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create_dashboard

Build or update a Streamlit dashboard from Python code, with automatic dependency installation and app reloading.

Instructions

Creates or updates the Streamlit dashboard with the given Python code.
Automatically installs missing dependencies and starts/reloads the app.

IMPORTANT: The variable DB_PATH is automatically injected at the top of
your code — always connect to the database using:
    conn = sqlite3.connect(DB_PATH)
Never hardcode a database path string yourself.

Args:
    code: Full Python source code for the Streamlit dashboard

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations, so the description carries the full load. It discloses helpful side effects (auto-install dependencies, automatic DB_PATH injection) and the create-or-update dual behavior, but omits permission needs, error modes, and whether the update replaces the whole app or merges. Partial behavioral coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the action, then the caveat about DB_PATH. The 'Args' block is boilerplate but short. Some redundancy between the first sentence and the later 'starts/reloads the app' clause, but overall efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists so return values need not be explained. The description covers the key constraint (DB_PATH) and high-level behavior, but does not address what happens on failure, dependency install scope, or how to recover a bad deploy, which matters for an agent invoking a code-writing tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and the only param is described minimally as 'Full Python source code for the Streamlit dashboard.' The important detail about DB_PATH injection is stated as prose rather than tied to the code parameter, and there is no guidance on structure, length, or modules allowed. With low coverage the description should compensate more.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (creates or updates) and resource (Streamlit dashboard) with the input that drives it (Python code). It does not explicitly contrast with siblings like read_dashboard or stop_dashboard, but the lifecycle role is clear from the phrasing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage through the code argument and the mention of reloading an existing app, but never states when to use this tool versus stop_dashboard/read_dashboard or what prerequisites exist. Usage is inferable rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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