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BotKelp

by io.github.aiscimi-code

Verify scaffold

verify_scaffold

Runs a real npm install && npm run build against the given files and reports whether the project builds. Free, no API key needed. Use it after editing a generate_scaffold result (e.g. wiring a component into a page) before handing the project to the user. The build runs asynchronously: call once with files (the full project — every file, not just the ones you changed) and you get back status "WAIT" and a jobId; call again with that jobId until you get "OK" (it builds) or "FAIL" (it does not, with the compiler error). A build usually takes one to two minutes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filesNoThe full project to verify — generate_scaffold's `files` output with your edits applied. Provide this to start a verification.
jobIdNoA job id from a previous WAIT response. Provide this to check a verification already started.
envVariablesNoEnv var names to write placeholder values for before building (typically generate_scaffold's `envVariables` output) — needed for components that read process.env at build time (e.g. a Supabase client).

Schema Changelog

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

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden and does well: it discloses that it runs a real build, requires no API key, is asynchronous with WAIT/OK/FAIL responses, and typically takes one to two minutes. This is exactly the kind of behavioral context an agent needs.

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?

The description is longer than strictly minimal, but every sentence earns its place: core purpose, usage context, async protocol, timing, and parameter guidance. It is front-loaded with the primary action and remains well-structured.

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?

For a tool with no annotations and no output schema, the description is remarkably complete. It covers the tool's operation, the async workflow, return states, expected duration, and parameter usage. An agent has enough information to call it correctly on the first attempt and to poll for completion.

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 description coverage is 100%, so the baseline is 3. The description adds meaningful semantics beyond the schema by emphasizing that `files` must be the full project, not just changed files, and by explaining how `jobId` is used across polling calls. This raises it above baseline.

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 and resource: it runs a real `npm install && npm run build` against given files and reports whether the project builds. This clearly distinguishes it from siblings like `generate_scaffold` or `get_scaffold_template`.

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?

It gives an explicit when-to-use instruction: after editing a `generate_scaffold` result, before handing the project to the user. It also explains the async polling workflow and timing, leaving no ambiguity about how to invoke it across calls.

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

A4.3/5.0
Disambiguation5/5

Each tool targets a distinct workflow step: registry search, update checking, three scaffold delivery modes, build verification, project registration, project listing, repo changes, and issue reporting. The apparent overlap between generate_scaffold, get_scaffold_template, and buy_scaffold_template is resolved by their clearly different delivery and payment models.

Naming Consistency5/5

All tool names follow a consistent imperative verb + noun snake_case pattern, such as search_components, create_project, verify_scaffold, and report_issue. The verbs are specific and predictable, making the set easy to navigate.

Tool Count5/5

Ten tools is well-scoped for a server handling component discovery, scaffold generation, build verification, project management, and feedback. Each tool covers a meaningful, non-redundant function without bloating the surface.

Completeness4/5

The core lifecycle is well covered: discover components, check for updates, generate or purchase scaffolds, verify builds, apply changes to a repository, and report issues. Minor gaps exist, such as no tool to delete or update a registered project, but these do not block the primary workflows.

Resources