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resolve_step

Resolve a Gherkin step phrase to existing matching step definitions, showing exact, ambiguous, or no matches so you reuse code and avoid duplicates.

Instructions

Resolve a Gherkin step phrase to the EXISTING step definition(s) that would bind it — the same way the runner does (regex/cucumber expression, keyword-agnostic). Use this BEFORE writing a new step so an agent reuses what already exists instead of authoring a duplicate. status is 'exact' (one binding — reuse it), 'ambiguous' (several match — a conflict to resolve), or 'none' (nothing binds — returns existing step definitions ranked by shared terms, to adapt rather than duplicate). Each match returns the expression, the C# class/method, the method parameters, the argument values captured from the phrase, and file:line.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe step phrase to resolve, without the leading Given/When/Then keyword (e.g. "the customer checks out").
keywordNoOptional; informational only — matching is keyword-agnostic, as in Reqnroll/SpecFlow.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.10

TDQS

A4.4/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and does so thoroughly. It discloses the three status outcomes, how matches are ranked in the 'none' case, the exact fields returned per match, and that matching ignores Given/When/Then keywords.

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 but well-structured, front-loading the core purpose before explaining statuses and return contents. Every sentence adds value, though it is slightly long and could be tightened without losing meaning.

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?

Given the lack of annotations and output schema, the description is exceptionally complete. It explains what the tool returns, how statuses should be interpreted, and why the agent should use it, leaving no critical gaps 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 schema already documents both parameters. The description reinforces that the keyword parameter is informational and keyword-agnostic, but it does not add substantial per-parameter semantics 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 clearly states the tool's verb and resource: it resolves a Gherkin step phrase to the existing step definition(s) that would bind it. It also differentiates itself from authoring a new step and explains its keyword-agnostic matching behavior, making its purpose distinct from sibling search tools.

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 explicit timing guidance: use this BEFORE writing a new step so the agent reuses existing definitions instead of duplicating. It does not explicitly name sibling alternatives or state when not to use it, but the usage context is clear and actionable.

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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