Boosthis

Jobs

boosthis_jobs

Read-only

Which scheduled jobs does this project report, and has any missed a run? Lists every job the app reports (nightly billing, queue drain, reindex), each in one state: on time; late; app unheard, meaning the app stopped reporting before the run was due, so the job is never blamed for it; never reported a single run; or no rhythm declared, so the job is remembered and not watched. Each job also carries its expected rhythm, how late is too late, when it last finished, whether that run succeeded, and how long runs take. Only a job’s name and timings are collected, never its arguments or data, and Boosthis never runs, triggers or schedules a job. Needs install_id and read_token from the project’s page in the Boosthis dashboard (“Connect AI once”).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`install_id`	Yes	The project’s install id, from its dashboard page.
`read_token`	Yes	That project’s read-only token (same card).

TDQS

A4.1/5.0

Behavior5/5

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

The description goes well beyond the readOnlyHint annotation by explaining nuanced states like 'app unheard', the rule that an unreported app is never blamed for a missed run, and that Boosthis never runs, triggers, or schedules jobs. It also clarifies that only job name and timings are collected. This is exemplary behavioral disclosure.

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 front-loaded with the core question and then covers states, payload contents, data-collection limits, and auth. It is slightly long-winded in the middle but every sentence adds useful context, and the examples of nightly billing, queue drain, and reindex are valuable.

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?

Even without an output schema, the description tells the agent exactly what each job entry includes: expected rhythm, lateness threshold, last finish time, run success, and duration. It also covers authorization needs and what the tool will never do, making it fully actionable.

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 description coverage is 100% because both install_id and read_token already have descriptions. The tool description only restates where they come from ('from the project’s page in the Boosthis dashboard') without adding meaningful new parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening question 'Which scheduled jobs does this project report, and has any missed a run?' plus 'Lists every job the app reports' clearly identifies the tool as a read-only status lister for scheduled jobs. It does not explicitly distinguish itself from sibling tools, so it misses the top tier.

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 clear context for when the tool is relevant: inspecting reported jobs and whether any missed a run. It does not mention alternatives or exclusions, which would be needed for a 5, but the use case is explicit enough.

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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