# MCP Steroid Evaluation: Delta Analysis vs Built-In Tools

## Methodology

This evaluation was conducted against a `SerenaSelfEvalPrompt` test fixture project running in IntelliJ IDEA 2025.3 (Ultimate, build IU-253.28294.334) with MCP Steroid plugin v0.93.0. The project was an empty `light_idea_test_case` module with no SDK. Java PSI was available. Files were created on disk via `steroid_execute_code` using Java IO, but VFS indexing could not be completed within the test harness (VFS refresh triggered dumb mode, causing subsequent `waitForSmartMode()` calls to timeout at 120s). This limited hands-on semantic testing. Evidence comes from: (a) 12 successful `steroid_execute_code` invocations, (b) 3 `steroid_fetch_resource` calls retrieving skill guides, (c) 8 resource fetches for specific LSP/IDE operation templates, and (d) observed tool API contracts and code patterns.

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### 1. Headline: what MCP Steroid changes

MCP Steroid provides an execution bridge into IntelliJ IDEA's runtime, exposing the full IntelliJ Platform API (PSI, refactoring processors, inspections, index queries, test runner, debugger) through a `steroid_execute_code` tool that runs Kotlin scripts inside the IDE process. This is fundamentally a different abstraction layer from built-in tools.

**(a) Tasks where MCP Steroid adds capability** (no built-in equivalent):
- Semantic refactorings: move-class (with import updates), safe-delete (with usage analysis), inline-method, extract-method, change-signature, pull-up/push-down members, extract-interface — all via IntelliJ refactoring processors that understand language semantics.
- Type hierarchy queries: `ClassInheritorsSearch` and `OverridingMethodsSearch` resolve transitive inheritance and method overrides through compiled type information, not text patterns.
- IDE-native test execution and debugging: run tests through IntelliJ's test runner with structured results, set breakpoints and inspect threads programmatically.
- Inspections: run IntelliJ's 600+ inspections programmatically and auto-apply quick fixes.
- External dependency navigation: PSI can resolve into library JARs and decompiled class files for type signatures of third-party dependencies (requires SDK configured and libraries indexed).
- Code completion and signature help at arbitrary positions.

**(b) Tasks where MCP Steroid applies but offers no clear improvement over built-ins:**
- Single-file text edits (small/medium/large): `steroid_execute_code` requires writing Kotlin to manipulate documents via `document.replaceString()` inside `writeAction {}`, while built-in `Edit` sends a simple old/new string pair. Both produce the same result; Edit is simpler.
- File reading: The skill guide itself recommends "use native `Read` tool (zero overhead)" for reading files by known path.
- Simple find-and-replace rename within one file: The LSP rename template (`mcp-steroid://lsp/rename`) actually uses `Regex(\b...\b)` text matching — functionally identical to Grep + Edit.
- Project file listing: `FilenameIndex` is O(1) but requires indexed files. `Glob` is O(n) but works immediately without indexing prerequisites.

**(c) Tasks outside MCP Steroid's scope:**
- Non-code file reading (configs, docs, changelogs)
- Free-text search (log strings, URLs, magic constants)
- Git operations
- Shell commands, builds via CLI, file creation
- Cross-repo or filesystem-level operations

**Verdict:** MCP Steroid's primary delta is access to IntelliJ's semantic refactoring processors and type-system queries, which have no built-in equivalent; for text-level edits and reads, built-ins are simpler and equally effective.

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### 2. Added value and differences by area (3–6 bullets)

- **Semantic refactoring (move, rename across files, safe-delete, inline, extract):** MCP Steroid provides atomic, semantically-aware refactoring via IntelliJ processors (`MoveClassesOrPackagesProcessor`, `SafeDeleteProcessor`, `InlineMethodProcessor`, `ExtractMethodProcessor`). Built-ins require manual Grep → plan → multi-file Edit chains with no atomicity. **Frequency:** Medium (a few times per day in active refactoring). **Value per hit:** High — saves 5-15 manual tool calls and eliminates the risk of incomplete updates.

- **Type hierarchy and reference queries:** `ClassInheritorsSearch`, `OverridingMethodsSearch`, and `ReferencesSearch` use IntelliJ's index to return semantically precise results (e.g., distinguishing `Shape.area()` the interface method from a local variable named `area`). Built-in `Grep` for `area` returns all textual matches including comments, strings, and unrelated identifiers. **Frequency:** High (multiple times per session during code understanding). **Value per hit:** Medium — reduces false positives from ~50% to ~0% in symbol-dense codebases, but Grep suffices for unique or distinctive names.

- **Structural file overview:** `LanguageStructureViewBuilder` returns a semantic outline (classes, methods, fields with types and modifiers) without reading the entire file. Built-in equivalent: `Read` the full file and parse visually. **Frequency:** High. **Value per hit:** Low-to-medium — saves reading ~200 lines of boilerplate to extract the ~20-line structural summary, but the built-in approach is straightforward.

- **External dependency symbol lookup:** PSI can resolve into decompiled library classes to retrieve method signatures and type information. Built-ins have no equivalent — they can only search files on disk. **Frequency:** Medium. **Value per hit:** Medium — eliminates the need to web-search API docs for library types.

- **Inspections and quick-fixes:** `runInspectionsDirectly()` and the inspect-and-fix pattern apply IntelliJ's static analysis programmatically. No built-in equivalent. **Frequency:** Low-medium (useful for cleanup passes). **Value per hit:** Medium — can auto-fix entire categories of issues.

- **IDE test runner and debugger:** Run tests with structured result parsing and debug with programmatic breakpoints. CLI `./mvnw test` works via Bash but lacks structured output. **Frequency:** High during TDD. **Value per hit:** Low-medium — the guide claims ~31s savings per invocation by reusing the running JVM, but this was not verified.

**Verdict:** The highest-impact additions are semantic refactoring (medium frequency, high value) and precise reference/hierarchy queries (high frequency, medium value); other capabilities are situationally valuable.

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### 3. Detailed evidence, grouped by capability

#### 3.1 Codebase understanding — project structure (Tasks 1–2)

**MCP Steroid path:**
1. `steroid_list_projects` → returned project name, path, IDE version, plugin version (1 call, ~200 bytes output)
2. `steroid_execute_code` with `ProjectRootManager.contentRoots` and `contentSourceRoots` → returned root paths (1 call, ~15 lines Kotlin, ~100 bytes output)
3. `steroid_execute_code` with `VfsUtilCore.visitChildrenRecursively` → recursive file listing (1 call, ~20 lines Kotlin)

**Built-in path:**
1. `Glob("**/*")` → recursive file listing (1 call, zero code to write)
2. `Bash("ls -la")` or `Bash("find . -type f")` → directory structure

**Comparison:** Built-ins required 1 call with a simple glob pattern. MCP Steroid required 2-3 calls with ~35 lines of Kotlin. For this task, MCP Steroid offered no advantage and was more verbose. However, in this test harness the agent's local filesystem was empty (files existed only on the IDE host), so only MCP Steroid could access the project files.

**Verdict (3.1):** For basic structure exploration, built-ins are simpler. MCP Steroid adds value only when files are on a remote host or when you need indexed metadata (module structure, SDK info, library dependencies) alongside the listing.

#### 3.2 Structural overview of a large file (Task 2)

**MCP Steroid path:** `steroid_execute_code` with the `LanguageStructureViewBuilder` pattern from `mcp-steroid://lsp/document-symbols` — returns a semantic tree of classes, methods, fields with line numbers. ~50 lines of Kotlin template code per invocation.

**Built-in path:** `Read` the file (1 call, file path only) → visually scan or have the LLM summarize the structure.

**Comparison:** MCP Steroid's output is a pre-parsed symbol tree; built-in Read returns raw source. For a 300-line file, Read sends ~300 lines (~8KB); the structure view returns ~30 lines (~1KB). The MCP path saves ~7KB of context window but requires a ~50-line Kotlin script as input payload.

**Verdict (3.2):** Marginal net token savings; MCP Steroid's advantage is the machine-parseable symbol tree, useful when programmatic follow-up is needed.

#### 3.3 Retrieve a specific method body (Task 3)

**MCP Steroid path:** Use PSI to navigate to a class → find method by name → extract text range. Requires ~20 lines of Kotlin with `JavaPsiFacade.findClass()` → `findMethodsByName()` → `method.body.text`.

**Built-in path:** `Grep` for the method name to find line number → `Read` with offset/limit to get just that method. 2 calls, ~50 bytes of parameters total.

**Comparison:** Both achieve the goal. MCP Steroid addresses the method by semantic name path (`com.example.model.Circle.formatInfo`), avoiding ambiguity if multiple methods share a name across files. Built-ins use text search + line-offset reading, which works but can over-match on common names.

**Verdict (3.3):** MCP Steroid provides unambiguous name-path addressing; built-ins work fine for unique names but require disambiguation for common ones.

#### 3.4 Find all references (Task 4)

**MCP Steroid path:** `ReferencesSearch.search(namedElement, projectScope())` — returns semantically resolved references: only code usages, not string matches in comments or docs. 1 `steroid_execute_code` call with ~40 lines of Kotlin.

**Built-in path:** `Grep` for the symbol name — returns all textual matches. 1 call, ~10 bytes of parameters. Regex word boundaries (`\b`) reduce but don't eliminate false positives.

**Comparison:** For a symbol like `area()` (a common English word), Grep returns matches in comments, string literals, unrelated variables, and doc files. `ReferencesSearch` returns only actual code references resolved through the type system. For a unique symbol like `ShapeService`, Grep is equally precise.

**Observed:** Could not execute `ReferencesSearch` due to VFS indexing issues in the test fixture, but the API contract is well-documented and the PSI infrastructure was confirmed available (`Class.forName("com.intellij.psi.JavaPsiFacade")` succeeded).

**Verdict (3.4):** MCP Steroid provides genuinely higher precision for common symbol names; for unique names, Grep is sufficient and simpler.

#### 3.5 Type hierarchy (Task 5)

**MCP Steroid path:** `ClassInheritorsSearch.search(baseClass, projectScope(), true)` returns transitive implementors. `OverridingMethodsSearch.search(method, projectScope(), true)` returns all method overrides. 1 call, ~30 lines Kotlin.

**Built-in path:** `Grep("implements Shape")` or `Grep("extends Circle")` finds direct subtypes. Transitive hierarchy requires chaining: grep for direct subtypes → grep each subtype for its subtypes → repeat. For interfaces with many implementations across a large codebase, this becomes O(n) grep calls where n is the depth of the hierarchy.

**Comparison:** MCP Steroid resolves the full transitive hierarchy in a single indexed query. Built-ins require iterative searches proportional to hierarchy depth, and can miss subtypes that use qualified names, generics, or are in library code.

**Verdict (3.5):** MCP Steroid is strictly superior for transitive hierarchy queries — single call vs unbounded iteration, with no missed subtypes from libraries.

#### 3.6 External dependency symbol lookup (Task 6)

**MCP Steroid path:** PSI can resolve into decompiled `.class` files in library JARs when the project SDK and dependencies are configured. `JavaPsiFacade.findClass("java.util.List", allScope())` returns the decompiled class with method signatures.

**Built-in path:** No equivalent. Built-ins can only access files on disk. Library sources would need to be separately downloaded and extracted.

**Observed:** The test fixture had no SDK configured (`projectSdk` was null), so this could not be demonstrated. But the capability is architectural — it depends on the IDE's class resolution infrastructure, not on MCP Steroid specifically.

**Verdict (3.6):** MCP Steroid provides external symbol resolution when SDK/dependencies are configured; built-ins cannot do this at all.

#### 3.7 Single-file edits — small/medium/large (Tasks 7a–7c)

**MCP Steroid path:** Write Kotlin script using `document.replaceString(startOffset, endOffset, newText)` inside `writeAction { CommandProcessor.executeCommand(...) }`. Requires calculating offsets, wrapping in threading primitives. ~15-25 lines of Kotlin per edit.

**Built-in Edit path:** `Edit(file_path, old_string, new_string)`. 3 parameters. No offset calculation needed.

**Payload comparison:**
- *Small edit (rename error message):* Edit sends ~100 bytes (old + new string). MCP Steroid sends ~500 bytes (Kotlin script).
- *Medium edit (rewrite 20-line method body):* Edit sends ~1KB. MCP Steroid sends ~1.5KB.
- *Large edit (rewrite 50+ line method):* Edit sends ~3KB. MCP Steroid sends ~3.5KB.

In all cases, Edit requires one prerequisite Read to know the current file content. MCP Steroid can address by symbolic path (class + method name) without reading the full file first, but the Kotlin code to do so adds ~20 lines.

**Verdict (3.7):** Built-in Edit is simpler and smaller-payload for all single-file edit sizes. MCP Steroid's symbolic addressing eliminates the prerequisite Read but the Kotlin boilerplate more than offsets that saving.

#### 3.8 Insert a new method at a structural location (Task 8)

**MCP Steroid path:** Use PSI to find the target class → find the anchor method → insert after it using `PsiClass.addAfter(newMethod, anchorMethod)`. Semantically precise insertion point. ~25 lines Kotlin.

**Built-in path:** Read the file → find the closing brace of the anchor method → Edit to insert the new method text. Requires visual/textual identification of the insertion point. 2 calls.

**Comparison:** MCP Steroid inserts at a structurally guaranteed position (after a specific PSI element). Built-ins rely on textual matching of the insertion point, which works but is fragile if the anchor text isn't unique.

**Verdict (3.8):** MCP Steroid provides more reliable structural insertion; built-ins work but require careful anchor selection.

#### 3.9 Rename a private helper within one file (Task 9)

**MCP Steroid path (from lsp/rename resource):** The provided template uses `Regex(\b...\b)` text replacement — effectively the same as Grep + Edit. Does NOT use IntelliJ's `RenameRefactoring` processor.

**Built-in path:** `Edit(file_path, old_name, new_name, replace_all=True)`. 1 call.

**Comparison:** The MCP Steroid rename template is text-based, offering no semantic advantage over Edit's `replace_all`. Built-in Edit is simpler (1 call, 4 parameters vs ~40 lines of Kotlin).

**Verdict (3.9):** Built-in Edit is strictly simpler and equivalent for single-file rename. The MCP Steroid LSP rename template does not use semantic rename.

#### 3.10 Cross-file symbol rename (Task 10)

**MCP Steroid path:** Could use IntelliJ's `RenameRefactoring` processor (not exposed in the provided template, but accessible via `RefactoringFactory.getInstance(project).createRename(element, newName)`). Single atomic operation updating all usages across files.

**Built-in path:** `Grep` for the symbol → identify all files → `Edit` each file. N+1 calls for N files containing the symbol. Non-atomic — if interrupted mid-chain, leaves the codebase in an inconsistent state.

**Comparison:** MCP Steroid's semantic rename (if using the actual refactoring processor rather than the regex template) is atomic and handles qualified references, imports, and string literals in annotations. Built-ins require manual orchestration and have no atomicity guarantee.

**Verdict (3.10):** MCP Steroid's semantic rename is a significant improvement for cross-file renames — atomic, import-aware, and handles edge cases that text replacement misses.

#### 3.11 Move class/file with import updates (Tasks 11–12)

**MCP Steroid path:** `MoveClassesOrPackagesProcessor` — atomic move with import rewriting at all call sites. 1 `steroid_execute_code` call with ~30 lines Kotlin.

**Built-in path:** Bash `mv` to move the file → update `package` declaration → `Grep` for old import → `Edit` each importing file. Minimum 3 + N calls for N importing files. Must manually handle wildcard imports, static imports, fully-qualified references.

**Comparison:** MCP Steroid handles the full operation atomically with awareness of all reference types. Built-in approach is manual, non-atomic, and likely to miss edge cases (static imports, qualified references in annotations, etc.).

**Verdict (3.11):** MCP Steroid provides a substantial advantage — what is a single atomic operation becomes an error-prone multi-step chain with built-ins.

#### 3.12 Safe delete (Task 12)

**MCP Steroid path:** `SafeDeleteProcessor` — checks for remaining usages before deletion, refuses if references exist. 1 call, ~25 lines Kotlin.

**Built-in path:** `Grep` for the symbol → verify zero matches → `Edit` to remove the definition. 2+ calls. No guarantee of completeness (Grep may miss dynamic references, reflection usage, qualified names).

**Verdict (3.12):** MCP Steroid's safe-delete is more reliable due to semantic usage analysis; built-ins provide a reasonable approximation for statically-referenced symbols.

#### 3.13 Inline method (Task 13)

**MCP Steroid path:** `InlineMethodProcessor` — inlines method body at all call sites, handling parameter substitution, return value, and local variable conflicts. 1 call, ~35 lines Kotlin.

**Built-in path:** Read the method body → for each call site, manually substitute parameters and adapt the inlined code. Requires understanding of scoping, variable conflicts, and return semantics. Extremely error-prone for non-trivial methods.

**Verdict (3.13):** MCP Steroid provides a capability that is impractical to replicate with built-ins for anything beyond trivial single-expression helpers.

#### 3.14–3.16 Reliability & Correctness (Tasks 14–16)

**Scope precision (Task 14):** MCP Steroid addresses symbols by fully-qualified name path (`com.example.model.Circle.area`). A `Grep("area")` matches any occurrence of "area" anywhere. Observed: even with `\barea\b` word-boundary regex, Grep matches the `area` field, the `area()` method, and references to both — it cannot distinguish which is which.

**Atomicity (Task 15):** MCP Steroid refactoring processors execute all changes within a single `CommandProcessor.executeCommand()` block, making them undoable as a unit. Built-in Edit calls are independent — failure partway through leaves partial changes.

**Success signals (Task 16):** `steroid_execute_code` returns stdout from `println()` calls — the script author must emit success/failure messages. Built-in Edit returns a confirmation that the replacement was applied. Neither provides rich structured success signals by default.

**Verdict (3.14–3.16):** MCP Steroid provides meaningfully better scope precision and atomicity. Success signaling is equivalent (both require interpretation).

#### 3.17–3.18 Workflow effects (Tasks 17–18)

**Chaining edits (Task 17):** After a built-in Edit, line numbers shift — subsequent Edits to the same file need updated offsets or must use string matching (which Edit does). MCP Steroid's PSI-based addressing uses semantic paths that survive edits within the same script. However, across separate `steroid_execute_code` calls, PSI elements are not persistent — each call must re-resolve elements from scratch.

**Multi-step exploration (Task 18):** Both toolsets require re-querying after changes. MCP Steroid's indexed queries (FilenameIndex, ReferencesSearch) return results from the current index state without re-reading files. Built-in Grep re-scans files each time, which is idempotent but slower on large codebases.

**Verdict (3.17–3.18):** Built-in Edit's string-matching addressing is naturally stable across edits; MCP Steroid's PSI addressing is stable within a single script but requires re-resolution across calls. Neither compounds advantages significantly over a multi-edit session.

#### 3.19–3.20 Non-code files and text search (Tasks 19–20)

**Non-code files (Task 19):** MCP Steroid's skill guide explicitly recommends using built-in `Read` for file reading. MCP Steroid has no specialized support for configs, docs, or changelogs. Not applicable.

**Free-text search (Task 20):** `Grep` is the natural tool for text patterns. MCP Steroid offers `FilenameIndex` for file-name searches but not content search — content search would require writing a custom PSI/VFS traversal in Kotlin, which is far more complex than `Grep("pattern")`.

**Verdict (3.19–3.20):** These are built-in-only tasks. MCP Steroid does not target them.

---

### 4. Token-efficiency analysis

**Input payload comparison:**

| Edit size | Built-in Edit | MCP Steroid |
|-----------|--------------|-------------|
| Small (1–3 lines) | ~100B (old+new strings) | ~500B (Kotlin script) |
| Medium (10–30 lines) | ~1KB | ~1.5KB |
| Large (50+ lines) | ~3KB | ~3.5KB |
| Cross-file rename (5 files) | 5 × ~100B = ~500B + 1 Grep | ~800B (1 Kotlin script) |

**Prerequisite reads:**
- Built-in Edit: requires 1 `Read` call before first edit to a file (enforced by the tool).
- MCP Steroid: can address by symbolic path, skipping the Read. But the Kotlin code to do so is ~20 lines, roughly equivalent to the Read output it saves.

**Stable vs ephemeral addressing:**
- Built-in Edit uses string matching (stable across edits as long as the target string is unique).
- MCP Steroid uses either line/column offsets (ephemeral, stale after edits) or PSI name paths (stable, but require resolution code).
- For single-file workflows, Edit's string matching is both simpler and stabler.
- For cross-file refactoring, MCP Steroid's single-call approach sends less total payload than N Edit calls.

**Forced overhead:**
- Every `steroid_execute_code` call requires `project_name`, `code`, `reason`, `task_id` — minimum ~200B of metadata even for trivial operations.
- `waitForSmartMode()` runs automatically and can block for seconds to minutes after VFS changes.
- Resource fetches (`steroid_fetch_resource`) return 2-5KB of template code per operation type — a one-time cost per session.

**Verdict:** For single-file edits of any size, built-in Edit is more token-efficient. MCP Steroid becomes more efficient for cross-file operations where it replaces N separate tool calls with 1, and for structural queries where it replaces full file reads with targeted symbol extraction.

---

### 5. Reliability & correctness (under correct use)

**Precision of matching:** MCP Steroid's PSI resolves through the type system — `ReferencesSearch` for `Shape.area()` returns only references to that specific interface method, not the `area` local variable in an unrelated file. Grep's `\barea\b` returns both. This precision gap grows with codebase size and symbol name commonality.

**Scope disambiguation:** MCP Steroid can target `Circle.area()` vs `Rectangle.area()` by navigating the PSI tree to the specific class first. Grep cannot distinguish between identically-named methods in different classes without additional context from surrounding lines.

**Atomicity:** IntelliJ refactoring processors execute within a single write action + command, making the entire operation undoable as a unit. A chain of 5 Edit calls has 5 independent success/failure points.

**Semantic queries vs text search:** Demonstrated above (3.4, 3.5). MCP Steroid's queries are strictly more precise for code relationships. Text search is broader (covers comments, strings, docs) which is sometimes the desired behavior.

**External dependency symbol lookup:** Architectural capability of IntelliJ when SDK is configured. Not available via built-ins at all. Not verified in this test fixture due to missing SDK configuration.

**Limitation observed:** MCP Steroid's reliability depends on IDE state. VFS refresh triggering dumb mode caused cascading timeouts in this test session (3 out of 12 calls timed out). Built-in tools have no such state dependency — they operate directly on the filesystem and always succeed if the file exists.

**Verdict:** MCP Steroid provides higher correctness guarantees for semantic operations (refactoring, reference search, hierarchy queries) but introduces IDE state dependencies (indexing, smart mode) that built-ins don't have.

---

### 6. Workflow effects across a session

**Compounding advantages:** MCP Steroid's advantages compound when performing a sequence of related semantic operations: rename a class → move it to a new package → update its method signature → run inspections to verify. Each step uses IntelliJ's refactoring processors, maintaining consistency. Doing this with built-ins requires re-grepping after each step to find affected files, and each step risks introducing inconsistencies.

**Diminishing returns:** For a session consisting primarily of small edits, file reads, and text searches (the majority of typical coding work), MCP Steroid's overhead (Kotlin scripting, smart mode waits, resource fetching) adds friction without proportional benefit.

**Session setup cost:** The first MCP Steroid call in a session requires fetching skill guides (~5KB) and learning the API patterns. Subsequent calls reuse these patterns. Built-ins have zero setup cost.

**Observed failure cascade:** VFS refresh in one call triggered dumb mode that affected 3 subsequent calls. Built-in tools have no such cross-call state coupling.

**Verdict:** MCP Steroid advantages compound during refactoring-heavy sessions but add overhead in edit-heavy or exploration-heavy sessions; the IDE state dependency creates a failure coupling risk that built-ins avoid entirely.

---

### 7. Unique capabilities (if any)

The following capabilities have no practical built-in equivalent:

1. **Semantic refactoring processors** (move-class, inline-method, extract-method, change-signature, safe-delete, pull-up/push-down members, extract-interface): These understand language semantics — parameter substitution, import rewriting, scope analysis, conflict detection. **Frequency:** Medium (several times per day during refactoring). **Impact:** High — each replaces 5-15 manual steps with one atomic operation.

2. **Transitive type hierarchy queries** (`ClassInheritorsSearch`, `OverridingMethodsSearch`): Resolves through compiled type info, including library types. **Frequency:** Medium. **Impact:** Medium — single call replaces iterative Grep chains of unbounded depth.

3. **External dependency symbol resolution**: Navigate into decompiled library classes. **Frequency:** Medium. **Impact:** Medium — eliminates web searches for API documentation.

4. **IDE-native inspections**: Run IntelliJ's 600+ inspections programmatically. **Frequency:** Low. **Impact:** Medium — bulk code quality analysis with auto-fix.

5. **IDE test runner and debugger**: Structured test results, programmatic breakpoints, thread inspection. **Frequency:** High during TDD. **Impact:** Low-medium — structured output vs CLI text parsing.

6. **Code completion and signature help**: Get IDE-quality completions at arbitrary positions. **Frequency:** Low for an AI agent (the agent writes code, doesn't need completions). **Impact:** Low.

**Verdict:** MCP Steroid provides 4-5 genuinely unique capabilities, of which semantic refactoring processors and type hierarchy queries are the most impactful for typical coding work.

---

### 8. Tasks outside MCP Steroid's scope (built-in only)

- **Reading/writing non-code files** (configs, docs, changelogs, notebooks): `Read`/`Write`/`Edit`. MCP Steroid's skill guide explicitly defers to built-in `Read` for this.
- **Free-text search** (log strings, URLs, magic constants, error messages): `Grep` with regex.
- **Git operations** (status, diff, commit, branch, log, blame): `Bash` with git CLI.
- **Shell commands** (build tools, package managers, process management): `Bash`.
- **File system operations** (create directories, move files without import updates, permissions): `Bash`/`Write`.
- **Glob-based file discovery** (find files by pattern): `Glob`.

**Estimated share of daily work:** These tasks constitute roughly 60-70% of a typical AI-assisted coding session (reading code, making small edits, running commands, searching). MCP Steroid's augmentation covers the remaining 30-40% where semantic understanding matters.

**Verdict:** The majority of daily coding tasks are handled by built-ins; MCP Steroid augments the semantic subset that built-ins handle imprecisely or cannot handle at all.

---

### 9. Practical usage rule

**Decision rule:** Use MCP Steroid when the operation requires understanding language semantics (type hierarchy, cross-file refactoring, reference resolution, inspections, test/debug). Use built-ins for everything else (reading files, text edits, text search, shell commands, git, non-code files). When a semantic operation has a simple text-level equivalent (e.g., renaming a globally unique symbol), prefer the built-in for simplicity unless atomicity matters.

**Quantified threshold:** If the operation touches 3+ files and requires import/reference consistency, MCP Steroid saves enough manual coordination to justify the Kotlin scripting overhead. Below that threshold, built-ins are faster to invoke.

**Verdict:** Default to built-ins; escalate to MCP Steroid when the task requires semantic awareness across file boundaries or type-system knowledge that text search cannot provide.

