Microsoft Graph integration for outreach drafts. Domain-whitelisted, dry-run by default \u2014 the agent prepares, the human sends.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/td>\n<\/tr>\n

\n\n\n\n
\n
\"\"<\/div>\n<\/td>\n
~15 minutes to go live<\/strong><\/p>\n

Once dec\u00eddalo is provisioned, the MCP integration is a single configuration step in the client of your choice.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Full technical documentation available on request for evaluation teams.<\/p>\n

<\/p>\n

Works with<\/strong>\u00a0\u00a0Claude<\/span>\u00a0\u00b7\u00a0Google Gemini<\/span>\u00a0\u00b7\u00a0ChatGPT \/ Codex<\/span>\u00a0\u00b7\u00a0Microsoft Copilot<\/span><\/div>\n<\/div>\n

<\/p>\n

\n
\n

BUILT BY THE TEAM BEHIND DEC\u00cdDALO<\/p>\n

The staffing platform consulting and professional services firms rely on \u2014 now the first of its kind ready for the AI-agent era.<\/h2>\n

Trusted by leading organizations around the world \u2014 including Fujitsu<\/strong>, T-Systems<\/strong>, and Computacenter<\/strong> \u2014 for skills, utilization, and staffing.<\/p>\n<\/div>\n<\/div>\n

<\/p>\n

\n

FAQ<\/p>\n

Questions buyers typically ask<\/h2>\n
\nDo I need to already be on dec\u00eddalo?<\/summary>\n

No. dec\u00eddalo and its MCP server are sold as a bundle \u2014 new customers go live on the platform and the MCP integration in the same onboarding.<\/p>\n<\/details>\n

\nDoes my data leave dec\u00eddalo?<\/summary>\n

No. The MCP runs against your tenant; the agent only sees what a user with the same permissions would see. Nothing is trained on your data.<\/p>\n<\/details>\n

\nWhich AI agents does this work with?<\/summary>\n

Any MCP-compatible client \u2014 Claude Desktop, Claude Code, Cursor, and others. The server implements the open Model Context Protocol, so it is not tied to a single vendor.<\/p>\n<\/details>\n

\nCan the agent actually change things in dec\u00eddalo, or is it read-only?<\/summary>\n

Write scopes are opt-in per capability. By default the agent reads and drafts, and your team approves before anything is written back to the system of record.<\/p>\n<\/details>\n

\nCan the agent send emails or Teams messages on its own?<\/summary>\n

No. Outreach is always drafted for human review. Domain whitelists and dry-run mode are on by default \u2014 the agent prepares, the human sends.<\/p>\n<\/details>\n

\nHow does re-planning handle constraints we haven’t explicitly told it about?<\/summary>\n

The agent surfaces its assumptions and trade-offs in the proposal \u2014 which people it would move, why, and what it’s holding constant. You accept, reject, or steer; the final decision is always yours.<\/p>\n<\/details>\n

\nWhat about GDPR, SOC 2, and data residency?<\/summary>\n

dec\u00eddalo is operated in line with European data protection regulations, and your tenant’s data residency controls apply to the MCP layer as well. Talk to sales for the current compliance documentation pack.<\/p>\n<\/details>\n

\nWhat’s the setup time?<\/summary>\n

Adding the MCP server to an AI client takes about 15 minutes. SSO and OAuth are typically wired up the same day.<\/p>\n<\/details>\n<\/div>\n

<\/p>\n

\n

Stop discussing staffing. Start shipping decisions.<\/h2>\n

30 minutes, live against a sandbox tenant with your kind of data. Bring the messiest staffing question you have.<\/p>\n

Book a 30-min demo<\/a><\/p>\n

Talk to an engineer<\/a><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

The first AI-agent-native staffing platform. dec\u00eddalo’s MCP server lets Claude and other MCP clients reason across live staffing data \u2014 skills, utilization, projects, profiles, resumes \u2014 and do the work: re-plan assignments, answer skill and bench questions, generate resumes, keep profiles up to date.<\/p>\n","protected":false},"author":4,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-no-title","meta":{"inline_featured_image":false,"footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-35191","page","type-page","status-publish","hentry"],"yoast_head":"\ndec\u00eddalo MCP Server \u2014 Resource management that gets done for you - decidalo<\/title>\n<meta name=\"description\" content=\"dec\u00eddalo MCP Server: Your AI agent re-plans staffing, finds matching consultants & generates CVs \u2014 from live data. 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