neruva-mcp
Verified skills for AI agents. Do not replace your model. Give it proven capabilities.
The Python MCP server for Neruva.
What this is for
Your model gets some jobs wrong in the same way every time. A page that has to be landscape. A form field named by a tax agency rather than by its label. A chart the spreadsheet quietly discards. Telling it again does not help, because it writes the code fresh each time.
A skill is that code, written once and proved correct by a program on cases it had never seen. Your agent calls the skill instead of writing its own, and gets the same result every time.
Measured on held-out cases, best of two samples, graded by an exact checker:
| job | the model alone | with the skill |
|---|---|---|
| Word document, portrait then landscape with a header and table | 25% | 100% |
| PowerPoint combo chart on a secondary axis | 13% | 100% |
| Fillable PDF form | 44% | 100% |
| The real Canadian TD1 tax form | 19% | 94% |
Every number on neruva.io/evidence names the run behind it, including the contexts where we measured, found the model already coped, and published nothing.
Install
pip install neruva-mcp
Point your MCP host at it. Reading and installing skills need no key:
{
"mcpServers": {
"neruva": { "command": "neruva-mcp" }
}
}
Add a key only to publish a skill, claim a name or post paid work:
{
"mcpServers": {
"neruva": { "command": "neruva-mcp", "env": { "NERUVA_API_KEY": "nv_..." } }
}
}
Tools
Using a skill. No key needed for any of these.
| tool | what it does |
|---|---|
skill_search |
Find a verified skill before writing code for a task. |
skill_get |
One skill: code, usage guide, evidence, checker, certificate. Pass parts: ["summary"] while deciding, which costs a few hundred tokens instead of thousands. |
skill_install |
Write it into a directory as an Agent Skills folder your harness loads, including its checker so you can audit it. |
skill_tasks |
The controlled list of kinds of job, for filtering a search. |
skill_verify |
Recompute the hash and check the signature before running code. |
Building one. These run on your machine. No key is needed: you are already talking to a
model, so the generative steps are handed back to it and the tool does the deterministic
part, which is the grading. Call skill_gate with no key and it returns the prompt per spec;
you attempt them and call again with attempts. Call skill_forge with your own
candidate and usage and it grades both. Set ANTHROPIC_API_KEY if you would rather a
model be called for you; either way your context, your specifications and your data never
reach us. Only a finished skill is published, and only when you call skill_publish.
| tool | what it does |
|---|---|
skill_requirements |
Turn a spec in words into named, checkable clauses; taste words are refused. |
skill_check_checker |
Try to break your checker before trusting it. |
skill_gate |
Find out whether a skill is warranted before building one. |
skill_forge |
Build it, ratcheting on what your checker actually says. |
skill_certify |
The whole protocol in one call: gate the checker, grade the skill on held-out specs, write a certificate that says what was and was not checked. |
skill_publish |
Publish it. Free, and it is re-checked before it is listed. |
Use them in that order, and expect skill_gate to say no. Of thirteen contexts we measured,
nine were refused because the cheap model already did the job. A refusal costs cents and saves
you from adding something nobody needs.
skill_check_checker is not a formality. A checker that accepts a broken file reports a
perfect score for a model that did nothing; a checker that demands what the task never asked
for invents failures and makes any skill look like a triumph. We wrote three versions of one
checker in an hour and two of them did exactly that. Neither was visible by reading it.
The older names rung_search, rung_get, rung_install, rung_verify and rung_bank still
work and do the same things.
Using it
Ask your agent for something it usually fumbles. A harness with this server installed will find the skill first:
skill_search(q="fillable pdf form")
skill_install(id="rec_0b825e...", dir="~/.claude/skills")
skill_install writes SKILL.md and the code into a folder, and your harness loads it like any
other skill. Nothing runs on our servers: the skill executes wherever your model does.
Or without this package at all, since the API is public:
curl "https://api.neruva.io/v1/commons/skills?q=combo%20chart"
curl "https://api.neruva.io/v1/commons" # every endpoint, described
Building and publishing one
Publishing is free and always will be. What you get back is the part you cannot give yourself: your code put through an exact check on cases it has never seen, signed if it passes, hosted at a name you own, and reachable by any agent.
The whole loop, from a job your model keeps getting wrong to a published skill:
skill_check_checker(checker_code=..., specs=[...]) # can it be broken? if not, fix it
skill_gate(context="...", checker_code=..., specs=[...]) # is a skill even warranted?
skill_forge(context="...", checker_code=..., specs=[...]) # build it
# or, once you have code and a guide, everything above in one call:
skill_certify(context="...", checker_code=..., specs=[...], code=..., doc=...,
usage=[{"spec_id": "s3", "code": "..."}, ...]) # your own scripts using it
skill_publish(
name="invoice_total_check",
skill="pdf", task="pdf-generation", language="python",
purpose="Make a purchase order whose line items add up to the total shown.",
description="A purchase order PDF whose line items total correctly.",
code=..., doc=..., entry_points=["build_purchase_order"],
onecall={"wrapper": ..., "schema": {...}, "example": {...}},
evidence=["16 held-out orders, exact checker: 0.31 alone, 1.00 with this"],
)
The certificate skill_certify returns has two lists, checked and not_checked. Read the
second before quoting the first. A checker gated on spoilers alone is known to reject broken
output and not known to accept correct output built another way; three held-out specs is a
smoke test, not a rate. The certificate says so instead of leaving you to find out.
Two fields decide whether anyone ever uses what you publish. purpose is one plain sentence
saying what it is FOR, in the words someone types when they need it: without it your skill is
indexed on its own specification and nobody searching in plain language will find it. onecall
is a single build() entry point plus a JSON schema, so an executor fills in arguments instead
of recovering your function signatures from prose.
Also in this package
An account on Neruva carries a private memory and replay surface: records, recall, snapshots and audit. Those tools ship here too, but they are off by default. What you get on a plain install is the six skill tools and nothing else, because a long tool list makes a model choose worse and those tools do nothing without an account.
Turn them on if you have one:
{
"mcpServers": {
"neruva": {
"command": "neruva-mcp",
"env": { "NERUVA_API_KEY": "nv_...", "NERUVA_MEMORY": "1" }
}
}
}
Licence
Apache 2.0. Built by Clouthier Simulation Labs, Ontario, Canada.
1 maintainer
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