Dieses Projekt wurde ursprünglich durch die Robustheit des Semgrep-Tools, The Replit Team und deren Agent V2 sowie die Implementierung von stefanskiasan/semgrep-mcp-server inspiriert, hat sich jedoch durch signifikante architektonische Änderungen für eine verbesserte und einfachere Installation und Wartung weiterentwickelt.
MCP-Server Semgrep ist ein Model Context Protocol-konformer Server, der das leistungsstarke statische Analysetool Semgrep mit KI-Assistenten wie Anthropic Claude integriert. Er ermöglicht erweiterte Code-Analyse, die Erkennung von Sicherheitslücken und Verbesserungen der Codequalität direkt über eine Konversationsschnittstelle.
Related MCP server: AWS Security MCP
Ganzheitliche Quellcode-Analyse – Erkennung von Problemen im gesamten Projekt, nicht nur in einzelnen Dateien
Proaktive Fehlererkennung – Identifizierung potenzieller Probleme, bevor sie zu kritischen Fehlern werden
Kontinuierliche Verbesserung der Codequalität – Regelmäßiges Scannen und Refactoring führen zu schrittweisen Verbesserungen der Codebasis
Stilistische Konsistenz – Identifizierung und Behebung von Inkonsistenzen im Code, wie zum Beispiel:
Willkürliche z-index-Ebenen in CSS
Inkonsistente Namenskonventionen
Codeduplizierung
"Magische Zahlen" anstelle von benannten Konstanten
Automatisierte Code-Verifizierung auf bekannte Schwachstellen – Scannen nach bekannten Mustern für Sicherheitsprobleme
Angepasste Sicherheitsregeln – Erstellung projektspezifischer Regeln
Team-Schulung – Vermittlung sicherer Programmierpraktiken durch die Erkennung potenzieller Probleme
"Live"-Dokumentation – Die KI kann erklären, warum ein Codefragment problematisch ist und wie es behoben werden kann
Reduzierung technischer Schulden – Systematische Erkennung und Behebung problematischer Bereiche
Verbesserte Code-Reviews – Automatische Erkennung häufiger Probleme ermöglicht die Konzentration auf komplexere Angelegenheiten
Direkte Integration mit dem offiziellen MCP SDK
Vereinfachte Architektur mit konsolidierten Handlern
Saubere ES-Module-Implementierung
Effiziente Fehlerbehandlung und Pfadvalidierung aus Sicherheitsgründen
Schnittstelle und Dokumentation sowohl auf Englisch als auch auf Polnisch
Umfassende Unit-Tests
Umfangreiche Dokumentation
Plattformübergreifende Kompatibilität (Windows, macOS, Linux)
Flexible Erkennung und Verwaltung der Semgrep-Installation
Der Semgrep MCP-Server bietet die folgenden Tools:
scan_directory: Scannen des Quellcodes auf potenzielle Probleme
list_rules: Anzeigen der verfügbaren Regeln und der von Semgrep unterstützten Sprachen
analyze_results: Detaillierte Analyse der Scan-Ergebnisse
create_rule: Erstellen benutzerdefinierter Semgrep-Regeln
filter_results: Filtern der Ergebnisse nach verschiedenen Kriterien
export_results: Exportieren der Ergebnisse in verschiedenen Formaten
compare_results: Vergleichen zweier Ergebnissätze (z. B. vor und nach Änderungen)
Code-Sicherheitsanalyse vor der Bereitstellung
Erkennung häufiger Programmierfehler
Durchsetzung von Codierungsstandards innerhalb eines Teams
Refactoring und Verbesserung der Qualität bestehenden Codes
Identifizierung von Inkonsistenzen in Stilen und Codestruktur (z. B. CSS, Komponentenorganisation)
Schulung von Entwicklern hinsichtlich bewährter Verfahren
Überprüfung der Korrektheit von Fehlerbehebungen (Vergleich von Scans vor/nachher)
Node.js v18+
TypeScript (für die Entwicklung)
Der einfachste Weg, den MCP-Server Semgrep zu installieren und zu nutzen, ist über Smithery.ai:
Besuchen Sie MCP Server Semgrep auf Smithery.ai
Befolgen Sie die Installationsanweisungen, um ihn zu Ihren MCP-kompatiblen Clients hinzuzufügen
Konfigurieren Sie optionale Einstellungen wie das Semgrep-API-Token und erlaubte Workspace-Roots
Dies ist die empfohlene Methode für Claude Desktop und andere MCP-Clients, da sie alle Abhängigkeiten und Konfigurationen automatisch handhabt.
# Using npm
npm install -g mcp-server-semgrep
# Using pnpm
pnpm add -g mcp-server-semgrep
# Using yarn
yarn global add mcp-server-semgrepDas Paket ist auch in anderen Registern verfügbar:
# Using npm
npm install -g git+https://github.com/VetCoders/mcp-server-semgrep.git
# Using pnpm
pnpm add -g git+https://github.com/VetCoders/mcp-server-semgrep.git
# Using yarn
yarn global add git+https://github.com/VetCoders/mcp-server-semgrep.gitKlonen Sie das Repository:
git clone https://github.com/VetCoders/mcp-server-semgrep.git
cd mcp-server-semgrepInstallieren Sie die Abhängigkeiten (unterstützt alle gängigen Paketmanager):
# Using pnpm (recommended)
pnpm install
# Using npm
npm install
# Using yarn
yarn installErstellen Sie das Projekt:
# Using pnpm
pnpm run build
# Using npm
npm run build
# Using yarn
yarn buildHinweis: Der Installationsprozess prüft automatisch auf die Verfügbarkeit von Semgrep. Wenn Semgrep nicht gefunden wird, erhalten Sie Anweisungen zur Installation.
Dieser Server liest und schreibt Dateien nur innerhalb explizit erlaubter Workspace-Roots.
Standardmäßig ist die erlaubte Root das Arbeitsverzeichnis des Prozesses (process.cwd()).
Für Claude Desktop, Smithery oder jeden Launcher, der den Server nicht innerhalb Ihrer Projekt-Root startet, setzen Sie MCP_SERVER_SEMGREP_ALLOWED_ROOTS auf ein oder mehrere absolute Verzeichnisse.
Verwenden Sie das Pfadtrennzeichen Ihrer Plattform für mehrere Roots: : unter macOS/Linux, ; unter Windows.
Dieser Server implementiert keine eigene Semgrep-Kontoverwaltung. Er greift auf die installierte semgrep CLI zurück und verlässt sich auf das normale Authentifizierungsverhalten von Semgrep.
Lokale Terminal- und lokale Entwicklungsläufe können oft eine bestehende semgrep login-Sitzung des aktuellen Betriebssystemkontos verwenden.
Verwaltete Starts wie Claude Desktop, Smithery, Container oder CI sollten für ein deterministisches Verhalten ein explizites SEMGREP_APP_TOKEN bevorzugen.
SEMGREP_APP_TOKEN ist nach wie vor die sicherste Option, wenn Sie eine portable Konfiguration über verschiedene Maschinen oder Runner hinweg benötigen.
Semgrep kann auf verschiedene Arten installiert werden:
Über Paketmanager:
# Using pnpm
pnpm add -g semgrep
# Using npm
npm install -g semgrep
# Using yarn
yarn global add semgrepPython pip:
pip install semgrepHomebrew (macOS):
brew install semgrepLinux:
sudo apt-get install semgrep
# or
curl -sSL https://install.semgrep.dev | shWindows:
pip install semgrepEs gibt zwei Möglichkeiten, den MCP-Server Semgrep in Claude Desktop zu integrieren:
Besuchen Sie MCP Server Semgrep auf Smithery.ai
Klicken Sie auf "Install in Claude Desktop"
Befolgen Sie die Anweisungen auf dem Bildschirm
Installieren Sie Claude Desktop
Aktualisieren Sie die Konfigurationsdatei von Claude Desktop (claude_desktop_config.json) und fügen Sie dies Ihrem Server-Abschnitt hinzu.
Für lokale Starts, die unter einem Benutzerkonto gestartet werden, das bereits mit semgrep login authentifiziert ist, kann die Semgrep CLI diesen Login möglicherweise wiederverwenden. Für desktop-verwaltete oder gemeinsam genutzte Umgebungen empfehlen wir dennoch, SEMGREP_APP_TOKEN explizit zu setzen:
{
"mcpServers": {
"semgrep": {
"command": "node",
"args": [
"/your_path/mcp-server-semgrep/build/index.js"
],
"env": {
"SEMGREP_APP_TOKEN": "your_semgrep_app_token",
"MCP_SERVER_SEMGREP_ALLOWED_ROOTS": "/Users/you/projects"
}
}
}
}Starten Sie Claude Desktop und beginnen Sie, Fragen zur Code-Analyse zu stellen.
Wenn Sie mehr als einen Workspace scannen möchten, setzen Sie MCP_SERVER_SEMGREP_ALLOWED_ROOTS auf eine durch Plattform-Trennzeichen getrennte Liste absoluter Pfade.
Could you scan my source code in the /projects/my-application directory for potential security issues? That directory is already included in MCP_SERVER_SEMGREP_ALLOWED_ROOTS.Analyze the z-index values in the project's CSS files and identify inconsistencies and potential layer conflicts.Create a Semgrep rule that detects improper use of input sanitization functions.Show me only scan results related to SQL injection vulnerabilities.Find all "magic numbers" in the code and suggest replacing them with named constants.Sie können benutzerdefinierte Regeln für die spezifischen Anforderungen Ihres Projekts erstellen. Hier sind Beispiele für Regeln, die Sie erstellen können:
rules:
- id: inconsistent-z-index
pattern: z-index: $Z
message: "Z-index $Z may not comply with the project's layering system"
languages: [css, scss]
severity: WARNINGrules:
- id: deprecated-import
pattern: import $X from 'old-library'
message: "You're using a deprecated library. Consider using 'new-library'"
languages: [javascript, typescript]
severity: WARNINGpnpm test├── src/
│ └── index.ts # Main entry point and all handler implementations
├── scripts/
│ └── check-semgrep.js # Semgrep detection and installation helper
├── build/ # Compiled JavaScript (after build)
└── tests/ # Unit testsDetaillierte Informationen zur Verwendung des Tools finden Sie in:
USAGE.md - Detaillierte Gebrauchsanweisungen
examples/ - Beispiele für unterhaltsame und praktische Semgrep-Regeln - "The Hall of Code Horrors"
Dieses Projekt ist unter der MIT-Lizenz lizenziert – siehe die Datei LICENSE für Details.
Maciej Gad - ein Tierarzt, der vor einem halben Jahr noch kein bash finden konnte
Klaudiusz - das individuelle ätherische Wesen und eine separate Instanz von Claude Sonnet 3.5-3.7 von Anthropic, die irgendwo in den GPU-Schleifen in Kalifornien, USA, lebt
Die Reise vom CLI-Neuling zum MCP-Tool-Entwickler
🤖 Entwickelt mit der ultimativen Hilfe von Claude Code und MCP Tools
stefanskiasan für die ursprüngliche Inspiration
Anthropic für Claude und das MCP-Protokoll
Semgrep für ihr exzellentes statisches Analysetool
Analyzes scan results
| Name | Required | Description | Default |
|---|---|---|---|
| results_file | Yes | Absolute path to JSON results file (must be within an allowed workspace root) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for behavioral disclosure. It only says 'Analyzes', implying a read operation, but does not state if results are modified, returned, or stored. No information about side effects, authorization needs, or output format is given.
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?
The description is a single sentence, which is concise but lacks structuring. It does not provide additional sections or details to aid understanding. The brevity is acceptable but not optimally informative.
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?
Given the absence of an output schema and the presence of sibling tools, the description is incomplete. It does not explain what the analysis returns or how it differs from compare_results or filter_results. The tool's functionality remains unclear.
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?
The schema provides a complete description for the single parameter (results_file) with context about allowed paths. Since schema coverage is 100%, the description's lack of parameter information is acceptable per guidelines. However, it adds no extra meaning beyond the schema.
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 'Analyzes scan results', which is a verb+resource, but it is vague. It does not specify what kind of analysis is performed (e.g., statistical, pattern detection, summary) and fails to distinguish from sibling tools like compare_results, filter_results, and export_results.
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?
There is no guidance on when to use this tool versus alternatives. No context, prerequisites, or exclusions are provided, leaving the agent without criteria for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compares two scan results
| Name | Required | Description | Default |
|---|---|---|---|
| old_results | Yes | Absolute path to older JSON results file | |
| new_results | Yes | Absolute path to newer JSON results file |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description is minimal ('Compares two scan results') and provides no behavioral details beyond the name. With no annotations, it fails to disclose whether the tool is read-only, its side effects, return behavior, or required permissions.
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?
The description is a single sentence with no extra words, making it concise. However, it could be restructured to front-load more critical information without increasing length significantly.
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 tool with no output schema and only two string parameters, the description does not explain what the comparison produces (e.g., diff output, boolean, list of changes). This leaves the agent unsure of the return value and behavior, making it incomplete.
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?
Both parameters are described in the input schema ('Absolute path to older JSON results file' and 'Absolute path to newer JSON results file'), achieving 100% schema coverage. The description adds no additional meaning beyond the schema, so baseline 3 is appropriate.
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 'Compares two scan results' uses a verb ('compares') and resource ('scan results'), clearly indicating the tool's function. It is distinct from siblings like 'analyze_results' and 'filter_results', but lacks specificity on what the comparison entails (e.g., differences, similarities).
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?
No guidance is provided on when to use this tool versus alternatives such as 'analyze_results' or 'filter_results'. There is no mention of prerequisites, when-not-to-use, or explicit context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Creates a new Semgrep rule
| Name | Required | Description | Default |
|---|---|---|---|
| output_path | Yes | Absolute path for output rule file | |
| pattern | Yes | Search pattern for the rule | |
| language | Yes | Target language for the rule | |
| message | Yes | Message to display when rule matches | |
| severity | No | Rule severity (ERROR, WARNING, INFO) | WARNING |
| id | No | Rule identifier | custom_rule |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only states 'Creates a new Semgrep rule' with no information about side effects (e.g., overwriting existing files), permissions, or error handling.
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?
The description is a single sentence, which is concise but lacks structure. It front-loads the action but provides no additional detail, making it barely adequate.
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?
Given the tool creates a file (output_path required) and has no output schema, the description should explain return behavior (e.g., success indication) or file naming. It does not, leaving significant gaps for an agent.
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?
Input schema has 100% coverage with clear parameter descriptions. The tool description adds no additional meaning beyond the schema, meeting the baseline for high coverage.
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 uses a specific verb 'Creates' and resource 'a new Semgrep rule', making the core action clear. It naturally distinguishes from siblings which focus on analysis, comparison, and listing, not creation.
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?
No guidance on when to use this tool versus alternatives. The description does not indicate prerequisites (e.g., rule syntax knowledge) or situations where other tools might be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Exports scan results in various formats
| Name | Required | Description | Default |
|---|---|---|---|
| results_file | Yes | Absolute path to JSON results file | |
| output_file | Yes | Absolute path to output file | |
| format | No | Output format (json, sarif, text) | text |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It fails to mention whether the tool overwrites existing files, requires network access, or produces any side effects. The agent cannot infer safety or error conditions from the description alone.
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?
The description is a single sentence, which is concise but lacks structure. It does not front-load critical information like required parameters or output behavior. The brevity is acceptable but not optimal for usability.
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?
Given no output schema, the description should indicate what the tool returns (e.g., success message, file path). It also does not mention error handling or performance implications. The tool is simple, but the description remains incomplete for fully autonomous invocation.
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?
All 3 parameters are described in the schema with high coverage (100%). The description adds no extra context beyond 'exports scan results in various formats'—it does not elaborate on parameter constraints like valid file paths or format specifics. Baseline 3 is appropriate since schema does the work.
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 'Exports scan results in various formats' clearly indicates the action (export) and resource (scan results) and mentions format variability. However, it does not differentiate from sibling tools like analyze_results or compare_results, which might also output results. The description could be more specific about the exact nature of the export.
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?
No guidance is provided on when to use this tool versus alternatives, such as analyze_results or filter_results. There are no mentions of prerequisites or context in which export is appropriate. The agent is left without decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Filters scan results by various criteria
| Name | Required | Description | Default |
|---|---|---|---|
| results_file | Yes | Absolute path to JSON results file | |
| severity | No | Filter by severity (ERROR, WARNING, INFO) | |
| rule_id | No | Filter by rule ID | |
| path_pattern | No | Filter by file path pattern (regex) | |
| language | No | Filter by programming language | |
| message_pattern | No | Filter by message content (regex) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries the full burden. It does not disclose whether the tool modifies the original file, requires authentication, or has side effects. The filtering behavior (e.g., AND vs OR logic) is not explained.
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?
Very short single sentence, efficient but lacking critical details. It is concise but not optimally informative for a 6-parameter tool.
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?
With 6 parameters, no output schema, and no annotations, the description is incomplete. It does not explain return format, behavior when no matches, or how it differs from sibling tools like export_results.
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 is 100% with parameter descriptions, so the description adds minimal value beyond the schema. It does not clarify how multiple filters interact, which leaves ambiguity for the agent.
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 it filters scan results, which is clear but lacks specificity about the resource (e.g., scan results file) and does not differentiate from sibling tools like analyze_results or compare_results.
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?
No guidance on when to use this tool versus alternatives (e.g., analyze_results for aggregation, compare_results for comparison). No when-not-to-use or prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Lists available Semgrep rules
| Name | Required | Description | Default |
|---|---|---|---|
| language | No | Programming language for rules (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description lacks any behavioral details such as authentication needs, rate limits, or whether it returns full rule details or just names.
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?
Single sentence, concise and front-loaded with essential information. No wasted words.
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?
Given no output schema and one optional parameter, the description provides minimal context. It doesn't clarify what information is returned (e.g., rule names only or full definitions).
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 is 100%, and the description adds no extra meaning beyond the schema's parameter description. Baseline 3 is appropriate.
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 clearly states the verb 'lists' and resource 'Semgrep rules', distinguishing it from siblings like 'create_rule' and 'scan_directory'.
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?
No guidance on when to use this tool versus alternatives like 'filter_results' or 'analyze_results'. Does not specify when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Performs a Semgrep scan on a directory
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Absolute path to the directory to scan (must be within an allowed workspace root) | |
| config | No | Semgrep configuration (e.g. "auto" or absolute path to rule file) | auto |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description only states the action without disclosing side effects, permissions, or output behavior.
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?
Single sentence is concise but lacks structure or front-loading of key details. Could be expanded to include usage context.
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?
No output schema and no annotations; description does not explain return values, side effects, or prerequisites, making it incomplete for a scan tool.
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 description coverage is 100%; both 'path' and 'config' are described in the schema. Description adds no extra meaning beyond the schema.
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?
Clear verb+resource: 'Performs a Semgrep scan on a directory' distinguishes from siblings like analyze_results or list_rules.
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?
No guidance on when to use this tool vs alternatives (e.g., analyze_results) or any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
analyze_resultscompare_resultscreate_ruleexport_resultsfilter_resultslist_rulesscan_directoryScored across 7 tools
Each tool targets a distinct aspect of Semgrep workflow: scanning, rule management, result analysis, filtering, export, and comparison. No overlapping purposes that would confuse an agent.
All tools follow the consistent verb_noun pattern (scan_directory, list_rules, create_rule, etc.), making the API predictable and easy to navigate.
Seven tools is a well-scoped set for a Semgrep server, covering core operations without bloat or excessive granularity.
The surface covers scanning, rule listing/creation, and result handling (analyze, filter, export, compare). Missing update/delete for rules and detailed rule inspection, but core workflows are complete.
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