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Request External Resource

request_external_resource

Request the USER'S OWN external credential for this project — their OpenAI or Anthropic API key, an external Postgres connection string, or any other service's key (type GENERIC, e.g. Stripe/Resend — name the env vars via secret_env_vars; a var the user can only produce LATER, like a webhook signing secret, goes in optional_env_vars so the dialog doesn't demand it up front). NOT for Floot-managed resources (database/auth/push/oauth/…) — use provision_resource for those; they need no user input. REUSE FIRST: if the project owner already has a matching credential on their account (list_resources section 2), this connects it silently and returns the env var names — no link, no user action, nothing to poll. Pass the name exactly as list_resources shows it to make that happen. Reusing a POSTGRES credential also seeds helpers/db, installs the query stack, and pulls the typed schema helper, so do NOT write those yourself afterwards. Otherwise it returns a secure connect link: SHOW it to the user (UI-capable hosts render a Connect button automatically; on terminal hosts with shell access open it in the user's default browser yourself and paste the URL as plain text) and ask them to open it. The call completes only when the user finishes the connect flow — it never expires. Do NOT block on it: request the credential EARLY, keep building everything that doesn't need the secret (the env var names are known now — reference process.env.X in code before the secret exists), and check the request between tasks; the user may never connect it, and the build must not stall. NEVER ask the user to paste a secret into the chat. On completion you get the env var names — never the secret values. Re-calling with the same type returns the same pending request.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoDisplay name for GENERIC requests, e.g. 'Stripe'.
typeYes
projectIdYes
instructionsNoWhy the key is needed / where the user can find it — shown to the user in the dialog.
secret_env_varsNoGENERIC only: the env var name(s) the secret(s) should be exposed as (default RESOURCE_API_KEY). Every var listed here is a REQUIRED field in the connect dialog — the user cannot submit while one is blank, so only list vars the user can produce right now; anything they'd fill in later belongs in optional_env_vars instead.
optional_env_varsNoGENERIC only: env vars the user may leave blank at connect time and fill in later — e.g. a webhook signing secret that only exists after the webhook endpoint is created. Rendered as optional fields; names here need not repeat secret_env_vars (a name in both stays required). To collect a skipped value later, call request_external_resource again — the connected resource opens in an update dialog.

TDQS

A4.7/5.0
Behavior5/5

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

The annotations (readOnlyHint=false, destructiveHint=false, openWorldHint=false) convey minimal behavior, so the description carries the full burden and delivers: it discloses blocking semantics (completes only when the user finishes the connect flow, never expires, should not be blocked on), side effects (POSTGRES reuse seeds helpers/db and installs the query stack), idempotency (re-calling returns the same pending request), and the post-condition (returns env var names, never secret values). No contradiction with annotations.

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

Conciseness3/5

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

The description is a dense, single run-on paragraph exceeding 1,000 characters with many nested clauses and parentheticals (e.g. 'on terminal hosts with shell access open it in the user's default browser yourself and paste the URL as plain text'). The ordering is logical and the purpose is front-loaded, and every sentence earns its place given the tool's complexity, but the lack of visual structure (bullets, headers) makes it hard to parse for an agent scanning quickly.

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

Completeness5/5

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

Given the tool's high complexity and minimal annotations, the description is remarkably complete: purpose, exclusions, reuse path, side effects, connect-link display behavior per host type, non-blocking guidance, secret-handling policy, and return semantics (env var names, never values). With no output schema, it correctly explains what the call returns. Nothing an agent needs to call this correctly is missing.

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

Parameters4/5

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

Schema coverage is 67% with solid param descriptions for name, instructions, secret_env_vars, and optional_env_vars. The description adds meaning beyond the schema by explaining the GENERIC type semantics (type GENERIC naming env vars via secret_env_vars), clarifying the optional_env_vars vs secret_env_vars distinction, and revealing the POSTGRES side effects. projectId is self-evident ('this project'). Strong compensation on top of already-decent schema documentation.

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

Purpose5/5

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

The description states a specific verb+resource — requesting the user's own external credential (API keys, connection strings) for a project — and immediately distinguishes itself from provision_resource by exclusion ('NOT for Floot-managed resources...use provision_resource for those'). An agent can unambiguously tell this tool apart from its siblings without opening the schema.

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

Usage Guidelines5/5

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

Exceptionally explicit: names the alternative (provision_resource) and the exact condition selecting it, plus the 'REUSE FIRST' rule referencing list_resources section 2. It also gives operational timing guidance — request EARLY, don't block, check between tasks, never ask for a secret in chat. This is the gold standard of when-to-use/when-not-to-use documentation.

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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TDQS

A3.6/5.0
Disambiguation4/5

Tools are mostly distinct, but there is some overlap among file-modifying tools (edit_file, write_file, apply_patch) and between run_code_in_vm and run_code_in_browser. Detailed descriptions and clearly scoped use cases help agents select correctly.

Naming Consistency4/5

Most tools follow a verb_noun snake_case pattern (create_project, list_files, execute_sql), but a few deviate (apply_patch, card_upload_asset, run_code_in_vm). Overall readable and predictable, with only minor inconsistencies.

Tool Count2/5

With 46 tools, the server exceeds the typical well-scoped range and approaches the extreme threshold. While the broad scope of a full development platform justifies many tools, this count may overwhelm agents and increase misselection risk.

Completeness4/5

The tool surface covers the full development lifecycle: project creation, file operations, database management, resource provisioning, deployment, testing, and debugging. Minor gaps exist (e.g., no delete_project or checkpoint management), but core workflows are well-supported.

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