Boosthis

Get Rule

boosthis_get_rule

Read-only

Full detail for a single rule: title, when_to_apply, evidence, and — for a registered project — the prescriptive fix_template, fetched per rule from the Boosthis server. when_to_apply is what decides whether the rule applies to a given piece of code. fix_available: false means no fix text is served for this project; fix_note says what would change that. counterparts, when present, names the same idea's rule in other languages, which is what answers 'does this also apply to my other service?'. The answer also carries also_applies_here: the other places in THIS project where the same rule applies, with a count, so the work can be finished as a set rather than one spot at a time.

Input Schema

TableJSON Schema

Name	Required	Description
`id`	Yes	Rule id, e.g. 'split-driver-jitter'
`skip`	No	Optional. The `part` id of one entry in this rule's also_applies_here list that has been considered and does not need doing. Recorded permanently — that part is never raised again for this project, so it is worth being sure.
`route_label`	No	Optional. The route or screen currently being worked on. Used only so the list of other affected places leaves out the one already open.
`skip_decided_by`	No	Optional. Who decided to skip it. Use 'developer' when the person said so themselves.

TDQS

A4.5/5.0

Behavior5/5

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

Beyond the readOnly and non-destructive annotations, the description discloses meaningful behavioral traits: fetching per rule from the server, the conditional nature of fix_template for registered projects, fix_available/fix_note semantics, the purpose of counterparts, and the also_applies_here grouping. This gives the agent a strong mental model of the returned data and its implications.

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 main purpose is front-loaded in the first sentence, and every subsequent sentence adds substantive semantic value about fields the agent needs to interpret. It is detailed but not padded or repetitive.

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?

With no output schema present, the description carries the burden of explaining the response, and it does so thoroughly: core fields, conditionality, counters, and cross-project applicability. Combined with the fully documented parameter schema, nothing essential is missing for correct invocation and interpretation.

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%, so the baseline is 3. The tool description focuses on response fields rather than parameter behavior, and the schema already fully documents id, skip, route_label, and skip_decided_by. No additional parameter-level meaning is added beyond what the schema provides.

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 opens with 'Full detail for a single rule' and enumerates the exact fields returned (title, when_to_apply, evidence, fix_template), giving a specific verb, resource, and scope. It clearly differentiates from list/match siblings by centering on a single rule fetched per id.

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 provides clear context: it is the tool for full detail on one rule, including fix availability, cross-language counterparts, and other affected places in the current project. It stops short of explicitly naming alternatives like list_rules or match_rules_for_code or stating when not to use it, so it lacks explicit exclusions.

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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