You
have
Claude
in
your
product.
You
have
real
data
somewhere
else
—
a
database,
an
API,
a
filesystem,
internal
tools.
Right
now,
the
only
way
to
connect
them
is
manual:
copy
data
into
the
prompt,
hope
it’s
fresh,
watch
the
token
count
explode,
and
accept
that
the
AI
has
no
live
connection
to
anything
that
matters.
Model
Context
Protocol
(MCP)
changes
that.
MCP
is
a
standardized
way
to
wire
AI
assistants
directly
to
external
data
sources
and
tools.
Not
through
janky
API
wrappers
or
custom
code
for
each
integration
—
through
a
protocol
that
any
AI
assistant
can
speak,
and
any
data
source
can
expose.
It’s
what
OpenAI’s
function
calling
tried
to
be,
but
actually
portable.
This
isn’t
marketing
language.
I’ve
spent
the
last
six
months
building
production
workflows
at
AlgoVesta
that
depend
on
external
data
—
market
feeds,
user
portfolios,
pricing
engines.
MCP
solves
a
specific
problem:
how
to
let
the
AI
access
live
information
without
turning
your
prompt
into
a
50-paragraph
data
dump,
and
without
rebuilding
the
integration
when
you
switch
models.
Here’s
what
you
need
to
know
to
actually
use
it.
What
MCP
Actually
Does
(and
Doesn’t)
Start
with
what
MCP
is
not:
it’s
not
a
replacement
for
RAG.
It’s
not
a
function-calling
framework.
It’s
not
a
deployment
layer.
MCP
is
a
communication
protocol.
Think
of
it
as
HTTP
for
AI
context.
In
a
traditional
setup,
your
application
connects
to
Claude’s
API,
sends
a
prompt,
Claude
processes
it,
and
returns
a
response.
Everything
the
AI
knows
comes
from
the
prompt
itself.
If
you
need
data
from
a
database,
you
fetch
it
in
your
application
code
and
paste
it
into
the
message.
If
the
data
changes,
you
fetch
again.
If
you
switch
to
a
different
model,
you
rebuild
the
integration.
With
MCP,
you
define
a
server
that
exposes
resources.
Claude
connects
to
that
server,
not
directly,
but
through
your
application.
When
Claude
needs
data,
it
asks
for
it
through
the
MCP
protocol.
Your
server
responds.
The
AI
gets
fresh
context
without
you
managing
the
data
pipeline
manually.
MCP
was
built
by
Anthropic
in
collaboration
with
other
AI
companies.
Claude
can
use
it
natively.
GPT-4o,
Gemini,
and
other
models
will
likely
support
it
through
adapters
as
the
protocol
matures,
but
today,
Claude
is
the
primary
consumer.
The
protocol
defines
three
layers:
- Resources:
static
or
semi-static
data
the
server
exposes
—
a
database
query
result,
a
file,
a
configuration
object.
The
client
(Claude)
can
request
them
by
name. - Tools:
actions
the
server
can
perform
—
run
a
query,
update
a
record,
trigger
a
workflow.
Claude
calls
them
and
passes
parameters. - Prompts:
reusable
prompt
templates
the
server
provides.
Claude
can
request
them
to
get
context-specific
instructions.
You’ll
use
resources
and
tools
constantly.
Prompts
are
useful
for
specialized
workflows
but
less
critical
for
most
setups.
Why
This
Matters
More
Than
It
Sounds
The
problem
MCP
solves
is
real:
data
staleness,
prompt
bloat,
and
tight
coupling
between
your
app
and
a
specific
AI
model’s
API.
In
early
2024,
I
built
a
financial
analysis
system
using
Claude.
The
workflow
looked
like
this:
user
asks
a
question,
my
app
fetches
relevant
market
data,
fetches
the
user’s
portfolio,
formats
both
into
the
prompt,
sends
it
to
Claude,
gets
a
response.
This
works.
It
also
scales
poorly.
A
single
analysis
request
triggered
five
database
queries,
two
external
API
calls,
and
produced
a
prompt
that
was
often
4,000+
tokens
just
for
context.
Token
costs
were
insane.
Latency
was
visible
to
users.
With
MCP,
the
same
workflow
changes:
Claude
connects
to
the
MCP
server.
When
Claude
wants
market
data,
it
asks
the
server
for
it
directly.
The
server
fetches
it.
Claude
makes
the
decision,
not
your
application.
This
sounds
like
a
small
shift.
It’s
not.
Benefits
in
practice:
- Latency
drops:
Claude
doesn’t
wait
for
your
application
to
fetch
and
format
data.
It
requests
what
it
needs,
when
it
needs
it,
in
parallel
with
its
reasoning. - Cost
decreases:
You’re
not
padding
every
prompt
with
