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Crash Risk

boosthis_crash_risk
Read-only

The crash classes this app has already recorded on the device — uncaught errors, unhandled promise rejections and caught render near-misses — newest first, each with an error name, a redacted top frame, an occurrence-count bucket, and relatedRules (rule ids boosthis_get_rule answers for). It is joined with the JS-thread (ANR-style) Stability summary from the latest snapshot and stabilityRules for hang and OOM prevention. Crash signatures are code-derived (error name plus redacted frame), never the raw message, so no user value is exposed. It answers live from the project's own Boosthis server when read credentials are configured; without them, or before a crash has been recorded, the answer is a note pointing at the in-app dashboard. Read-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
install_idNoOptional: 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_tokenNoOptional: 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

A4.1/5.0
Behavior5/5

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

Annotations already carry readOnlyHint=true and destructiveHint=false, and the description's closing 'Read-only' is consistent with them. Beyond that, the description discloses three genuinely non-obvious behaviors: the credential-dependent live-vs-note fallback, the privacy-preserving redaction policy (code-derived signatures never expose raw user messages), and the multi-source join with the JS-thread Stability summary.

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?

Four dense sentences, each carrying distinct information: data composition, the stability-summary join, the redaction policy, and the auth-dependent fallback behavior. There is no redundancy and the core purpose is front-loaded; the length is justified by the complexity of the tool's data model rather than padding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the return-value burden and largely succeeds: it conveys per-crash structure, ordering (newest first), the joined stability section, and the no-credentials/no-data edge cases. Minor gaps remain — no mention of result limits or pagination, and no explicit sibling routing for raw-trace or sample-level needs — but these are small against the overall richness.

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

Parameters3/5

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

Schema coverage is 100% — both install_id and read_token have thorough schema descriptions covering their source, the local-stdio env fallback, and the read-only scoping of the token. The tool description adds no parameter-specific detail, but at this coverage level the baseline of 3 applies; no compensation is needed.

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?

Names a specific resource — crash classes recorded on the device — and enumerates its exact contents (error name, redacted top frame, occurrence-count bucket, relatedRules) plus the three crash categories it covers. It distinguishes itself from siblings like boosthis_full_stack_trace by emphasizing that signatures are code-derived, never raw messages.

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

Usage Guidelines3/5

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

Provides conditional usage context: it answers live when read credentials are configured and falls back to a dashboard-pointing note when they aren't or when no crash has been recorded. It cross-references boosthis_get_rule for interpreting relatedRules, but never explicitly states when to prefer this tool over sibling alternatives such as boosthis_full_stack_trace or boosthis_recent_samples, leaving routing to inference.

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.

Resources