conda
Runs conda commands (list, info, env-list, create, remove, update) and returns structured JSON output.
This record as markdown: /tools/io-github-dave-london-pare-python/conda.md
What conda does on Python
AI agents invoke conda to trigger actions in Python. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
| Parameter | Type | Required | Description |
|---|---|---|---|
all | boolean | — | For update action, update all installed packages (--all) |
name | string | — | Environment name (used with 'list' action to list packages in a specific env) |
path | string | — | Working directory |
action | string | Yes | Conda action to perform |
prefix | string | — | Environment prefix path as alternative to name (--prefix DIR) |
compact | boolean | — | Prefer compact output |
packages | array | — | Packages used by create/remove/update actions |
packageFilter | string | — | Regex filter for conda list to show only matching packages |
Parameters from the server's own tool schema.
Why conda is rated High
The tool executes conda commands including environment creation, removal, and package updates. While 'remove' and 'update' could be considered destructive, the primary action is executing external conda commands whose effects vary by subcommand. The 'remove' subcommand can irreversibly delete environments, making this high severity.
From the tool's definition Runs conda commands (list, info, env-list, create, remove, update)
Risk signalsAccepts file system path (path)
Attacks that exploit this kind of access
The rule that runs conda safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Python, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For conda, this is the rule to start with:
conda stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Python, apply this rule, and every conda call is checked against it from then on.
Questions about conda
Runs conda commands (list, info, env-list, create, remove, update) and returns structured JSON output. It is categorised as a Execute tool in the Python MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
conda accepts 8 parameters: all, name, path, action, prefix, compact, packages, packageFilter. Required: action. The full parameter table on this page comes from the server's own tool schema.
Register the Python MCP server in PolicyLayer and add a rule for conda: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Python. Nothing to install.
conda is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the conda rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for conda. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
conda is provided by the Python MCP server (Dave-London/Pare). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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