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Agent skill

Verify Local MCP

by every-app in every-app/open-seo

Verify the OpenSEO MCP server end-to-end on a local dev server — protocol-level correctness against real DataForSEO, then a headless-agent consumer probe that tests tool ergonomics (descriptions…

MITAuto-check: notesAgent Workflows

Install Verify Local MCP

skills CLI
$ npx skills add every-app/open-seo --skill verify-local-mcp -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install every-app/open-seo verify-local-mcp --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/every-app/open-seo.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/verify-local-mcp .claude/skills/verify-local-mcp && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
verify-local-mcp
GitHub stars
23k
Token cost
~1.3k tokens
SKILL.md length
570 words
Files
1
Skills in repo
19
Repo updated
First seen
Licence
MIT

At a glance

Verify the OpenSEO MCP server end-to-end on a local dev server — protocol-level correctness against real DataForSEO, then a headless-agent consumer probe that tests tool ergonomics (descriptions…

  • Works in 5 steps: Boot → Protocol smoke (cheap, deterministic) → Consumer probe (the ergonomics test) → …
  • Tasks that involve MCP servers
  • SKILL.md covers 1. Boot, 2. Protocol smoke (cheap,…, 3. Consumer probe (the… and 4. Ergonomics rubric — what…, plus 1 more section
  • Calls curl, claude and pnpm; needs DATAFORSEO_API_KEY

What it does

Verify Local MCP is an agent skill from every-app/open-seo. Verify the OpenSEO MCP server end-to-end on a local dev server — protocol-level correctness against real DataForSEO, then a headless-agent consumer probe that tests tool ergonomics (descriptions, schemas, output size, errors, async flows) without the maintainer manually driving an MCP client. Use after adding or changing MCP tools, or when asked to check that the MCP "works" or "is ergonomic".

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering MCP servers. It works with Model Context Protocol. The repository describes itself as: Open source alternative to Semrush and Ahrefs. The licence is MIT.

When your agent uses it

  • Tasks that involve MCP servers

Example prompts

  • “is ergonomic”
  • “/verify-local-mcp”

Requirements

  • A credential in DATAFORSEO_API_KEY

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Boot
  2. Protocol smoke (cheap, deterministic)
  3. Consumer probe (the ergonomics test)
  4. Ergonomics rubric — what feedback to act on
  5. Iterate and clean up

What it can do on your machine

Read from SKILL.md and the folder at commit 89e5a00. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • curl
    • claude
    • pnpm

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use curl and pnpm, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • DATAFORSEO_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Verify Local MCP loads about 1.3k tokens when it runs. Until then it costs about 103 tokens; SKILL.md has 570 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~103
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:14
    - `.env.local` needs `AUTH_MODE=local_noauth` and `DATAFORSEO_API_KEY` (base64 of `login:password`). Never print the key

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from every-app/open-seo at commit 89e5a00, republished under its MIT licence (© every-app). 570 words, ~1,349 tokens.

Download SKILL.mdSave it as .claude/skills/verify-local-mcp/SKILL.md (or your agent's skills folder).
name
verify-local-mcp
description
Verify the OpenSEO MCP server end-to-end on a local dev server — protocol-level correctness against real DataForSEO, then a headless-agent consumer probe that tests tool ergonomics (descriptions, schemas, output size, errors, async flows) without the maintainer manually driving an MCP client. Use after adding or changing MCP tools, or when asked to check that the MCP "works" or "is ergonomic".
metadata.internal
true

Verify local MCP

Two layers, in order. The protocol layer proves the server and provider behave; the consumer layer proves an agent that has never seen the code can use the tools well. They catch different bugs — protocol testing found DataForSEO quirks (zoom-dependent empty SERPs), the consumer probe found ergonomics failures (9KB provider rows overflowing client token budgets, fractional inputs rejected upstream with raw provider errors). Do both.

1. Boot

  • .env.local needs AUTH_MODE=local_noauth and DATAFORSEO_API_KEY (base64 of login:password). Never print the key.
  • Start pnpm dev:agents in the background. The server URL is branch-prefixed: http://<branch-suffix>.open-seo.localhost:1355 (the exact URL is printed on boot; logs tee to .logs/dev-server.log).
  • With local_noauth, /mcp needs no token. Vite hot-reloads server code, so fix → re-call without restarting.
  • After changing a tool's input or output schema, refresh the client's tool discovery (tools/list) or reconnect the MCP client before calling it again. Clients can cache validators from the previous tool list and reject valid results after the provider call has already incurred a charge.

2. Protocol smoke (cheap, deterministic)

Raw JSON-RPC against /mcp — the layer for asserting exact shapes and driving edge cases (resume taskIds, empty results, invalid inputs):

bash
curl -sS http://<url>/mcp \
  -H 'content-type: application/json' -H 'accept: application/json, text/event-stream' \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'
# tools/call: {"method":"tools/call","params":{"name":"<tool>","arguments":{...}}}
  • Bootstrap: list_projects, then create_project if empty — most tools need a projectId.
  • Test the happy path AND at least one edge per changed tool: an empty result (obscure query), an invalid identifier, and for queued tools the full lifecycle including resuming with the returned taskId.
  • These are real, billed DataForSEO calls (metering itself short-circuits in local_noauth — billing needs unit tests, not this). Keep depths 10–20.

3. Consumer probe (the ergonomics test)

Spawn a headless Claude subprocess connected as a real MCP client. Write a config:

json
{
  "mcpServers": {
    "openseo-local": { "type": "http", "url": "http://<url>/mcp" }
  }
}

Then run a NATURAL task — never name the tools; whether the model finds them from descriptions alone is the test:

bash
claude -p "<natural task a customer would ask>. Keep spend minimal: depths 10-20, one 3x3 grid max, ~10 paid calls.
Deliver two sections: 1. FINDINGS — the task result. 2. MCP FEEDBACK — critique the MCP as a first-time consumer:
were descriptions enough to pick tools without trial and error? confusing schemas, surprising output shapes or sizes,
unclear errors, credit-cost surprises? Did async/taskId flows behave as described? List anything that made you hesitate or retry." \
  --mcp-config mcp-local.json --strict-mcp-config \
  --allowedTools "mcp__openseo-local,mcp__openseo-local__*" \
  --model sonnet --max-turns 30

Use --model sonnet as the typical-client proxy — if sonnet navigates it cold, weaker clients likely can too. Read FINDINGS for correctness (did it get real, sensible data?) and MCP FEEDBACK for the rubric below.

Show full SKILL.md (242 more words)Show less

4. Ergonomics rubric — what feedback to act on

  • Tool selection: the probe should pick the right tool first try. Retries or wrong-tool detours mean a description needs a sharper "use this when / not this" sentence.
  • Schemas: every constraint the provider enforces silently must be in the field's .describe() (units, whole-number requirements, defaults, what's ignored when). If the probe guessed-and-retried an input, encode the rule server-side (coerce/round) or document it — prefer coercing.
  • Output size: budget roughly a few KB per row. Provider rows carrying popular_times/attribute trees/photo URLs must be trimmed to the fields the tool's job needs; point to the single-entity tool for the full shape.
  • Errors: actionable, never a raw upstream field name without a hint at the fix. Failures after a billed step must keep the recovery handle (e.g. the taskId) in the message.
  • Async copy: descriptions must match typical latency ("usually completes within this call") and the resume path must actually work when driven by the probe, not just by curl.
  • Credit honesty: each description's credit sentence matches reality, including cache-hit and resume paths.

5. Iterate and clean up

Fix findings → hot-reload picks them up → re-verify just the changed behavior via curl (cheap) → rerun the full consumer probe once per iteration round (it re-tests selection and flow, not just the fix). When done: stop the dev server background task, run the repo's tests/ci:check, and fold genuine provider quirks into code comments or tests so the next agent doesn't rediscover them.

© every-app, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/verify-local-mcp of every-app/open-seo.

Open the folder on GitHubat commit 89e5a00

Compare with similar skills

Verify Local MCP next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Verify Local MCP compared with similar skills
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Verify Local MCP this skillevery-app/open-seo23k—~1.3kAutomated safety check: NotesMIT
MCP Server Builderanthropics/skills180k63 repos~2.3kAutomated safety check: PassApache-2.0
MCP Server BuildershareAI-lab/learn-claude-code78k5 repos~1.2kAutomated safety check: PassMIT
MCP Integration for Pluginsanthropics/claude-plugins-official38k11 repos~3.1kAutomated safety check: PassApache-2.0
Crush Configurationcharmbracelet/crush29k—~3.7kAutomated safety check: PassCustom licence
Context Mode Output Sandboxmksglu/context-mode26k—~4.1kAutomated safety check: PassCustom licence

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Works with

Categories

Questions about Verify Local MCP

What does Verify Local MCP do?

Verify the OpenSEO MCP server end-to-end on a local dev server — protocol-level correctness against real DataForSEO, then a headless-agent consumer probe that tests tool ergonomics (descriptions…. Verify Local MCP is an agent skill from every-app/open-seo. Verify the OpenSEO MCP server end-to-end on a local dev server — protocol-level correctness against real DataForSEO, then a headless-agent consumer probe that tests tool ergonomics (descriptions, schemas, output size, errors, async flows) without the maintainer manually driving an MCP client.

When should I use Verify Local MCP?

Verify Local MCP fits situations like: tasks that involve MCP servers.

How do I install Verify Local MCP in Claude Code?

Run `npx skills add every-app/open-seo --skill verify-local-mcp -a claude-code`. Or copy the skill folder (.agents/skills/verify-local-mcp in every-app/open-seo) into .claude/skills/verify-local-mcp in your project. Claude Code loads it when a task matches its description.

How do I install Verify Local MCP in Codex?

Run `npx skills add every-app/open-seo --skill verify-local-mcp -a codex`. Or copy the skill folder (.agents/skills/verify-local-mcp in every-app/open-seo) into .agents/skills/verify-local-mcp in your project. Codex loads it when a task matches its description.

Can I use Verify Local MCP in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add every-app/open-seo --skill verify-local-mcp -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/verify-local-mcp, .gemini/skills/verify-local-mcp, .github/skills/verify-local-mcp and .opencode/skills/verify-local-mcp in your project.

What does Verify Local MCP need to run?

Going by SKILL.md and its folder, Verify Local MCP needs the command-line tools its instructions call (curl, claude and pnpm) and credentials named DATAFORSEO_API_KEY. Our summary lists: A credential in DATAFORSEO_API_KEY.

Does Verify Local MCP access the network?

SKILL.md contains no URLs. Its commands use curl, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Verify Local MCP safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Verify Local MCP use?

Verify Local MCP is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Verify Local MCP use?

About 1.3k tokens (SKILL.md is roughly 5.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Verify Local MCP?

Skills that share tags, products or a category with Verify Local MCP: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 38k stars) and Crush Configuration (charmbracelet/crush, 29k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Verify Local MCP?

every-app (a GitHub organization) maintains it in every-app/open-seo, which has 22,828 GitHub stars. The repository holds 19 skills in this directory. The repository was last updated on October 8, 2026.

Source: every-app/open-seo on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.