Boosthis

Snapshot

boosthis_snapshot

Read-only

The whole Boosthis bubble for one install — the device's latest upload: boot ladder, frame meters (Speed / Smoothness / Scroll / Stability / Render), Frustration and Idle axes, per-route rows, per-screen diagnosis, session summary and budgets. It answers live from the project's own Boosthis server when read credentials are configured (environment values on a local stdio server, install_id + read_token arguments on the hosted one). Without them, or before the app has uploaded a snapshot, the answer is a note pointing at the in-app dashboard. A read token cannot delete anything. An account can hold several projects, each reporting in several runtimes. With an account_token, project (name, 'name (runtime)', or an install id) selects one; without it the answer is the most recent reporter. The rest are listed under your_projects; entries sharing a project_group are one project in different runtimes, each with its own readings.

Input Schema

TableJSON Schema

Name	Required	Description
`project`	No	Which project in the account to read — its dashboard name, that name with a runtime as 'name (runtime)', or an install id. Only meaningful with account_token.
`install_id`	No	Optional: the install id to read live data for. On the HOSTED Boosthis MCP, copy it from the in-app dashboard's "Connect your AI" card and pass it here. Omit on a local stdio server (it uses BOOSTHIS_INSTALL_ID from the env).
`read_token`	No	Optional: the SELF-scoped read token for that install (paired with install_id). It is read-only — it can read this app's own perf data but CANNOT delete it. Copy it from the in-app dashboard. Omit on a local stdio server (it uses BOOSTHIS_READ_TOKEN from the env).

TDQS

A4.7/5.0

Behavior5/5

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

The description goes well beyond the readOnly/destructive annotations by explaining the live-server dependency, fallback behavior when credentials are absent, the read token's inability to delete, project grouping semantics, and selection of the most recent reporter. This is rich behavioral context that annotations alone do not provide.

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 dense and somewhat long, but every sentence adds necessary operational detail. The main content payload is front-loaded, and the caveats about credentials and project selection are placed logically. It could be tightened, but it is not padded.

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 complex read tool with no output schema, the description covers the full behavior: what data is included, when live data is unavailable, how authentication works across local and hosted contexts, how multi-project accounts are handled, and what the read token can and cannot do. Nothing critical for correct invocation is missing.

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

Parameters5/5

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

Even though schema coverage is 100%, the description adds substantial meaning to each parameter: project is clarified as only meaningful with account_token, install_id is tied to hosted vs local environments, and read_token is explicitly read-only. This is exactly the kind of extra semantic value that helps an agent invoke parameters correctly.

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 specifies exactly what the tool returns: the complete Boosthis snapshot for one install, including boot ladder, frame meters, axes, per-route rows, per-screen diagnosis, session summary, and budgets. This clearly distinguishes it from narrower sibling tools like boosthis_session_summary or boosthis_budgets.

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 clearly explains the credential conditions under which live data is returned versus a fallback note, and how project selection works with accounts. It does not explicitly name sibling alternatives or say 'use this instead of X', but the context is strong enough for an agent to infer appropriate usage.

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

A3.9/5.0

Disambiguation4/5

Most tools target a distinct concern—alerts, budgets, jobs, promises, rules, crash risk, install/removal—and the descriptions are detailed enough to separate them. A few pairs, like verify_kit/verify_kit_install and recent_samples/session_summary, could be misselected without close reading, but they are not functionally identical.

Naming Consistency3/5

All tools share the boosthis_ prefix and use snake_case, which creates a recognizable namespace. However, the second part mixes noun-only names (alerts, budgets, jobs, snapshot, trend), verb-based names (get_rule, remember_promise, verify_kit), and one sentence-style name (what_should_i_look_at_next), so there is no consistent verb_noun convention.

Tool Count3/5

At 24 tools, this sits squarely in the borderline-heavy range for an MCP server. Each tool does have a distinct role, but the surface feels large, especially with several overlapping read-only diagnostics that could potentially be consolidated.

Completeness4/5

The set covers the main observability lifecycle well: install, verify, monitor, diagnose, check trends, and record promises. Minor gaps exist—like no way to mutate alert states or delete promises through the MCP—but these appear intentional and are documented as dashboard-side actions.

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