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get_app_config

Reads a deployed app's persisted configuration, including deploy settings, environment key names, and AI bindings, while returning only secret references rather than secret values.

Instructions

Read a deployed app's daemon-persisted config (deploy fields + ai bindings).

    Secret values are never returned — only ``${secrets.X}`` refs and env
    key names. Each ``[ai.<name>]`` binding shown here is injected into the
    app at launch as ``NERDIT_AI_<NAME>_URL/_KEY/_MODEL``, and the
    ``default`` binding also sets ``OPENAI_BASE_URL/OPENAI_API_KEY/
    OPENAI_MODEL`` (drop-in OpenAI SDK).

    ``deploy.release`` is the command run once inside the new image before
    traffic moves (a failure aborts the deploy and reverts the image,
    never the data — write idempotent migrations); ``deploy.cutover`` null =
    zero-downtime swap when eligible, false = same-port swap. ``env_keys``
    are literal runtime env names; secret refs are accepted only by
    ``ai.*.api_key``, ``db.*.password``, ``edge_auth.password`` and a git
    ``token_ref`` — a ``${secrets.shared.KEY}`` there resolves at launch for
    any owner, so a human can place the secret and the agent only names it.

Input Schema

NameRequiredDescriptionDefault
nameYesName of a deployed app (``kind=service``). A served model or a managed database is a 404 ``config.not_an_app``; an unknown name a 404 ``not_found``.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior5/5

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

With zero annotations the description carries the full burden and does so richly: secret values are never returned (only ${secrets.X} refs and env key names), ai bindings are injected at launch as NERDIT_AI_<NAME>_* vars, deploy.release failures abort and revert the image, and cutover null vs false is spelled out. It also names which fields accept secret refs — high-value behavioral context an agent could not get elsewhere.

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?

Purpose is front-loaded in the first sentence and subsequent paragraphs map to real returned fields rather than restating the name. It is dense but mostly earns its space; the parenthetical '(write idempotent migrations)' is advisory padding that could be trimmed.

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

Completeness4/5

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

No output schema exists, so the description must convey the return shape — it covers deploy fields, ai bindings, env_keys, and secret refs, which is the bulk of what matters. It does not enumerate other config sections (db, domains, edge_auth) even though edge_auth.password is referenced, leaving the full response shape partially described.

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

Parameters3/5

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

Schema coverage is 100% and the single 'name' parameter already has a detailed description with 404 semantics, so the schema does the heavy lifting. The tool description adds no parameter-level information (no naming conventions or format), which is acceptable at this coverage level but earns only the baseline.

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?

Opens with a specific verb+resource+scope: 'Read a deployed app's daemon-persisted config (deploy fields + ai bindings)'. An agent knows exactly what it gets. It does not, however, distinguish itself from the nearby sibling 'get_config' or 'get_service', leaving the sibling boundary to inference.

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 phrase 'deployed app's' implicitly scopes usage to apps (kind=service) rather than models or databases, but the explicit exclusion only appears in the schema's name description, not the tool description. No alternative tool (get_config, get_service) is named, so when-not branches must be inferred.

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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