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WebZum - The Hosting Layer for AI-Generated Web Content

by io.github.suprraz

regenerate_image

Regenerate one image inside a specific section of a WebZum site. Creates a new version with a freshly AI-generated image for that section and reassembles.

Use the optional userMessage to steer the new image — "show a wider shot", "change the angle", "make it sunset lighting", etc.

Required: businessId, versionId, sectionId.

Returns { versionId, status: 'completed' | 'in_progress', ...extra }. If status is 'in_progress', poll get_site_status with the returned versionId every 5-10s until isComplete is true.

Concurrency: edits on the same businessId MUST be serial. Never fire parallel edit calls on the same site.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sectionIdYesThe sectionId whose image should be regenerated.
versionIdYesThe versionId to base this regeneration on.
businessIdYesThe site's businessId.
userMessageNoOptional steering for the new image (e.g. "wider shot", "sunset lighting").
assistantContextNoOptional assistant context to accompany the userMessage.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint: false, destructiveHint: false), the description discloses that the tool creates a new version, returns in_progress/completed status, requires polling, and mandates serial edits. This is meaningful behavioral context not available from structured data.

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

Conciseness5/5

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

The description is well-structured and front-loaded with the core purpose. It uses short paragraphs and bullet-like hints, with no filler. Each sentence adds operational value, from the basic action to polling and concurrency.

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 absence of an output schema, the description adequately covers the return shape, the async in_progress flow, and how to follow up. It addresses the main operational caveat (serial edits) and is complete for the tool's complexity.

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 100% with each parameter described. The description adds extra value by providing concrete examples for userMessage ('wider shot', 'sunset lighting') and explicitly listing the required parameter set, though it does not deeply describe assistantContext beyond the schema.

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 uses a specific verb and resource: 'Regenerate one image inside a specific section of a WebZum site.' It clearly defines the scope (one image in a section) and distinguishes from sibling tools like regenerate_header or regenerate_logo.

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

Usage Guidelines4/5

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

The description gives explicit guidance on required parameters, optional userMessage usage, and polling behavior via get_site_status. It also warns about serial edit concurrency. However, it does not explicitly name sibling tools for alternative image regeneration scenarios, though the tool name and scope make this largely implicit.

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

A4/5.0
Disambiguation4/5

Most tools have clear, distinct purposes: cloning, creating, hosting, editing, and regenerating different site parts. The main ambiguity is between create_lead_gen_site and generate_geo_page, which both create lead-gen pages but differ in targeting and workflow; however, their descriptions clarify the use cases sufficiently.

Naming Consistency4/5

Tool names follow a consistent lowercase verb_noun pattern (e.g., clone_site, host_file, update_site_html). Minor inconsistencies exist: create vs. generate for similar actions (create_site vs. generate_geo_page) and get vs. list for retrieval (get_hosted_files vs. list_user_sites), but these are predictable and readable.

Tool Count3/5

With 17 tools, the count is slightly heavy for a hosting service, but the variety of operations (creation, cloning, file hosting, editing, regeneration, status, search) justifies most of them. A few tools (e.g., four regenerate_* tools) could potentially be consolidated, but the scope still feels reasonable.

Completeness4/5

The toolset covers the full lifecycle: create, clone, host, list, edit, update, and regenerate site components. Notable gaps include no delete operation for sites or files and no explicit version rollback, but agents can work around these by using host_file to overwrite and relying on site status for progress.

Resources