boosthis_match_rules_for_codeRanks Boosthis rules against a code snippet using each rule's id tokens and when_to_apply text. Answers with up to 8 candidates. They are ranked guesses from a text match, not findings: each rule's when_to_apply is what settles whether it really applies.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the read-only annotation, the description discloses an important behavior: results are 'ranked guesses from a text match, not findings,' and that each rule's when_to_apply is what settles true applicability. This prevents misuse and gives the agent a clear mental model of the tool's limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, no filler, with the core action first, output count second, and the critical caveat last. Every sentence earns its place and the structure aids comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter read-only tool with no output schema, the description covers the main purpose, output cardinality, and interpretive caveat. It is slightly incomplete on what the candidate response format looks like, but the core usage guidance is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage for the only parameter is 0%, so the description carries the burden. It does clarify that the 'code' parameter is a 'code snippet,' which matches the field name and adds minimal context, but it does not specify accepted formats, language expectations, size limits, or whether raw text versus a path is expected.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('Ranks Boosthis rules against a code snippet'), identifies the input resource, and clarifies the mechanism (id tokens and when_to_apply text). It clearly differentiates this matching/ranking tool from siblings like list_rules or get_rule by describing its output as candidate guesses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context: the tool is for matching rules to a code snippet and returns up to 8 ranked candidates. It implicitly tells agents when to use it, but does not explicitly name alternatives or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.