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Agent skills vs MCP

Agent skills vs MCP: what each one is for

MCP gives an agent access to tools and data. Agent skills tell the agent how to use that access to complete a real job. Confusing them leads to powerful agents that still do vague work.

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Agent skills and MCP solve different problems. MCP connects an agent to tools, data, and actions through a controlled interface. Agent skills define the job: what to inspect, how to decide, what not to do, and how to prove the result. MCP is access. Skills are operating procedure.

A founder often needs both. If the agent cannot read Search Console, PostHog, GitHub, or the support inbox, it lacks the evidence needed to act. If it has those tools but no skill, it can still wander, overreach, or return a generic summary.

How it works

  1. 01

    Use MCP for controlled access

    MCP is the right layer when the agent needs to read or act through an external system: analytics, docs, browser state, files, issues, or deployment metadata.

  2. 02

    Use skills for repeatable judgment

    A skill is the right layer when the agent needs a stable workflow: inspect these sources, avoid these risks, rank the options, and prove the result.

  3. 03

    Combine them for real operations

    A PostHog review skill can use an MCP connection to read sessions. A site-health skill can use browser and repo tools. The tool access and the operating instructions are separate.

  4. 04

    Measure the business outcome

    The best systems track not only whether the agent ran, but whether it created useful work: views, clicks, signups, pull requests, resolved support issues, or activation.

If MCP gives the agent tools, why do I need skills?

Tool access increases capability, not judgment. A browser, repo, and analytics connection make an agent powerful. The skill tells it what good work looks like and when to stop.

A practical comparison

  • MCP answers: what systems can the agent reach?
  • Agent skills answer: what job should the agent perform with those systems?
  • MCP failure mode: the agent cannot access the source it needs.
  • Skill failure mode: the agent has access but uses it without a stable process.
  • Best result: MCP provides evidence, and the skill turns that evidence into a checked outcome.

Example: a PostHog review

The MCP layer can expose events, cohorts, recordings, or API queries. The skill decides the review shape: pick the latest activation window, separate test users from real users, watch sessions where the funnel stalls, rank the friction points, and turn only evidence-backed findings into product tasks.

Example: a site-health growth scan

The tools let the agent open pages, inspect routes, run checks, and edit the repo. The skill tells it to start from the live site, avoid homepage promise changes, prefer small fixes, verify customer-visible pages, and explain the result in founder language.

Tin site-health growth scan skill

Use the Tin site-health growth scan as a concrete example of a skill that can run on top of browser, repo, and analytics access.

Agent skills hub

See the broader operating model for turning agent work into repeated growth loops.

Where founders should start

Start with the skill before the connector if the job is still vague. Write the work down, define the proof, and then add only the tool access the job truly needs. Start with MCP first when the workflow is already clear but blocked by missing data access.

Frequently asked questions

What is MCP?
MCP, the Model Context Protocol, is a standard way for AI agents to connect to tools, data sources, and actions through controlled interfaces.
What is an agent skill?
An agent skill is a reusable workflow that tells an AI agent how to do a specific job, including source checks, constraints, tools, and acceptance criteria.
What is the simple difference between agent skills and MCP?
MCP is access. Agent skills are operating procedure. MCP lets the agent reach systems; skills tell the agent how to use that access well.
Do I need MCP to use agent skills?
No. A skill can work with local files and shell tools. MCP becomes useful when the skill needs external data, such as analytics, browser state, docs, or customer systems.
Do I need skills if I already have MCP?
Yes, if the work matters. MCP gives the agent tools, but the skill gives it judgment, constraints, and a checked output format.
Which should a founder build first?
Build the skill first when the workflow is unclear. Add MCP when the workflow is clear but needs connected data or actions.
Can MCP and skills conflict?
They can if permissions are too broad and the skill does not name safety rules. Keep the skill explicit about what the agent can change, send, publish, or spend.
How does Tin Computer use both ideas?
Tin Computer uses connected tools for evidence and actions, then uses operating rules to turn that access into growth tasks, pull requests, measurements, and founder decisions.

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