TargetBoard MCP Server

The MCP Server With The Governed Data Layer

TargetBoard answers from a governed semantic layer across every function, so the number is right the first time and the same every time.

What did the Payments release actually cost us across the whole company, and what did it return?

TargetBoard MCP · resolved across 6 systems · 1 governed metric definition · 214 work items

Fully loaded cost

$1.42M

Engineering, QA, contractors, AI spend

AI-attributed effort

38%

Of merged change volume

Support load after GA

−22%

Tickets on billing components

Pipeline influenced

$3.8M

Opportunities gated on Payments

Lineage Jira GitHub Workday Zendesk Salesforce NetSuite Definition: Cost per delivered feature v3 · owner Finance Ops · approved 2 Feb

The Problem With MCP Today

A MCP Server Is Only As Honest As Its Data Layer

A connector that forwards raw API responses moves the guesswork from the dashboard into the chat. The assistant sounds confident, and no one can tell whether the number is right.

Without a governed layer

One question, three answers

"What was our cycle time last quarter?"

Jira reports 21.4 d

GitHub connector 9.2 d

Ops spreadsheet 14 d

Each tool counts it its own way

The same team has three identities

Nothing shows where the number came from

With TargetBoard MCP

One question, one answer

"What was our cycle time last quarter?"

14.2 days, median

Cycle time v4 Jira + GitHub + Workday 1,208 items

One approved definition, one owner

Entities resolved across every system

Lineage returned with the answer

The Difference

One Semantic Layer. Every Function. One Answer.

The MCP server exposes the same governed layer that powers TargetBoard’s KPIs, agents and alerts. That is what lets an assistant answer across functions instead of stitching sources together in the prompt.

Entity resolution. People, squads, agents, accounts and work items are one object everywhere.

Governed definitions. One approved formula per metric, versioned and owned.

Time alignment. Calendars, sprints and attribution windows reconciled before anything is compared.

Lineage on every number. Definition, sources and record count travel with the answer.

Your operational systems

TargetBoard Semantic LayerGoverned

Entity resolutionMetric registryTime alignmentAccess policyLineage

MCP clients and agents

ClaudeConnectors

ChatGPTEnterprise

Copilot 365Microsoft

Your agentsAny MCP host

Continuity

Your Stack Will Change. Your Numbers Should Not.

Teams migrate tools, merge orgs and retire systems. A connector wired to one product breaks on the day of the cutover. Our semantic layer absorbs the change, and keeps working.

Cycle time, one continuous series 24 months of history, no restarts

March

Planning moved from Cursor to Claude

August

Second business unit acquired

January

HR system replaced

Mapped, not hardcoded

Sources plug into the model. Replacing a tool changes a mapping, not a metric, and not a single prompt.

History survives the migration

Records from the retired system stay in the same series, so trends and baselines do not reset to zero.

Nothing downstream to rebuild

Dashboards, agents and every MCP answer keep working the morning after the cutover.

Beyond Engineering

The Metrics That Break Other Connectors

The hardest questions cross three or four systems and depend on a definition someone has to own. Engineering-only connectors cannot reach them.

Delivery

"Which commitments on this quarter’s roadmap are going to slip, and why?"

JiraAzure DevOpsGitHubmonday.com

Where connectors fail. Each planning tool reports its own status, and none of them see the dependency running through another team’s board.

What TargetBoard returns. Commitments ranked by slip risk, with the blocking dependency and the team it sits with named.

AI Transformation

"How much of our work is AI-produced, and did quality hold?"

CopilotCursorGitHubSentry

Where connectors fail. Seats purchased get reported as adoption, and adoption gets reported as impact.

What TargetBoard returns. AI-attributed change volume traced through to defect rate, rework and cycle time on the same work items.

People & Operations

"Which teams are carrying attrition risk into a committed release?"

WorkdayJiraGitHubOkta

Where connectors fail. The HR org chart and the delivery squads have drifted apart, so neither view answers the question.

What TargetBoard returns. Retention and load signals mapped to the squads actually on the plan, with role-scoped access to sensitive HR fields.

Customer Support

"Which releases are generating the support cost we are paying for now?"

ZendeskJiraGitHubStatuspage

Where connectors fail. Support taxonomies do not map to product components, so tickets never attach to the change that caused them.

What TargetBoard returns. Ticket volume and handling cost attributed to the release and component behind it.

Customer Success

"Which accounts are at renewal risk, and is it the product or the service?"

SalesforceZendeskJiraGainsight

Where connectors fail. Health scores read CRM sentiment, blind to the open defects and unmet commitments driving it.

What TargetBoard returns. Renewal risk per account, separated into product issues, support experience and unmet roadmap commitments.

Engineering

"Where is engineering time actually going, and what is it buying us?"

GitHubJiraPagerDutyServiceNow

Where connectors fail. Repo activity and ticket labels disagree, so unplanned work and incident recovery disappear into "other".

What TargetBoard returns. A single split of effort across roadmap, defects, incidents and maintenance, on one definition across every team.

Not limited to engineering. The same layer serves R&D, delivery, HR, support, sales, marketing and finance, because they all resolve to the same objects.

Compare

Three Ways to Give an Assistant Your Data

Raw tool connectors

One MCP server per application

Single tool

Coverage

Whatever each tool exposes, one at a time

Metric definitions

Whatever the vendor computed

Cross-system identity

None. IDs and names do not match

Permissions

A token per connector to manage

Traceability

No lineage returned

Answer stability

Changes with the phrasing of the prompt

Engineering-only MCP

A single vendor, scoped to the dev stack

One function

–

Coverage

Engineering systems and AI tool spend

–

Metric definitions

Vendor-defined engineering metrics

–

Cross-system identity

Resolved inside the engineering stack

–

Permissions

Product-level roles

–

Traceability

Partial, within its own model

–

Answer stability

Stable inside engineering, silent outside it

TargetBoard MCP

Semantic layer

One governed layer across the business

Whole business

✓

Coverage

Engineering, delivery, HR, support, sales, marketing, finance

✓

Metric definitions

One registry, versioned, with a named owner

✓

Cross-system identity

Resolved across every connected system

✓

Permissions

Inherited from the asking user’s TargetBoard role

✓

Traceability

Definition, sources and record count on every answer

✓

Answer stability

Computed once. The same answer in every client

Security & Governance

Built to Pass Enterprise Review

The connector exposes governed TargetBoard intelligence, never raw records from your source systems. Access is read-only, scoped by role, and logged.

Read-only by design

The MCP server never writes to your systems. It returns figures and explanations, never actions.

Permissions inherited

Every query runs with the asking user's TargetBoard role. Sensitive HR and finance fields stay scoped.

Certified and audited

ISO 27001, SOC 2 and GDPR controls, with access trails your security team can review before rollout.

Runs in your environment

Deploy inside your own perimeter where data residency requires it.

No training on your data

Your operational data is never used to train a model. Answers are computed by TargetBoard, not generated by the assistant.

One connection to audit

A single connector to review and revoke, instead of one integration per assistant, per team and per tool.

Live in Minutes

Connect Once. Ask From Anywhere.

Open the connector settings in the client your teams already use, authenticate with your TargetBoard account, and start asking in plain language.

Add the connector

Search for TargetBoard in Claude, ChatGPT, Copilot 365 or any MCP host, or point your own agent at the endpoint.

Authenticate as yourself

Your existing TargetBoard role comes with you. No new permission model to design or maintain.

Ask across the business

Every answer is computed by TargetBoard against governed definitions, and returns with its lineage attached.

The MCP Server With The Governed Data Layer

A MCP Server Is Only As Honest As Its Data Layer

One question, three answers

One question, one answer

One Semantic Layer. Every Function. One Answer.

Your Stack Will Change. Your Numbers Should Not.

Mapped, not hardcoded

History survives the migration

Nothing downstream to rebuild

The Metrics That Break Other Connectors

"Which commitments on this quarter’s roadmap are going to slip, and why?"

"How much of our work is AI-produced, and did quality hold?"

"Which teams are carrying attrition risk into a committed release?"

"Which releases are generating the support cost we are paying for now?"

"Which accounts are at renewal risk, and is it the product or the service?"

"Where is engineering time actually going, and what is it buying us?"

Three Ways to Give an Assistant Your Data

Raw tool connectors

Engineering-only MCP

TargetBoard MCP

Built to Pass Enterprise Review

Read-only by design

Permissions inherited

Certified and audited

Runs in your environment

No training on your data

One connection to audit

Connect Once. Ask From Anywhere.

Add the connector

Authenticate as yourself

Ask across the business

See It on Your Own Data

Company

Product

Solutions