Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/jketreno/clare/mcp-servergit clone --depth 1 https://github.com/jketreno/clareWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/jketreno/clare/mcp-server)<a href="https://agentmods.dev/commands/jketreno/clare/mcp-server"><img src="https://agentmods.dev/badge/commands/jketreno/clare/mcp-server.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00017 | $0.02842 |
| Opus 5 | $0.00009 | $0.01421 |
| Sonnet 5 | $0.00003 | $0.00568 |
| Haiku 4.5 | $0.00002 | $0.00284 |
Grade A, and why
mcp-server scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
import { execSync } from 'child_process'; How it starts
The opening of the file, as written. The whole thing — 363 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLARE MCP Server
What this skill does: Scaffolds a minimal MCP (Model Context Protocol) server that exposes CLARE's enforcement primitives —
verify-ci.shandautonomy.yml— as typed tool calls. Any MCP-compatible agent or orchestrator can then call CLARE tools without needing bash access or CLARE-specific prompt engineering.When to use: When running multi-agent pipelines, headless agents, or any workflow where you need CLARE enforcement available as a structured tool rather than a bash script.
Output: A
mcp/directory containing a runnable MCP server + registration instructions.
Context
CLARE's two core enforcement primitives are:
clare/verify-ci.sh— runs all CI checks, exits non-zero on failureclare/autonomy.yml— YAML file mapping file paths to autonomy levels
This skill exposes them as three MCP tools:
| Tool | Input | Output |
|---|---|---|
clare_verify |
(none) | {status, passed[], failed[{check, output}], summary} |
clare_check_autonomy |
{path: string} |
{path, matched_rule, level, reason} |
clare_list_humans_only |
(none) | {humans_only_paths: string[]} |
Instructions
When this skill is invoked, generate a CLARE MCP server for the current project.
Step 1: Detect the project runtime
Check for package.json → generate Node.js server.
Check for pyproject.toml or requirements.txt → generate Python server.
If both exist, ask the user which runtime to use.
If neither, default to Node.js.
Step 2: Scaffold the server
For Node.js — create mcp/clare-server.js:
#!/usr/bin/env node
// @generated — regenerate from clare/templates/skills/mcp-server.md, do not hand-edit
//
// CLARE MCP Server
// Exposes CLARE enforcement primitives as MCP tool calls.
// See docs/agentic.md for usage in multi-agent pipelines.
import { Server } from '@modelcontextprotocol/sdk/server/index.js';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
import { CallToolRequestSchema, ListToolsRequestSchema } from '@modelcontextprotocol/sdk/types.js';
import { execSync } from 'child_process';
import { readFileSync } from 'fs';
import { resolve, dirname } from 'path';
import { fileURLToPath } from 'url';
import yaml from 'js-yaml';
const __dirname = dirname(fileURLToPath(import.meta.url));
const PROJECT_ROOT = resolve(__dirname, '..');
const server = new Server(
{ name: 'clare', version: '1.0.0' },
{ capabilities: { tools: {} } }
);
server.setRequestHandler(ListToolsRequestSchema, async () => ({
tools: [
{
name: 'clare_verify',
description: 'Run clare/verify-ci.sh and return structured pass/fail results. Call this after any code generation before reporting work complete.',
inputSchema: { type: 'object', properties: {}, required: [] }
},
{
name: 'clare_check_autonomy',
description: 'Look up the autonomy level for a file path in clare/autonomy.yml. Call this before modifying any file.',
inputSchema: {
type: 'object',
properties: {
path: { type: 'string', description: 'File path relative to project root' }
},
required: ['path']
}
},
{
name: 'clare_list_humans_only',
description: 'List all humans-only paths from clare/autonomy.yml. Call this as a pre-flight check before delegating tasks to sub-agents.',
inputSchema: { type: 'object', properties: {}, required: [] }
}
]
}));
server.setRequestHandler(CallToolRequestSchema, async (request) => {
const { name, arguments: args } = request.params;
if (name === 'clare_verify') {
try {
const output = execSync(`${PROJECT_ROOT}/clare/verify-ci.sh`, {
cwd: PROJECT_ROOT,
encoding: 'utf8',
stdio: ['pipe', 'pipe', 'pipe']
});
return {
content: [{ type: 'text', text: JSON.stringify({ status: 'passed', output, summary: 'All checks passed' }) }]
};
} catch (err) {
const output = err.stdout + err.stderr;
const failedChecks = (output.match(/❌ (.+)/g) || []).map(l => l.replace('❌ ', '').trim());
return {
content: [{ type: 'text', text: JSON.stringify({ status: 'failed', failed: failedChecks, output, summary: `${failedChecks.length} check(s) failed` }) }]
};
}
}
if (name === 'clare_check_autonomy') {
const targetPath = args.path;
const autonomyFile = readFileSync(`${PROJECT_ROOT}/clare/autonomy.yml`, 'utf8');
const autonomy = yaml.load(autonomyFile);
const modules = autonomy.modules || [];
let matched = modules.find(m => m.path !== '*' && targetPath.startsWith(m.path));
if (!matched) matched = modules.find(m => m.path === '*');
return {
content: [{
type: 'text',
text: JSON.stringify({
path: targetPath,
matched_rule: matched?.path || 'none',
level: matched?.level || 'unknown',
reason: matched?.reason || ''
})
}]
};
}
if (name === 'clare_list_humans_only') {
const autonomyFile = readFileSync(`${PROJECT_ROOT}/clare/autonomy.yml`, 'utf8');
const autonomy = yaml.load(autonomyFile);
const humansOnly = (autonomy.modules || [])
.filter(m => m.level === 'humans-only')
.map(m => m.path);
return {
content: [{ type: 'text', text: JSON.stringify({ humans_only_paths: humansOnly }) }]
};
}
throw new Error(`Unknown tool: ${name}`);
});
const transport = new StdioServerTransport();
await server.connect(transport);
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 363 lines · 17 tokens per session scan A 751c33cde387
mcp-server is a command published in the GitHub repository jketreno/clare (5 stars, last pushed 1mo ago), licensed MIT. It adds 17 tokens to every session and 2,842 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.