The Credit Optimizer v5 MCP server helps you optimize AI agent prompts to reduce credit consumption (e.g., on Manus) with zero quality loss. It offers three core functions:
analyze_prompt: Submit any AI agent prompt to receive a full optimization analysis — including recommended strategy, model selection (Standard vs. Max), estimated credit savings, quality impact assessment, and efficiency directives.
get_strategy_for_task: Get the optimal execution strategy and model recommendation for a specific task type: qa, code, research, content, data_analysis, media, or automation.
get_golden_rules: Retrieve the 10 audited golden rules for credit optimization, each with detailed explanations, guaranteed to preserve output quality.
The server also helps identify common credit-wasting patterns like wrong model routing or unnecessary agent mode for simple tasks. It is free and compatible with Claude Desktop, Cursor, Windsurf, Copilot, and other MCP clients.
Zero downsides. 47% average savings. Pays for itself in ~36 prompts. Audited across 53 adversarial scenarios. Quality Veto Rule: if it can't save without loss, it doesn't touch your task.
Get it: Landing Page · Gumroad ($12) · Power Bundle ($12) · SkillFlow Free MCP Server: PyPI · Smithery
Dev.to · PulseMCP · Smithery · MCPServers.org · Agensi.io · Indie Hackers · Awesome Manus AI
Manus charges credits per task. Most users waste 30–75% because of four default behaviors:
Problem | What Happens | Waste |
Wrong model routing | Simple tasks run in Max mode when Standard produces identical results | Up to 5x overpay |
Slow browser navigation | Each page takes 8–45 seconds via browser tool calls, burning credits on wait time | 150+ seconds per 10 URLs |
No chat detection | Tasks that cost $0 in Chat Mode (Q&A, brainstorm, translation) run in Agent Mode | 100% overpay |
Context bloat | Tokens accumulate across steps — each subsequent step costs more than the last | Exponential growth |
These aren't bugs. They're default settings. The Power Stack overrides all four.
Related MCP server: ComputeGauge MCP
This is the most important thing to understand:
The Power Stack can never make things worse. It has a hardcoded Quality Veto Rule: if an optimization would reduce output quality by even 1%, it skips that optimization entirely. In the worst case, it does nothing. In the best case, it saves you 75%.
There is no scenario where installing the Power Stack produces a worse result than not having it.
Analyzes every prompt before execution and decides the cheapest path that delivers identical quality.
Intent Classification across 12 categories (code, research, creative, data, translation...)
Model Routing: Standard vs Max — only uses Max when complexity genuinely requires it
Chat Mode Detection: Routes simple Q&A to Chat Mode ($0 cost)
Batch Detection: Groups related tasks to reduce overhead
Context Hygiene: Compresses accumulated context to keep token costs linear
Replaces slow browser tool calls with a programmatic toolkit.
httpx + selectolax replaces Playwright overhead (30–2,000x faster)
Browser Cookie Bridge for authenticated sites
Async parallel fetching — 10 URLs in 1.3 seconds instead of 150+ seconds
Intelligent disk caching with TTL — repeated visits are instant
Credit Optimizer decides what to optimize. Fast Navigation decides how fast to execute it. Together, they multiply:
Metric | Without Stack | With Stack | Improvement |
Simple Q&A cost | Full price | $0 (Chat Mode) | 100% saved |
10-URL research | 150+ seconds | 1.3 seconds | 115x faster |
Web scraping task | Full credits | 33% of original | 67% saved |
Full-stack web app | Full credits | 40% of original | 60% saved |
Research report | 12 minutes | 2 minutes | 6x faster |
Average across all tasks | Baseline | 47% less | 47% saved |
The Power Stack costs $9 one-time (or $12 for the bundle with Fast Navigation). Here's how fast it pays for itself:
Plan | Monthly Cost | 47% Wasted | Daily Waste | Payback |
Plus ($39/mo) | 6,500 cr/day | ~3,055 cr/day | ~$0.44/day | ~20 prompts |
Max ($99/mo) | 16,250 cr/day | ~7,637 cr/day | ~$0.92/day | ~10 prompts |
Teams ($79/user/mo) | Varies | ~47% | Varies | 1–2 days |
After payback, every prompt saves you money. For the rest of your Manus subscription. Forever.
Annual savings estimate: ~$500–$1,000+ depending on usage.
Copy skill files to ~/skills/credit-optimizer/ and ~/skills/fast-navigation/
Both activate automatically on every task — no configuration needed
pip install mcp-credit-optimizer
python -m mcp_credit_optimizerAdd to your MCP config:
{
"mcpServers": {
"credit-optimizer": {
"command": "python",
"args": ["-m", "mcp_credit_optimizer"]
}
}
}Works with Claude Desktop, Cursor, Windsurf, Copilot, and any MCP-compatible client.
Why pay when the MCP server is free? The MCP server saves credits when you remember to call it. The Manus Skill saves credits on every single prompt automatically — no manual invocation needed. The Skill also includes Fast Navigation (115x speed boost), which is not available as MCP.
All 53 test scenarios pass with zero quality degradation:
Category | Scenarios | Quality Loss |
Code generation (Python, JS, React, SQL) | 12 | 0% |
Creative writing (blog, marketing) | 8 | 0% |
Data analysis (CSV, JSON, API) | 7 | 0% |
Research (multi-source synthesis) | 6 | 0% |
Translation & localization | 5 | 0% |
Bug fixing & debugging | 5 | 0% |
Documentation generation | 5 | 0% |
Mixed-intent tasks | 5 | 0% |
"Everyone hates the Manus credit-based system. It's insane how fast credits burn." — Reddit, 90 upvotes
"I used 100,000 credits in a week and a half. That's my entire monthly allocation gone." — Reddit
"Manus is so expensive it's absurd. $200/month and credits still run out." — Reddit
The Power Stack exists because these frustrations are real — and fixable.
Awesome Manus AI — Curated list of Manus AI resources, tools, and tips
Pricing Guide 2026 — Complete Manus AI pricing breakdown
Standard vs Max Guide — When to use each mode
Tutorial — Complete beginner's guide
Glossary — 30+ Manus AI terms explained
Roadmap — What's coming next
Channel | URL |
Landing Page | |
Buy Individual ($9) | |
Buy Bundle ($12) | |
SkillFlow | |
PyPI (Free MCP) | |
Smithery | |
PulseMCP | |
Awesome List |
MIT — see LICENSE for details.
Built by Rafael Silva · creditopt.ai
Analyze an AI agent prompt and return optimization recommendations.
Returns strategy, model recommendation, estimated credit savings, quality impact assessment, and efficiency directives.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The user's prompt/task description to analyze |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It mentions what it returns but lacks details on side effects, idempotency, or prerequisites.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with action and result. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given one parameter and presence of output schema, description covers purpose and what is returned completely.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema coverage, baseline is 3. Description does not add meaning beyond the schema's description of the 'prompt' parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it analyzes an AI agent prompt and returns optimization recommendations, listing specific return fields. Distinguishes from siblings get_golden_rules and get_strategy_for_task.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied but not explicitly stated. No guidance on when to use this tool versus siblings or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the 10 Golden Rules for credit optimization with ZERO quality loss.
Returns: The 10 audited golden rules with explanations
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the full burden of behavioral disclosure. It does not confirm the tool is read-only or safe, nor does it describe any side effects, authorization needs, or rate limits. The claim of 'ZERO quality loss' pertains to the rules, not the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is only two sentences, front-loads the purpose, and contains no extraneous information. Every word serves a purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no parameters, clear retrieval) and the existence of an output schema, the description adequately explains what the tool returns: 'the 10 audited golden rules with explanations.' No further context seems necessary.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters and 100% coverage, so baseline is 4. The description does not need to add parameter-level detail, and it appropriately confirms no arguments are required.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Get the 10 Golden Rules for credit optimization with ZERO quality loss,' which specifies the exact resource (10 Golden Rules) and the action (Get). This distinguishes it from sibling tools analyze_prompt and get_strategy_for_task.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or exclusions. It simply states what it returns.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the optimal strategy for a specific task type.
| Name | Required | Description | Default |
|---|---|---|---|
| task_type | Yes | One of: qa, code, research, content, data_analysis, media, automation |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It only states it 'gets' a strategy, but does not disclose any behavioral traits like whether it is read-only, if there are side effects, or what the output entails. Minimal behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, concise and front-loaded. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Simple tool with one param and output schema, but description does not mention output or any constraints. Lacks context about behavior and return value, despite output schema existing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has one parameter with description listing allowed values, coverage is 100%. Description adds no additional meaning beyond schema. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the verb 'get' and resource 'strategy' for a specific task type. Input schema defines task_type with allowed values. It is distinct from sibling tools 'analyze_prompt' and 'get_golden_rules'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied but not explicitly stated. No guidance on when to use this tool versus alternatives, nor any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v5.0.0analyze_prompt1 field changedInput schema / properties / prompt / descriptionAdded value: +"The user's prompt/task description to analyze"get_strategy_for_task1 field changedInput schema / properties / task_type / descriptionAdded value: +"One of: qa, code, research, content, data_analysis, media, automation"v5.2.0analyze_promptget_golden_rulesget_strategy_for_taskEach tool targets a distinct aspect of credit optimization: analyzing prompts, retrieving golden rules, and obtaining task-specific strategies. There is no overlap in functionality.
All tool names follow a consistent verb_noun pattern in snake_case (analyze_prompt, get_golden_rules, get_strategy_for_task), making them predictable and easy to understand.
With 3 tools, the server is slightly minimal but still well-scoped for its advisory purpose. Each tool serves a clear role, and the count is reasonable given the focused domain.
The tools cover the core functionalities of analysis, reference rules, and strategy selection. A minor gap is the lack of an execution tool, but for an advisory server this is acceptable.
Unclaimed servers have limited discoverability.
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