This document explains how model-driven tools are added to AI Studio. Tool calling lets a model request a small, well-defined action during a chat or assistant run, such as searching the web or reading a web page.
Tools are part of the .NET app. They are not Lua plugins and they are not loaded dynamically from user folders. Adding a tool requires code changes.
## Architecture
A tool has two parts:
- A JSON definition in `app/MindWork AI Studio/wwwroot/tool_definitions/`
- A C# implementation of `IToolImplementation` in `app/MindWork AI Studio/Tools/ToolCallingSystem/ToolCallingImplementations/`
At startup, `ToolRegistry` reads all JSON definitions and matches each definition to a registered implementation by `implementationKey`. `ToolExecutor` runs the implementation when a provider returns a matching function call.
The provider only sees tools that are available for the current component, selected by the user or defaults, supported by the model, configured correctly, and allowed by the provider confidence rules. The shared tool-call loop limit is `ToolSelectionRules.MAX_TOOL_CALLS`, and all provider tool-call paths use that same limit.
The JSON definition in `wwwroot/tool_definitions` is the single source of truth for a tool. Do not create separate tool definition files for different provider APIs. Provider-specific request shapes are generated in code from the same `ToolDefinition`.
Chat Completions compatible APIs use a nested function shape:
```json
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location.",
"parameters": {},
"strict": true
}
}
```
The OpenAI Responses API uses a flat function shape:
```json
{
"type": "function",
"name": "get_current_weather",
"description": "Get the current weather in a given location.",
"parameters": {},
"strict": true
}
```
Keep this difference contained in provider adapter code. `ProviderToolAdapters` maps a canonical `ToolDefinition` to the Chat Completions or Responses wire shape. Tool implementations should not know which provider API shape was used.
Tool result handling also differs by API. Chat Completions returns tool calls in `message.tool_calls` and receives results as `role: "tool"` messages. Responses returns `function_call` output items and receives results as `function_call_output` input items correlated by `call_id`. Both paths still execute local tools through `ToolExecutor`, so validation, provider confidence checks, trace formatting, and blocked-call behavior stay shared.
If a tool throws `ToolExecutionBlockedException`, `ToolExecutor` returns the exception message as plain text to the model and records the trace as `BLOCKED`. Other exceptions are logged with details and returned to the model as plain text in the form `Tool execution failed: ...`, with the trace recorded as `ERROR`.
Create one JSON file per tool under `wwwroot/tool_definitions`. The file describes the user-visible tool metadata, optional settings, the function schema sent to the model, and optional per-tool policy guidance injected centrally into the system prompt.
"description": "The city to find the weather for, e.g. 'San Francisco'."
},
"state": {
"type": "string",
"description": "The two-letter abbreviation for the state, e.g. 'CA'."
},
"unit": {
"type": "string",
"description": "The unit to fetch the temperature in.",
"enum": [
"celsius",
"fahrenheit"
]
}
},
"required": [
"city",
"state",
"unit"
],
"additionalProperties": false
}
}
}
```
Use stable lower-case IDs with underscores. Keep `id`, `implementationKey`, and `function.name` identical unless there is a clear compatibility reason not to.
Keep `function.descriptionForLLM` focused on what the tool does. This value is mapped to the provider's function `description` field and is only shown to the LLM. Put sequencing rules, answer-format guidance, or other behavior instructions in `systemPromptInstructions`. When runnable tools are selected, their non-empty policy text is combined centrally and appended to the effective system prompt.
public string GetDescription() => TB("Use this demo tool to retrieve the current weather for a given city and state."); // this Description is shown to the user
The example above is documentation-only. Do not keep demo tools in the production tool catalog.
## Settings And Secrets
Tool settings are stored through `ToolSettingsService`. Plain settings are stored in the regular configuration data. Settings marked with `"secret": true` are stored in the OS keyring through the Rust service.
Use `ValidateConfigurationAsync` when a setting needs more than "required field is present" validation, such as URL syntax, numeric limits, mutually exclusive options, or allowlist parsing.
Use `SensitiveTraceArgumentNames` for model-provided arguments that must not be shown in tool traces. Do not return secrets in `TextContent`, `JsonContent`, exception messages, logs, or trace formatting.
When a tool returns data that future messages must only send to providers at or above a specific confidence level, set `ToolExecutionResult.RequiredProviderConfidence`. AI Studio persists the highest requirement reached by the chat and applies it to later provider checks.
Treat model-provided tool arguments as untrusted input.
For tools that perform network requests:
- Accept only the schemes and hosts that are required for the feature.
- Validate redirects before following them.
- Do not allow model-supplied URLs to access localhost, loopback, link-local, multicast, or private network targets unless the feature has an explicit policy for that.
- Check `ToolExecutionContext.ProviderConfidence` before returning sensitive data to the model.
- Throw `ToolExecutionBlockedException` for intentional policy blocks so the UI can show the call as blocked instead of failed.
`web_search` is a combined search-and-retrieve tool. It asks the configured SearXNG instance for ranked candidates, applies the requested result limit, deduplicates equivalent URLs, and then loads the remaining public HTTP or HTTPS pages. Up to four pages are retrieved concurrently. Failed, blocked, unsupported, and empty pages are omitted, while an overall retrieval timeout returns any pages that completed successfully before cancellation.
Page loading and readable Markdown extraction are shared with `read_web_page` through `WebPageRetrievalService`. The service validates DNS results and every redirect target before connecting. `web_search` always uses the public-only policy and never reads private, loopback, link-local, or otherwise non-public targets. `read_web_page` remains the independent single-URL tool and may use its configured private-host allowlist, provider-confidence check, and operating-system sign-in behavior.
The `web_search` result separates each hit into `search_metadata` and `page`. Top-level counters report how many ranked candidates were considered, how many unique retrievals started, how many final pages were returned, and how many candidates were omitted. Search-result URLs and final redirect URLs are deduplicated separately so metadata from merged candidates is retained with the best rank.
Every successfully retrieved page with readable content is also returned as a structured tool source. The source uses the final URL after redirects and prefers the extracted page title, followed by the search-result title and URL as fallbacks. The provider collects these sources across local tool calls and attaches them to the final response under the separate “Sources used by tools” heading. Failed, blocked, empty, and duplicate retrievals do not add sources.
Retrieved Markdown shares a configurable total character budget. Every successful result first receives its configured minimum allocation; the remaining budget is then assigned in ranking order. Short pages leave their unused allocation available to later results. Truncated pages use the shared truncation marker and report `partial` status together with original and returned character counts.
The Web Search settings use these defaults and hard maximums:
All values must be positive. The total budget must be large enough to reserve the configured minimum for the hard limit of 20 results. The existing `timeoutSeconds` setting continues to apply only to the SearXNG request.
The two tools can be selected independently. Tool policy text tells the model not to call `read_web_page` for a URL already returned by `web_search`, because the search result already contains that page's retrieved content.
Every non-secret tool field that administrators should be able to manage centrally must have an explicit enterprise mapping in `ToolSettingsService`. Add its backing setting to the appropriate `Settings/DataModel` class, register it with `ManagedConfiguration.Register(...)`, process it in `PluginConfiguration`, clean leftovers in `PluginFactory.Loading`, and document its allowed values, default, and limits in `Plugins/configuration/plugin.lua`. Locked enterprise values override the local field, while editable enterprise defaults apply only until a user saves a local value. Secret fields require the existing OS-keyring path and must not be routed through plain enterprise settings.