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Swagger MCP

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Swagger MCP

Ein MCP-Server, der eine Verbindung zu einer Swagger-Spezifikation herstellt und einer KI hilft, alle erforderlichen Modelle zu erstellen, um einen MCP-Server für diesen Dienst zu generieren.

Merkmale

  • Lädt eine Swagger-Spezifikation herunter und speichert sie lokal, damit Sie schneller darauf zugreifen können.

  • Gibt eine Liste aller Endpunkte und ihrer HTTP-Methoden und Beschreibungen zurück

  • Gibt eine Liste aller Modelle zurück

  • Gibt ein Modell zurück

  • Gibt den Dienst zurück, um eine Verbindung zum Endpunkt herzustellen

  • Gibt MCP-Funktionsdefinitionen zurück

  • Generiert vollständige MCP-Tooldefinitionen mit vollständigen Schemainformationen

  • Enthält KI-spezifische Anweisungen in den Werkzeugbeschreibungen

Related MCP server: Mcp-Swagger-Server

Voraussetzungen

  • Node.js (v14 oder höher)

  • npm oder yarn

Installation

  1. Klonen Sie das Repository:

git clone https://github.com/readingdancer/swagger-mcp.git
cd swagger-mcp

  1. Installieren Sie Abhängigkeiten:

npm install

  1. Erstellen Sie eine .env Datei basierend auf der .env.example -Datei:

cp .env.example .env

  1. Aktualisieren Sie die .env Datei.

Konfiguration

Bearbeiten Sie die .env Datei, um die Anwendung zu konfigurieren:

  • PORT : Der Port, auf dem der Server läuft (Standard: 3000)

  • NODE_ENV : Die Umgebung (Entwicklung, Produktion, Test)

  • LOG_LEVEL : Protokollierungsebene (Info, Fehler, Debug)

Verwendung

Erstellen der Anwendung

Erstellen Sie die Anwendung:

npm run build

Dadurch wird der TypeScript-Code kompiliert und ist bereit, als MCP-Server verwendet zu werden

Wird als MCP-Server ausgeführt

So führen Sie es als MCP-Server zur Integration mit Cursor und anderen Anwendungen aus:

node build/index.js

Verwenden des MCP-Inspektors

So führen Sie den MCP-Inspector zum Debuggen aus:

npm run inspector

Zum Cursor hinzufügen

So fügen Sie diesen MCP-Server zu Cursor hinzu:

  1. Öffnen Sie Cursoreinstellungen > Funktionen > MCP

  2. Klicken Sie auf „+ Neuen MCP-Server hinzufügen“

  3. Geben Sie einen Namen für den Server ein (z. B. „Swagger MCP“)

  4. Wählen Sie „stdio“ als Transporttyp

  5. Geben Sie den Befehl zum Ausführen des Servers ein: node path/to/swagger-mcp/build/index.js und fügen Sie dann bei Bedarf die Befehlszeilenargumente wie oben erwähnt hinzu.

  6. Klicken Sie auf „Hinzufügen“

Die Swagger MCP-Tools stehen jetzt dem Cursor-Agenten in Composer zur Verfügung.

Verfügbare Swagger MCP-Tools

Die folgenden Tools sind über den MCP-Server verfügbar:

  • getSwaggerDefinition : Lädt eine Swagger-Definition von einer URL herunter

  • listEndpoints : Listet alle Endpunkte aus der Swagger-Definition auf

  • listEndpointModels : Listet alle Modelle auf, die von einem bestimmten Endpunkt verwendet werden

  • generateModelCode : Generiert TypeScript-Code für ein Modell

  • generateEndpointToolCode : Generiert TypeScript-Code für eine MCP-Tooldefinition

Verfügbare Swagger MCP-Eingabeaufforderungen

Der Server bietet außerdem MCP-Eingabeaufforderungen, die KI-Assistenten durch gängige Arbeitsabläufe führen:

  • add-endpoint : Eine Schritt-für-Schritt-Anleitung zum Hinzufügen eines neuen Endpunkts mit den Swagger MCP-Tools

Um eine Eingabeaufforderung zu verwenden, können Clients eine prompts/get -Anforderung mit dem Namen der Eingabeaufforderung und optionalen Argumenten stellen:

{
  "method": "prompts/get",
  "params": {
    "name": "add-endpoint",
    "arguments": {
      "swaggerUrl": "https://petstore.swagger.io/v2/swagger.json",
      "endpointPath": "/pets/{id}",
      "httpMethod": "GET"
    }
  }
}

Die Eingabeaufforderung gibt eine Reihe von Nachrichten zurück, die den KI-Assistenten durch den genauen Prozess führen, der zum Hinzufügen eines neuen Endpunkts erforderlich ist.

Einrichten Ihres neuen Projekts

Bitten Sie den Agenten zunächst, die Swagger-Datei abzurufen. Geben Sie ihm unbedingt die URL für die Swagger-Datei oder zumindest eine Möglichkeit, diese für Sie zu finden. Dadurch wird die Datei heruntergeladen und lokal unter einem gehashten Dateinamen gespeichert. Dieser Dateiname wird automatisch zu einer .swagger-mcp Einstellungsdatei im Stammverzeichnis Ihrer aktuellen Lösung hinzugefügt.

Automatisch generierte .swagger-mcp-Konfigurationsdatei

SWAGGER_FILENAME = TheFilenameOfTheLocallyStoredSwaggerFile

Diese einfache Konfigurationsdatei verknüpft Ihr aktuelles Projekt mit einer bestimmten Swagger-API. Wir können sie in Zukunft verwenden, um weitere Details zu speichern.

Nach der Konfiguration kann das MCP Ihre Swagger-Definition finden und sie mit Ihrer aktuellen Lösung verknüpfen. Dadurch wird die Anzahl der API-Aufrufe reduziert, die erforderlich sind, um das Projekt und die Aufgaben abzurufen, die mit der Lösung zusammenhängen, an der Sie arbeiten.

Verbesserter MCP-Tool-Codegenerator

Der MCP-Toolcodegenerator wurde erweitert, um vollständigere und benutzerfreundlichere Tooldefinitionen bereitzustellen:

Wichtige Verbesserungen

  1. Vollständige Schemainformationen : Der Generator enthält jetzt vollständige Schemainformationen für alle Modelle, einschließlich verschachtelter Objekte, direkt im Eingabeschema.

  2. Bessere Parameterbenennung : Parameternamen sind jetzt semantischer und vermeiden problematische Zeichen wie Punkte (z. B. taskRequest statt task.Request ).

  3. Semantische Toolnamen : Toolnamen sind jetzt aussagekräftiger und folgen konsistenten Namenskonventionen basierend auf der HTTP-Methode und dem Ressourcenpfad.

  4. Unterstützung für YAML-Swagger-Dateien : Der Generator unterstützt jetzt sowohl JSON- als auch YAML-Swagger-Definitionsdateien.

  5. Verbesserte Dokumentation : Generierte Werkzeugdefinitionen enthalten umfassende Beschreibungen aller Parameter und Eigenschaften.

  6. Keine externen Abhängigkeiten : Der generierte Code erfordert keinen Import externer Modelldateien, wodurch er eigenständiger und einfacher zu verwenden ist.

  7. KI-spezifische Anweisungen : Die Toolbeschreibungen enthalten jetzt spezielle Anweisungen für KI-Agenten, die ihnen helfen zu verstehen, wie sie die Tools effektiv nutzen können.

Beispielverwendung

So generieren Sie eine MCP-Tooldefinition für einen Endpunkt:

import generateEndpointToolCode from './services/generateEndpointToolCode.js';

const toolCode = await generateEndpointToolCode({
  path: '/pets',
  method: 'POST',
  swaggerFilePath: './petstore.json',
  singularizeResourceNames: true
});

console.log(toolCode);

Dadurch wird eine vollständige MCP-Tooldefinition mit vollständigen Schemainformationen für den POST /pets-Endpunkt generiert.

Lizenz

Dieses Projekt ist unter der MIT-Lizenz lizenziert – Einzelheiten finden Sie in der Datei LICENSE .

MCP-Eingabeaufforderungen für KI-Assistenten

Damit KI-Assistenten die Swagger MCP-Tools effektiv nutzen können, haben wir eine Reihe von Eingabeaufforderungen erstellt, die sie durch häufige Aufgaben führen. Diese Eingabeaufforderungen bieten Schritt-für-Schritt-Anleitungen für Prozesse wie das Hinzufügen neuer Endpunkte, die Verwendung generierter Modelle und mehr.

Die vollständige Sammlung der Eingabeaufforderungen finden Sie in der Datei PROMPTS.md .

Beispiel-Anwendungsfall: Wenn Sie einen KI-Assistenten bitten, Ihrem Projekt einen neuen Endpunkt hinzuzufügen, können Sie auf die Eingabeaufforderung „Hinzufügen eines neuen Endpunkts“ verweisen, um sicherzustellen, dass der Assistent den richtigen Prozess in der richtigen Reihenfolge befolgt.

Available Tools

5 tools
execute_api_requestC

Execute an API request to a specific endpoint

ParametersJSON Schema
NameRequiredDescriptionDefault
methodYesHTTP method (GET, POST, PUT, DELETE, etc.)
pathYesThe endpoint path (e.g., '/users/123')
paramsNoQuery parameters as key-value pairs
bodyNoRequest body as a JSON object (for POST/PUT/PATCH)
headersNoCustom headers as key-value pairs

TDQS

C2.6/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool executes an API request but fails to describe critical traits such as authentication requirements, error handling, rate limits, side effects (e.g., whether it modifies data), or response format. This leaves significant gaps for a tool that could perform destructive operations like DELETE.

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 a single, efficient sentence that gets straight to the point without unnecessary words. It's appropriately sized for a general-purpose tool, though it could be more front-loaded with key details if it were more comprehensive.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of executing arbitrary API requests (with potential for mutations, auth needs, etc.), no annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects, return values, or usage context, making it inadequate for safe and effective tool selection by an AI agent.

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 input schema already documents all 5 parameters thoroughly. The description adds no additional meaning beyond what's in the schema (e.g., it doesn't clarify parameter interactions or provide examples), resulting in a baseline score of 3 where the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the action ('execute') and resource ('API request to a specific endpoint'), which provides a basic purpose. However, it lacks specificity about what kind of API or system this targets, and it doesn't clearly differentiate from sibling tools like 'fetch_swagger_info' or 'validate_api_response', which might involve similar API interactions but for different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context (e.g., for testing or production calls), or exclusions, leaving the agent to infer usage based on the generic name and parameters alone, which is insufficient given the presence of sibling tools like 'get_endpoint_details' or 'list_endpoints'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

fetch_swagger_infoC

Fetch Swagger/OpenAPI documentation to discover available API endpoints

ParametersJSON Schema
NameRequiredDescriptionDefault
urlNoURL to the swagger.json or swagger.yaml file. If not provided, will try to use the base URL with common Swagger paths.

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't describe how it behaves—such as whether it makes network requests, handles errors, returns structured data, or has any side effects. This leaves significant gaps for an agent to understand the tool's operation.

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 description is a single, clear sentence that efficiently conveys the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

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 incomplete for a tool that likely returns complex API documentation. It doesn't explain what the output looks like (e.g., JSON/YAML structure), potential errors, or how it interacts with sibling tools, leaving the agent with insufficient context for effective use.

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?

The input schema has 100% description coverage, so the schema already documents the single parameter ('url') adequately. The description adds no additional meaning or context about the parameter beyond what's in the schema, such as examples or constraints, but this is acceptable given the high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('fetch') and resource ('Swagger/OpenAPI documentation'), and indicates what it's used for ('to discover available API endpoints'). However, it doesn't explicitly differentiate this from sibling tools like 'list_endpoints' or 'get_endpoint_details', which might have overlapping functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'list_endpoints' or 'get_endpoint_details'. It mentions the purpose but doesn't specify scenarios, prerequisites, or exclusions that would help an agent choose between these related tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_endpoint_detailsB

Get detailed information about a specific API endpoint

ParametersJSON Schema
NameRequiredDescriptionDefault
pathYesThe endpoint path to get details for (e.g., '/users/{id}')
methodYesThe HTTP method (GET, POST, PUT, DELETE, etc.)

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves information, implying a read-only operation, but doesn't specify aspects like authentication requirements, rate limits, error handling, or the format of the returned details. This is a significant gap for a tool with no annotation coverage.

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 description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part earns its place by directly stating the tool's function, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (2 required parameters, no output schema, no annotations), the description is minimally adequate. It clarifies the purpose but lacks behavioral details and usage guidelines. Without an output schema, it doesn't explain return values, which could be a gap, but the description focuses on the input aspect, making it borderline complete for a basic read operation.

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?

The schema description coverage is 100%, with clear descriptions for both parameters ('path' and 'method'), so the schema does the heavy lifting. The description adds no additional parameter semantics beyond implying that these inputs identify a 'specific API endpoint', which is already inferred from the schema. Baseline 3 is appropriate as the schema provides adequate documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Get') and resource ('detailed information about a specific API endpoint'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'fetch_swagger_info' or 'list_endpoints', which likely provide similar API information but with different scopes or formats.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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. With siblings like 'fetch_swagger_info' (which might retrieve broader API documentation) and 'list_endpoints' (which might list endpoints without details), the description lacks context for selection, leaving the agent to infer usage based on tool names alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_endpointsB

List all available API endpoints after fetching Swagger documentation

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.4/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that endpoints are listed 'after fetching Swagger documentation', hinting at a dependency or sequence, but it doesn't describe what 'list' entails (e.g., format, pagination, or if it's a read-only operation). For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 description is a single, efficient sentence that front-loads the core action ('List all available API endpoints') and adds necessary context ('after fetching Swagger documentation'). There is no wasted verbiage, and every part of the sentence contributes to understanding the tool's purpose and sequence.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity is low (0 parameters, no output schema), the description is adequate but has gaps. It covers the purpose and hints at a sequence, but without annotations or output schema, it lacks details on behavior (e.g., what 'list' returns, any side effects). For a simple listing tool, it's minimally viable but could be more complete by clarifying the output or dependencies.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% description coverage, so the schema fully documents the lack of inputs. The description doesn't need to add parameter details, and it appropriately doesn't mention any. Baseline is 4 for 0 parameters, as the description doesn't introduce confusion or redundancy.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('List all available API endpoints') and the resource ('API endpoints'), making the purpose immediately understandable. It distinguishes itself from siblings like 'fetch_swagger_info' by specifying it operates 'after fetching Swagger documentation', though it doesn't explicitly contrast with all siblings like 'get_endpoint_details'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage by mentioning 'after fetching Swagger documentation', suggesting a prerequisite or sequence, but it doesn't provide explicit guidance on when to use this tool versus alternatives like 'get_endpoint_details' or 'execute_api_request'. No exclusions or clear alternatives are stated, leaving usage context somewhat vague.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

validate_api_responseB

Validate an API response against the schema from Swagger documentation

ParametersJSON Schema
NameRequiredDescriptionDefault
pathYesThe endpoint path
methodYesThe HTTP method
statusCodeYesThe HTTP status code
responseBodyYesThe response body to validate

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool validates responses but doesn't explain how validation works (e.g., returns validation errors, success/failure status), what happens on failure, or any side effects. For a validation tool with zero annotation coverage, this is a significant gap in transparency.

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 description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and wastes no space, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (4 parameters, validation logic) and lack of annotations or output schema, the description is minimally adequate. It covers the basic purpose but fails to provide critical context like validation outcomes, error handling, or integration with sibling tools, leaving gaps for an AI agent.

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?

The input schema has 100% description coverage, clearly documenting all four parameters (path, method, statusCode, responseBody). The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Validate an API response against the schema from Swagger documentation'. It specifies the verb 'validate' and the resource 'API response', making it understandable. However, it doesn't explicitly differentiate from sibling tools like 'execute_api_request' or 'fetch_swagger_info', which might handle related but distinct operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing Swagger documentation loaded), context (e.g., after an API call), or exclusions. With siblings like 'execute_api_request' and 'fetch_swagger_info', this lack of differentiation leaves usage ambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 5 tool updates
    • First observedexecute_api_request
    • First observedfetch_swagger_info
    • First observedget_endpoint_details
    • First observedlist_endpoints
    • First observedvalidate_api_response
Deploy Server
Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

TDQS

A3.5/5.0

Scored across 5 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: fetching documentation, listing endpoints, getting endpoint details, executing requests, and validating responses. The descriptions make it easy to tell them apart, as they target different stages of the API workflow.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (e.g., fetch_swagger_info, list_endpoints, execute_api_request). The naming is predictable and readable throughout, with no deviations in style or convention.

Tool Count5/5

With 5 tools, the server is well-scoped for its purpose of interacting with Swagger/OpenAPI documentation and APIs. Each tool earns its place by covering distinct aspects of the workflow, from discovery to execution and validation.

Completeness5/5

The tool set provides complete coverage for the domain: it supports fetching documentation, listing and detailing endpoints, executing requests, and validating responses. There are no obvious gaps, and agents can handle the full API lifecycle without dead ends.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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