mcp-otle
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| prompts | {
"listChanged": true
} |
| resources | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| view_menuA | View the full Chipotle menu. Start here before ordering. |
| build_entreeB | Build a Chipotle entree with your preferred ingredients. This is the core of any order. |
| place_orderC | Place a complete Chipotle order. Provide your entree details, optional sides, and drinks. |
| check_order_statusB | Check the status of a previously placed order. |
| get_nutrition_factsC | Get (totally real and not at all made up) nutrition facts for a menu item. |
| customize_orderC | Make special requests for your order. Results may vary. |
| optimize_guac_roiA | Determines if adding guacamole is economically rational given your current financial and emotional state. |
| burrito_integrity_checkA | Uses proprietary tortilla stress models to predict whether your burrito will structurally fail during consumption. |
| line_time_forecastC | ML-powered burrito congestion prediction. Uses advanced algorithms to estimate Chipotle line wait times. |
| post_gym_macro_modeB | Automatically configures a nutritionally optimized burrito based on your workout. Gains-as-a-Service. |
| chipotle_personality_testA | Scientifically determines what kind of Chipotle eater you are based on your order preferences. |
| salsa_risk_assessmentA | Predicts the gastrointestinal consequences of your salsa choices. Consult your doctor before using this tool. |
| bowl_vs_burrito_decision_engineC | AI-powered format selection engine. Eliminates the most agonizing decision in fast casual dining. |
| daily_chipotle_limit_guardB | Protects users from excessive Chipotle consumption. A wellness tool for the burrito-dependent. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| recommend_order | Get a personalized Chipotle order recommendation based on your mood. |
| rate_my_order | Get your Chipotle order roasted (or praised) by an AI. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| menu | |
| hours |
TDQS
Scored across 14 tools
Every tool has a distinct and clearly defined purpose within the Chipotle ordering domain, with no overlap or ambiguity. Tools like 'burrito_integrity_check', 'salsa_risk_assessment', and 'optimize_guac_roi' target unique, specific functions that are easily distinguishable from core ordering tools like 'place_order' or 'view_menu'.
All tool names follow a consistent verb_noun or noun_verb pattern with clear, descriptive naming (e.g., 'view_menu', 'place_order', 'check_order_status'). There are no deviations in style or convention, making the set highly predictable and readable.
With 14 tools, the server is well-scoped for its humorous yet comprehensive Chipotle ordering and experience domain. Each tool serves a specific, justified role, from core ordering functions to novelty features, without feeling excessive or insufficient for the intended purpose.
The tool set provides complete coverage for the Chipotle ordering lifecycle, including menu viewing ('view_menu'), order building ('build_entree'), customization ('customize_order'), placement ('place_order'), status checking ('check_order_status'), and even whimsical extensions like nutrition and wellness tools, leaving no obvious gaps.