webmcp
WebMCP
Procedures
Step 1: Identify the browser integration surface
- Inspect the workspace for browser entry points, UI handlers, and any existing app state or form handling layer.
- Execute
node scripts/find-webmcp-targets.mjs .to inventory likely frontend files and existing WebMCP markers when a Node runtime is available. - If a Node runtime is unavailable, inspect the nearest
package.json, HTML entry point, and framework bootstrap files manually to identify the browser app boundary. - If the workspace contains multiple frontend apps, prefer the app that contains the active route, component, or user-requested feature surface.
- If the inventory still leaves multiple plausible frontend targets, stop and ask the user which app should receive the WebMCP integration.
- If the project is not a browser web app, stop and explain that this skill does not apply.
Step 2: Choose the WebMCP shape
- Read
references/webmcp-reference.mdbefore writing code. - Read
references/declarative-api.mdwhen the feature can be expressed as an HTML form flow or needs agent-invoked submit handling. - Read
references/compatibility.mdwhen native availability, Chrome preview setup, or draft-versus-preview behavior matters. - Read
references/troubleshooting.mdwhen registration, schema serialization, or agent-driven form execution fails. - Verify that the integration runs in a secure window browsing context.
- If the feature must run on the server, in a worker, or headlessly without a visible browsing context, stop and explain the platform limitation.
- Choose the imperative API when the tool wraps existing JavaScript logic, requires dynamic registration, or needs
ModelContextClient.requestUserInteraction(). - Choose the declarative API when the user flow already maps cleanly to a form submission and the page can keep the human-visible form in sync with agent activity.
- Keep tool names, descriptions, and parameters explicit and positive, and prefer atomic tools over overlapping variants.
Step 3: Implement tool registration
- Read
assets/model-context-registry.template.tsand adapt it to the framework, state model, and file layout in the workspace when using the imperative API. - Register imperative tools with
navigator.modelContext.registerTool()using a stablename, a positivedescription, an objectinputSchema, and anexecutecallback. - Set
annotations.readOnlyHinttotrueonly for tools that do not modify state. - Validate business rules inside the tool implementation even when the schema is strict, and return descriptive errors that help the agent retry with corrected input.
- Return tool results only after the UI and application state reflect the tool's effect.
- If tool availability depends on route, selection, or page state, register tools only while they are valid and unregister stale tools by aborting the
AbortControllerwhose signal was passed toregisterTool(); during the Chrome 148 transition window, also callnavigator.modelContext.unregisterTool?.()with optional chaining before aborting. - For declarative tools, annotate the target
<form>withtoolnameandtooldescription, and let form controls define the parameter surface. - Use labels or
toolparamdescriptionto produce clear parameter descriptions for declarative fields. - Use
toolautosubmitonly when the page should submit automatically after the agent populates the form.
Step 4: Wire agent-driven UX safely
- Preserve the normal human interaction path even when the page supports agent invocation.
- When an imperative tool needs explicit confirmation or a user-facing step, call
client.requestUserInteraction()instead of bypassing the UI. - When customizing declarative submit handling, call
preventDefault()beforerespondWith()and return structured validation errors for agent-invoked submits. - Use preview-only events such as
toolactivated,toolcancel,agentInvoked, and WebMCP form pseudo-classes only behind compatibility-aware UI logic. - Keep destructive or sensitive actions gated behind visible user confirmation, even if the agent can prepare the input.
- Keep UI state synchronized so the same page accurately reflects changes caused by human input and tool calls.
Step 5: Validate behavior
- Test the register and unregister lifecycle, including duplicate-name protection and cleanup on route or state changes.
- Test invalid or incomplete inputs to confirm the tool returns corrective errors instead of silently failing.
- For declarative tools, verify generated parameter descriptions, required fields, submit behavior, and any custom
respondWith()handling. - If the target environment is the current Chrome preview, confirm the required version and flag state from
references/compatibility.mdbefore treating runtime failures as application bugs. - Use the Model Context Tool Inspector or equivalent preview tooling only as a validation aid, not as a runtime dependency.
- Validate deterministic execution first by inspecting the registered tool set and manually invoking the tool with representative arguments when preview tooling is available.
- After deterministic execution is correct, validate natural-language routing so descriptions and parameter shapes guide the agent toward the correct tool.
- Run the workspace build, typecheck, or tests after editing.
Error Handling
- If
navigator.modelContextis missing, confirm the code is running in a secure browser window context and then check the preview requirements inreferences/compatibility.md. - If
registerTool()throwsInvalidStateError, check for duplicate names or emptynameordescriptionvalues. - If
registerTool()throwsTypeErroror JSON serialization errors, replace non-serializable or circularinputSchemavalues with plain JSON-compatible objects. - If an older demo or article references
provideContext,clearContext, ortoolparamtitle, treat those surfaces as obsolete for current implementations. - If declarative execution does not update the page correctly, read
references/declarative-api.mdandreferences/troubleshooting.mdbefore changing the tool contract.
More from webmaxru/agent-skills
github-agentic-workflows
Authors, reviews, installs, and debugs GitHub Agentic Workflows in repositories, including workflow markdown, frontmatter, gh aw compile and run flows, safe outputs, security guardrails, and operational patterns. Use when creating or maintaining GH-AW automation. Don't use for standard deterministic GitHub Actions YAML, generic CI pipelines, or non-GitHub automation systems.
96proofreader-api
Implements and debugs browser Proofreader API integrations in JavaScript or TypeScript web apps. Use when adding Proofreader availability checks, monitored model downloads, proofread flows, correction metadata handling, or permissions-policy checks for built-in proofreading. Don't use for generic prompt engineering, server-side LLM SDKs, or cloud AI services.
93prompt-api
Implements and debugs browser Prompt API integrations in JavaScript or TypeScript web apps. Use when adding LanguageModel availability checks, session creation, prompt or promptStreaming flows, structured output, download progress UX, or iframe permission-policy handling. Don't use for server-side LLM SDKs, REST AI APIs, or non-browser providers.
92writing-assistance-apis
Implements and debugs browser Summarizer, Writer, and Rewriter integrations in JavaScript or TypeScript web apps. Use when adding availability checks, model download UX, session creation, summarize or write or rewrite flows, streaming output, abort handling, or permissions-policy constraints for built-in writing assistance APIs. Don't use for generic prompt engineering, server-side LLM SDKs, or cloud AI services.
92agent-package-manager
Installs, configures, audits, and operates Agent Package Manager (APM) in repositories. Use when initializing apm.yml, installing or updating packages, validating manifests, managing lockfiles, compiling agent context, browsing MCP servers, setting up runtimes, or packaging resolved context for CI and team distribution. Don't use for writing a single skill by hand, generic package managers like npm or pip, or non-APM agent configuration systems.
92language-detector-api
Implements and debugs browser Language Detector API integrations in JavaScript or TypeScript web apps. Use when adding LanguageDetector support checks, availability and model download flows, session creation, detect() calls, input-usage measurement, permissions-policy handling, or compatibility fallbacks for built-in language detection. Don't use for server-side language detection SDKs, cloud translation services, or generic NLP pipelines.
92