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Intelligent AI model routing with MCP server for Claude Desktop & Codex CLI. Auto-routes tasks to Claude, GPT, Gemini, DeepSeek, Llama with RL-based smart routing and cost optimization.
MMCP exhibits significant definition quality gaps. While tool names follow verb_noun conventions (analyze_task, plan_task, execute_task), descriptions vary widely in quality. Most tools have descriptions (10/15), but many lack parameter-level documentation. Critical issue: 5 tools (web_search, http_request, read_file, write_file, run_command) have minimal or missing parameter descriptions. Input schemas are present but incomplete, parameters lack type constraints (enums, ranges, patterns). Output schemas are undocumented across all tools. Error handling is absent; no recovery guidance or categorization. The run_command tool exposes a security risk with only a vague allowlist warning. Overall, definitions are functional but fall short of production-grade quality expected for agent tool composition.
Analyze a task's complexity, domain, and optimal model tier. Given a task description, returns: - Complexity tier (TRIVIAL/STANDARD/COMPLEX/FRONTIER) - Detected domain (code, math, creative, etc.) - Recommended model with justification - Alternative cheaper model with savings percentage - Estimated output tokens. Use this before executing a task to understand its requirements and which AI model provides the best value.
View/update MMCP configuration.
Actionable savings recommendations. Analyzes spending patterns and suggests cheaper models or tools.
Get spending summary over a period. Returns total tokens, cost, and breakdown by model.
Execute a task end-to-end: plan → route models → execute → return results. This is the full autonomous pipeline. It will: 1. Analyze the task complexity 2. Create an execution plan (3-6 steps) 3. Auto-select the optimal model for each step 4. Execute each step in order (respecting dependencies) 5. Track costs and return the final output
Output schemas undocumented across all 15 tools. LLMs cannot predict response structure, forcing them to guess at field names and types for downstream tool chaining.
Parameter descriptions missing or minimal for 5 tools (web_search query, http_request method/url/body, read_file path, write_file path/content, run_command command). LLMs cannot infer parameter semantics from names alone.
No input validation or error handling guidance. Tools lack recovery hints (e.g., 'If file not found, check path with list_files'). Errors will not guide LLM self-correction.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 57 | 2026-07-28+ | v2 |
Make an HTTP request to any URL
List saved reusable pipeline skills.
Which models create value vs. which are overkill. Analyzes task complexity vs. model tier used.
Create an execution plan for a complex task. Decomposes the task into 3-6 executable steps, each with: - The optimal AI model (auto-selected by complexity) - Model justification (why this model, not a cheaper one) - Tool requirements (web search, file ops, etc.) - Dependency chain (which steps feed into which)
Read contents of a local file
⚠️ Run a shell command (allowlisted only). SECURITY: Only allowlisted command prefixes are permitted. This is NOT a security boundary for production/multi-user use.
Discover the best tools for a given task. Analyzes the task and returns ranked tools, preferring free built-in tools over expensive MCP server tools. Returns tools with: name, type (builtin/MCP), relevance score, cost per call, and average latency.
Set daily spend cap.
Search the web using DuckDuckGo (free, no API key needed)
Write content to a local file
run_command tool exposes shell execution with only vague 'allowlisted only' warning. No explicit list of allowed commands, no sandboxing details, no audit trail declaration. Security boundary unclear.
Parameter constraints missing. No enums for period (cost_summary), action (configure), method (http_request). Free-form strings invite hallucinated invalid values.