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DevTwin MCP

Dale a los agentes de codificación de IA una comprensión viva y estructurada de tu entorno de desarrollo local.

DevTwin es un servidor del Model Context Protocol (MCP) que responde a una pregunta central para un agente de codificación de IA: ¿por qué el entorno de este desarrollador es diferente, está roto o no es saludable?

Detecta la tecnología del proyecto, comprueba las versiones de runtime instaladas frente a lo que el proyecto realmente requiere, inspecciona el estado de las dependencias y del lockfile, encuentra los servicios locales necesarios (Postgres, Redis, ...) y si están en ejecución, comprueba puertos y el estado de Git, y convierte todo eso en diagnósticos estructurados basados en evidencia -- sin enviar nunca tu entorno a un backend en la nube, y sin exponer nunca valores secretos al modelo.

Contenido

Related MCP server: devscope

Por qué existe DevTwin

  • Los agentes de codificación de IA leen bien el código, pero son ciegos al entorno en el que ese código realmente se ejecuta.

  • "¿Por qué falla npm test en mi máquina?" normalmente no tiene nada que ver con el código -- es una versión de Node incorrecta, un servicio que no está en ejecución, o dependencias que nunca se instalaron.

  • DevTwin le da a un agente la misma señal que un ingeniero senior recopilaría a mano -- node --version, git status, lsof -i :5432, docker ps -- como llamadas a herramientas estructuradas en lugar de conjeturas.

Preguntas frecuentes: Claude CLI ya tiene un shell, ¿entonces por qué un MCP?

Esta suele ser la primera pregunta que hace un desarrollador, y es justa. En un cliente como Claude Code que ya tiene una herramienta Bash, puedes simplemente pedirle que ejecute node --version, docker ps, lsof -i :5432, etc. directamente -- no se necesita ningún servidor MCP. La brecha que cierra DevTwin no es "¿se puede hacer esto en absoluto?" -- es esta:

Sin DevTwin (Bash sin procesar)

Con DevTwin

El agente puede ejecutar cualquier cosa, incluidos comandos destructivos, incluso sin querer.

Cero ejecución arbitraria -- solo una lista fija de comprobaciones de solo lectura/seguras. Ver Modelo de seguridad.

Elige una investigación diferente en cada sesión; puede pasar por alto casos límite del ecosistema (Gradle wrapper vs. Gradle del sistema, .nvmrc vs. engines de package.json).

La misma comprobación seleccionada y probada cada vez, para cada ecosistema.

Un comando como cat .env puede llevar un valor secreto real directamente a la conversación.

Estructuralmente nunca devuelve valores secretos -- solo presencia/ausencia. Ver Modelo de privacidad.

Solo funciona en clientes que tienen una herramienta de shell (no Claude Desktop, algunos plugins de IDE).

Funciona en cualquier cliente MCP, con shell o sin shell.

~6 idas y vueltas separadas para diagnosticar un fallo.

1 llamada. Ver el ejemplo práctico.

Respuesta honesta específicamente para Claude CLI: dado que ya tiene Bash, la ventaja de DevTwin allí es menor que "capacidad que no tenías" -- son garantías de seguridad y salida estructurada y consistente, no acceso completamente nuevo. Por eso tampoco es gratis -- ver Coste de tokens para saber cuánto cuesta realmente conectarlo, y cuándo merece la pena.

Algunas preguntas más que vale la pena hacerse antes de adoptar esto:

"¿No es esto solo un script doctor (make doctor, bin/setup) con pasos extra?" Conceptualmente, sí -- muchos repositorios maduros ya escriben uno a mano. La diferencia de DevTwin es que la mayoría de los repositorios no tienen uno, escribir uno bueno por ecosistema es trabajo real, su salida es JSON estructurado sobre el que un agente puede razonar en lugar de texto plano que lee un humano, y las mismas 10 herramientas funcionan de forma idéntica en todos los repositorios en lugar de un script hecho a medida por proyecto con sus propias convenciones y puntos ciegos.

"¿Esto solo funciona con Claude / Claude Code?" No. DevTwin habla el Model Context Protocol estándar -- cualquier cliente compatible con MCP (Claude Desktop, Cursor, Windsurf, etc.) puede conectarse a él de la misma manera. Nada de esto es específico de Claude.

"¿Es seguro depender de esto -- se mantiene activamente?" Está en estado Alpha y es un proyecto joven -- lee el código (es corto) antes de confiar en él en un flujo de trabajo del que dependas, igual que harías con cualquier nueva dependencia de herramientas de desarrollo.

"¿Podría sugerir algo incorrecto, o ejecutar automáticamente una recomendación mala?" Ninguna herramienta aquí ejecuta una cadena de recommendations -- esas son solo texto para que el agente (o tú) lo lea y decida. dev_check es la única herramienta que ejecuta algo, y solo comandos que reconoció por sí misma contra una lista fija -- ver Modelo de seguridad.

"¿Llama a casa o envía telemetría a algún sitio?" No. Cero llamadas de red propias -- ver Arquitectura local-first.

"No quiero que un servidor MCP ejecute ningún comando en mi máquina." 9 de las 10 herramientas son de solo lectura puras (lectura de archivos, comprobaciones de versiones). Solo dev_check ejecuta algo, y solo comandos que el propio DevTwin reconoció de los archivos del proyecto, comprobados contra una lista de permitidos, con shell=False y un tiempo de espera -- ver Modelo de seguridad para saber exactamente qué permite y qué no.

Beneficios

  • Menos diagnósticos erróneos. Sin DevTwin, un agente que depura un fallo solo puede leer código y adivinar -- a menudo propondrá una corrección de código para lo que en realidad es una versión de Node incorrecta o una base de datos detenida. DevTwin le da la verdad del terreno en lugar de una conjetura.

  • Una llamada en lugar de muchas. Una sola llamada a dev_health agrupa ~10 comprobaciones subyacentes (versiones de runtime, estado de dependencias, servicios, puertos, Git) en un resultado estructurado y puntuado -- en lugar de que un agente haga una docena de idas y vueltas de shell separadas y analice la salida de CLI sin procesar cada vez.

  • La misma comprobación cada vez. Las comprobaciones exactas por ecosistema (Gradle wrapper vs. Gradle del sistema, .nvmrc vs. engines de package.json, ...) están codificadas una vez, para que el diagnóstico sea consistente entre sesiones en lugar de depender de lo que al agente se le ocurra ejecutar.

  • Más seguro que darle un shell a un agente. Sin ejecución arbitraria de comandos, sin operaciones destructivas, nunca -- ver Modelo de seguridad.

  • Los secretos nunca se tocan. Las variables de entorno que parecen secretas se comprueban solo por presencia; los valores nunca se leen ni se devuelven -- ver Modelo de privacidad.

  • Funciona incluso donde el agente no tiene shell. Los clientes MCP sin herramienta Bash (algunos asistentes de IDE, agentes restringidos) obtienen esta capacidad por completo, no cero capacidad.

Coste de tokens

Números reales, no una estimación -- medidos directamente de los esquemas de herramientas MCP de este propio servidor (mcp.list_tools()) y de una respuesta real de dev_health(), usando la aproximación estándar de ~4 caracteres por token.

Dos momentos diferentes gastan tokens, y cuestan de manera muy diferente:

Cuándo

Qué sucede

Coste

En el momento en que el cliente se conecta a DevTwin

Los 10 esquemas de herramientas (nombre, descripción, parámetros) se añaden a cada solicitud en esa sesión -- se llame o no a alguna herramienta. Esto es cierto para cualquier servidor MCP, no es específico de DevTwin.

≈1.400 tokens, en cada turno

Solo cuando se llama realmente a una herramienta

La respuesta JSON de esa única herramienta se añade al contexto, una vez.

~120-200 tokens por llamada (varía según cuántos problemas se encuentren)

Desglose de esquema por herramienta (medido):

Herramienta

Tamaño del esquema

≈ tokens

dev_detect

440 caracteres

~110

dev_health

500 caracteres

~125

dev_drift

470 caracteres

~117

dev_explain_failure

793 caracteres

~198

dev_project_info

523 caracteres

~130

dev_dependencies

507 caracteres

~126

dev_services

507 caracteres

~126

dev_check

771 caracteres

~192

dev_prepare

645 caracteres

~161

dev_precommit

481 caracteres

~120

Total (las 10 herramientas)

5.637 caracteres

≈1.400

La conclusión honesta: para un único diagnóstico puntual en una sesión que de otro modo nunca toca una pregunta de entorno, el Bash sin procesar puede salir más barato en tokens totales -- el impuesto fijo de ~1.400 tokens del esquema a menudo supera el ahorro de reemplazar varios comandos de shell con una llamada. Ver la comparación práctica a continuación para números reales en ambos lados.

El caso de DevTwin se fortalece cuantas más preguntas de entorno surgen en una sesión (el impuesto fijo se paga una vez; cada pregunta posterior cuesta ~150 tokens con DevTwin frente a cientos más con Bash sin procesar cada vez) -- y su ventaja real no es en absoluto el recuento bruto de tokens, es la consistencia, la seguridad y el funcionamiento en clientes MCP que no tienen herramienta Bash. Ver Beneficios y Compensaciones honestas.

Implicación práctica: registra DevTwin por proyecto, no para todo el usuario, para que el impuesto fijo solo se pague en sesiones donde sea realmente útil -- ver Usarlo en otro proyecto.

Compensaciones honestas

DevTwin no es una herramienta de uso diario para un entorno estable -- nadie necesita volver a comprobar "¿está Postgres en ejecución?" en cada función que escribe. Es una herramienta de emergencia: de alto valor en momentos específicos (un clon reciente, una compilación que falla misteriosamente, justo antes de un commit), e inactiva el resto del tiempo. Ese es el patrón de uso previsto, no una deficiencia.

  • El gasto de tokens se paga en cada turno en el momento en que se conecta, se use o no — consulta Coste de tokens para cifras reales medidas.

  • No gana de forma fiable en tokens para una sola consulta puntual; gana en consistencia, seguridad y alcance en clientes sin shell — consulta Beneficios.

  • Si un agente ya tiene acceso completo al shell de un repositorio que controlas por completo y rara vez tiene desviación de entorno, puede que ahí no necesites DevTwin en absoluto.

  • DevTwin se justifica sobre todo en: repositorios compartidos/de incorporación, configuraciones de agentes menos fiables o sin shell, y monorepos multiecosistema donde "qué compruebo siquiera" es la parte difícil.

Con DevTwin vs. sin DevTwin: un ejemplo práctico

Supón que le preguntas a un agente "¿por qué falla npm test?" y la causa real es un desajuste de la versión de Node y que Postgres no está en ejecución.

Sin DevTwin (agente usando Bash puro): tiene que adivinar la secuencia correcta, un comando a la vez:

cat package.json                      # spot "engines": {"node": ">=20"}
node --version                        # v16.20.0 -- mismatch found
grep -i "pg\|postgres" package.json   # spot the Postgres dependency
cat .env                              # risk: may print a real secret into context
lsof -i :5432                         # nothing listening
docker ps                             # check if it's in a container instead

Seis idas y vuelos, una ruta de investigación que el agente tuvo que improvisar, una posibilidad real de que un secreto se filtre en la conversación en el paso 4, y aproximadamente 400-800 tokens de texto de comando más salida (varía según los tamaños de archivo y cuántos contenedores Docker estén ejecutándose).

Con DevTwin, una sola llamada:

dev_health()
{
  "status": "error",
  "summary": "2 issues found: runtime drift, service down",
  "issues": [
    "Node 16.20.0 installed, project requires >=20 (from package.json engines)",
    "Postgres required (found in docker-compose.yml) but not running on 5432"
  ],
  "recommendations": [
    "nvm install 20 && nvm use 20",
    "docker compose up -d postgres"
  ]
}

La misma conclusión, ~150 tokens por la respuesta — más el canon fijo de esquema de ~1.400 tokens ya pagados em ese turno de todos modos (consulta Coste de tokens). Una llamada en lugar de seis, sin posibilidad de filtrar un secreto y exactamente la misma comprobación prerfijada cada vez, en lugar de una investigación improvisada que varía de una sesión a otra.

Preguntas de ejemplo que esto permite

  • "Revisa mi entorno de desarrollo."

  • "¿Por qué falla la compilación de mi proyecto Kotlin?"

  • "¿Es correcta mi versión de Node para este repositorio?"

  • "¿Por qué no se conecta mi aplicación a Postgres?"

  • "¿Se desvía mi entorno de lo que this repostorio espera?"

  • "¿Qué debería ejecutar antes de hacer commit?"

  • "Acabo de clonar este repositorio, ¿qué necesito para ponerlo en marcha?"

Ejemplos por lenguaje

Una fila por ecosistema soportado: una pregunta que realmente harías, qué comprueba DevTwin para responderla y el comando de test/build que reconoce para dev_check.

Ecosistema

Pregunta de ejemplo

Qué se comprueba

Comandos reconocidos

Python

"¿Es correcta mi versión de Python para este repositorio?"

python/python3 vs. .python-version o pyproject.toml [project.requires-python]; uv/pip/poetry/pipenv + lockfile

pytest, ruff check ., mypy .

Node.js

"¿Por qué falla npm test?"

node vs. .nvmrc/.node-version/package.json engines; npm/pnpm/yarn/bun + lockfile

npm test (o pnpm test/yarn test/bun test), <mgr> run

JVM (Java + Kotlin + Android)

"¿Por qué no me compila mi app de Android tras un clon nuevo?"

java/kotlinc versión; versión del wrapper de Gradle vs. instalada; Maven wrapper; en proyectos Android específicamente: ANDROID_HOME/ANDROID_SDK_ROOT, o sdk.dir de local.properties y si esa ruta existe

./gradlew test, ./mvnw test

Go

"¿Es correcta mi versión de Go para este repositorio?"

go vs. la versión requerida en go.mod

go test ./..., go build ./...

Rust

"¿Por qué falla cargo build?"

rustc vs. el canal de rust-toolchain[.toml]

cargo test

.NET

"¿Por qué falla dotnet build?"

presencia y versión del SDK de dotnet

dotnet test

Swift (iOS/macOS)

"¿Por qué falla mi compilación de iOS?"

swift/xcodebuild vs. la versión de herramientas de Package.swift; estado del lockfile de Symfony2 CocoaPods/SPM

swift test (solo proyectos SPM)

Ruby

"¿Por qué falla bundle exec rspec?"

ruby vs. .ruby-version; Bundler + Gemfile.lock

bundle exec rspec, bundle exec rake test

PHP

"¿Por qué arranca mal mi aplicación PHP?"

php vs. require.php de composer.json; Composer + composer.lock

composer test, vendor/bin/phpunit

Genérico (recurso alternativo)

"Este repositorio no está en ningún lenguaje de lostinos: ¿qué me puedes decir?"

Makefile/Taskfile.yml/justfile/Dockerfile/servicios de compose

make test, task test, just test

Arquitectura

Un solo servidor MCP, muchos adaptadores de ecosistema: no un servidor separado por lenguaje.

MCP server -> core (workspace/detector/health/drift/diagnostics) ->
adapters (python/node/jvm/go/rust/dotnet/swift/ruby/php/generic) ->
system inspection (os/process/ports/env/fs/docker) ->
service detection (postgres/redis/generic)

Detalles completos en docs/architecture.md. Cómo añadir un nuevo adaptador de lenguaje: docs/adapters.md.

Ecosistemas soportados

Ecosistema

Detectado a partir de

Runtime comprobado

Gestores de paquetes

Python

pyproject.toml, requirements.txt, uv.lock, poetry.lock, Pipfile, .python-version

python/python3

uv, pip, poetry, pipenv

Node.js

package.json, lockfiles, .nvmrc, .node-version

node

npm, pnpm, yarn, bun

JVM (Java + Kotlin)

pom.xml, build.gradle[.kts], fuentes .java/.kt

java, kotlinc

Gradle (wrapper), Maven (wrapper)

Go

go.mod, go.sum, go.work

go

go modules

Rust

Cargo.toml, rust-toolchain[.toml]

rustc

cargo

.NET

*.csproj/*.fsproj/*.vbproj, *.sln, global.json

dotnet

NuGet

Swift (iOS/macOS)

Package.swift, *.xcodeproj, *.xcworkspace, Podfile

swift, xcodebuild

SPM, CocoaPods

Ruby

Gemfile, *.gemspec, .ruby-version

ruby

Bundler

PHP

composer.json

php

Composer

Genérico (fallback)

Makefile, Taskfile.yml, justfile, Dockerfile, archivos de compose

--

make/task/just/docker

También los proyectos que no coincidan con un adaptador específico siguen obteniendo una salida útil del adaptador genérico: DevTwin nunca se queda vacío para un proyecto no reconocido.

Instalación

uv pip install devtwin-mcp
# or
pip install devtwin-mcp

Para desarrollo local desde un clon de este repositorio, consulta docs/development.md.

Configuración del cliente MCP

SinTaxis de configuración exacta varía según el cliente; consulta la documentación de tu cliente. Genéricasmente, DevTwin es un servidor MCP por stdio que se invoca como:

{
  "mcpServers": {
    "devtwin": {
      "command": "devtwin"
    }
  }
}

Para desarrollo local desde un clon (sin instalar el paquete):

{
  "mcpServers": {
    "devtwin": {
      "command": "uv",
      "args": ["run", "--directory", "/absolute/path/to/devtwin-mcp", "devtwin"]
    }
  }
}

Verifica el descubrimiento de herramientas con el MCP Inspector:

npx @modelcontextprotocol/inspector uv run devtwin

Usarlo en otro proyecto (para otros desarrolladores)

DevTwin es un solo binario: puedes apuntar todos los proyectos que quieraas a la misma instalación, sin necesidad de reinstalarlo para cada proyecto. Dos ámbitos de aplicación:

Ámbito

Carga

Cuándo usarlo

Proyecto

Solo en este repositorio

Opción por defecto: consulta Coste de tokens para ver no por qué

Usuario

Cada proyecto, cada sesión

Cuando recurras a DevTwin en la mayoría de tus repositorios

Ámbito de proyecto: pon un .mcp.json en la raíz del proyecto:

{
  "mcpServers": {
    "devtwin": {
      "command": "/absolute/path/to/devtwin-mcp/.venv/bin/devtwin"
    }
  }
}

o con la CLI de Claude Code:

claude mcp add devtwin /absolute/path/to/devtwin-mcp/.venv/bin/devtwin --scope project

Ámbito de usuario:

claude mcp add devtwin /absolute/path/to/devtwin-mcp/.venv/bin/devtwin --scope user

Después de añadirlo, reinicia el cliente (o reconecta el servidor MCP); luego simplemente haz preguntas normales: vea Pregunta de ejemplo que esto habilita.

Consejo para monorepos: en un repositorio que mezcla plataformas (p. ej., Android, iOS y backend), dirige las preguntas a la subcarpeta específica en lugar de a la raíz del repositorio, p. ej. "Revisa el estado de la app android/". dev_detect, en la raíz de un repositorio mixto, informa de todos los ecosistemas que encuentra; es útil una vez, pero es ruidoso para una comprobación específica.

Referencia de herramientas

Todas las herramientas devuelven {status, summary, data, issues, recommendations}. status es uno de ok, warning, error o unknown.

Herramienta

Clase

Descripción

dev_detect

solo lectura

Detección rápida de proyectos/ecosistemas basada en archivos, con evidencia.

dev_health

solo lectura

Puntuación de salud completa de 0-100 que combina runtime, dependencias, servicios y estado de Git.

dev_drift

solo lectura

Compara las versiones de runtime/herramientas requeridas frente a las realmente instaladas.

dev_explain_failure

solo lectura

Diagnostica un mensaje de error dado en causas raíz clasificadas y respaldadas por evidencia.

dev_project_info

solo lectura

Inspección detallada del proyecto: runtimes, herramientas de compilación, comandos, SO, Git.

dev_dependencies

solo lectura

Estado de dependencias/archivos de bloqueo por ecosistema.

dev_services

solo lectura

Servicios locales requeridos (Postgres, Redis, servicios de compose) y su estado de ejecución.

dev_check

ejecución segura

Ejecuta comandos reconocidos de test/lint (p. ej. pytest, ./gradlew test) con tiempo límite.

dev_prepare

solo planes

Produce un plan de preparación para un repositorio recién clonado; nunca lo ejecuta.

dev_precommit

solo lectura

Resumen de preparación para el commit: estado de Git, salud, archivos que parecen contener secretos.

Modelo de seguridad

  • Sin ejecución arbitraria de comandos. No existe la herramienta execute_shell. dev_check solo ejecuta comandos que el propio DevTwin reconoce a partir de los archivos del proyecto, verificados contra una lista de permitidos, ejecutados con shell=False y un tiempo límite.

  • Sin acciones destructivas, nunca. DevTwin nunca ejecuta git reset --hard, rm -rf, kill -9, docker compose down, eliminación de archivos de bloqueo o mutación de .env.

  • dev_prepare solo planifica. Clasifica cada paso propuesto (read_only/safe/requires_approval/dangerous) y nunca ejecuta nada por sí mismo.

Detalles completos: docs/security.md.

Modelo de privacidad

  • Las variables de entorno se verifican solo en cuanto a su presencia cuando su nombre parece secreto (PASSWORD, TOKEN, SECRET, API_KEY, PRIVATE_KEY, ACCESS_KEY, AUTH, CREDENTIAL, ...) — los valores nunca se devuelven.

  • Los archivos .env se escanean solo para obtener los nombres de las variables.

  • dev_precommit marca archivos en el área de preparación (staging) que parecen secretos sin leer ni informar sobre su contenido.

Arquitectura local-primero

  • Sin componente de servidor, sin cuenta, sin llamadas de red propias más allá de los comandos locales que inspecciona (git, docker, cadenas de herramientas de lenguaje).

  • Todo lo que informa proviene de archivos y procesos que ya están en la máquina donde se ejecuta.

Desarrollo

uv sync --all-extras
uv run pytest
uv run ruff check .
uv run mypy src
uv run devtwin

Consulta docs/development.md para el flujo de trabajo completo.

Contribuciones

Consulta CONTRIBUTING.md. Añadir un nuevo ecosistema de lenguaje es la contribución más común — consulta docs/adapters.md para una plantilla, o src/devtwin/adapters/swift.py, ruby.py y php.py para ejemplos reales y fusionados en los que puedes basar el tuyo.

Hoja de ruta

  • Adaptadores de ecosistema adicionales: Elixir, Dart, Scala, C/C++ (CMake/Bazel/Buck), Nix (consulta docs/adapters.md para saber cómo añadir uno)

  • Detectores de servicios adicionales (MySQL/MariaDB, MongoDB, Kafka, RabbitMQ)

  • Comparación de deriva más rica contra la configuración de CI (p. ej., matrices de runtime de GitHub Actions)

  • Caché local opcional de comprobaciones costosas entre llamadas a herramientas dentro de una sesión

Licencia

Apache-2.0 — consulta LICENSE.

Available Tools

10 tools
dev_checkA

Run recognized project checks (tests/lint) detected from project files, e.g. pytest, ./gradlew test, npm test, cargo test. Only commands DevTwin itself recognized are ever executed (never an arbitrary string), each with a timeout. Pass run to restrict to a subset of the recognized commands (call dev_project_info first to see what's available).

ParametersJSON Schema
NameRequiredDescriptionDefault
runNo
workspaceNo.

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4.2/5.0
Behavior4/5

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 explicitly states that only DevTwin-recognized commands are ever executed (never arbitrary strings) and that each command has a timeout, which is valuable safety information. It doesn't mention side effects or output specifics, but the core behavioral constraints are transparent.

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 three sentences, front-loaded with the core purpose, then safety details, then usage guidance. Every sentence earns its place with specific, actionable information and no filler.

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

Completeness4/5

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

The tool has an output schema, so return values are documented separately. The description covers safety, usage flow, and available options, but omits any mention of the workspace parameter and doesn't clarify behavior when no checks are recognized. Given the tool's complexity and the availability of an output schema, these are minor gaps.

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 0%, so the description must compensate. It explains the 'run' parameter (restrict to a subset of recognized commands) and directs the agent to dev_project_info to see valid values. However, the 'workspace' parameter is completely unaddressed; an agent might not know it represents the working directory. The explanation for 'run' is helpful but not comprehensive for both parameters.

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 runs recognized project checks (tests/lint) with concrete examples (pytest, gradlew test, npm test, cargo test). It uses a specific verb 'Run' and a well-defined resource, distinguishing it from sibling tools like dev_detect or dev_health which have 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 Guidelines4/5

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

The description advises calling dev_project_info first to discover available commands and to use the 'run' parameter to restrict execution to a subset. This provides clear context and a specific prerequisite, though it doesn't explicitly contrast with alternatives or state when not to use this tool.

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

dev_dependenciesC

Dependency state per detected ecosystem: package manager, lockfile presence/consistency signals, dependency counts, and whether packages appear installed locally (e.g. node_modules/, .venv/).

ParametersJSON Schema
NameRequiredDescriptionDefault
workspaceNo.

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

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 alone must disclose behavior. It describes output content but does not state whether the tool is read-only, scans the filesystem, requires network access, or has side effects. It also omits any performance or error implications. The behavior is not transparent.

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, concise sentence with no filler. It front-loads the key concept and lists specifics efficiently. While it could be slightly more structured with an explicit verb, it is economical and easy to parse.

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?

The tool has one undocumented parameter and no annotations. Even though an output schema exists (which may clarify return values), the description omits critical context about the workspace parameter and how ecosystems are detected. It is incomplete for an agent to call correctly without further inference.

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

Parameters1/5

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

The only parameter 'workspace' has zero schema description coverage (0%), and the description does not mention it at all. The agent has no idea what workspace does, how it affects the results, or what values are acceptable. The description fails to compensate for the undocumented schema.

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 reports dependency state per ecosystem, listing specific data points (package manager, lockfile, counts, local install). It is specific about content, though it lacks an explicit action verb like 'get' or 'list'. It does not differentiate from siblings, but the purpose is unambiguous.

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?

There is no guidance on when to use this tool versus alternatives like dev_detect, dev_health, or dev_drift. No mention of context that would trigger this tool, no exclusions, and no mention of alternatives. The agent must infer when to call it.

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

dev_detectB

Detect what kind of project lives at workspace: languages, ecosystems, and the evidence (files) behind each detection. Fast, file-based only.

ParametersJSON Schema
NameRequiredDescriptionDefault
workspaceNo.

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations provided, the description carries the behavioral disclosure burden. It adds two meaningful traits: 'file-based only' (implying read-only, local operation) and 'fast' (performance). However, it lacks depth—no mention of error handling, what happens if the workspace is invalid, or whether it caches results. It provides some transparency but not comprehensive context.

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 two sentences with no fluff. It leads with the core purpose, then appends the behavioral notes. Every word contributes; nothing is redundant or unnecessary. This is an exemplary concise structure.

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 simplicity (one optional parameter, output schema exists), the description covers the essentials: what it does, the parameter, and some behavior. However, it omits usage context—when to choose this over siblings—and lacks deeper behavioral details like edge cases or side effects. It is adequate for basic use but not fully complete without referencing alternatives.

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 schema description coverage is 0%, so the description must compensate. It explains 'workspace' as the location where the project lives, which clarifies the parameter's role. The default '.' is also consistent. While it doesn't detail accepted path formats, for a single optional parameter this is sufficient and adds meaning beyond the schema.

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 'detect' and the resource 'project at workspace', and specifies the outputs: languages, ecosystems, and evidence files. It distinguishes itself from a tautology and conveys a specific capability, though it does not explicitly contrast with sibling tools like dev_project_info.

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?

There is no explicit guidance on when to use this tool versus alternatives. The phrase 'Fast, file-based only' hints at scope and performance but does not state when a developer should pick this over siblings such as dev_health or dev_project_info, nor does it mention exclusion criteria or prerequisites.

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

dev_driftA

Compare expected project state (required runtime/tool versions from project files) against what's actually installed locally. Reports each mismatch as a structured drift item.

ParametersJSON Schema
NameRequiredDescriptionDefault
workspaceNo.

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It uses non-mutating verbs ('compare', 'reports') which strongly imply a read-only operation, but it does not explicitly state that it does not modify anything or disclose any side effects, permissions, or limitations. The lack of explicit transparency is a moderate gap for a tool that could potentially access local installations.

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 two sentences with no unnecessary words. It front-loads the core comparison action and then specifies the output format. Every word contributes to understanding the tool's function and result.

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

Completeness4/5

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

For a tool with a single optional parameter, an output schema, and no annotations, the description provides sufficient context to call it correctly. The purpose and result are clear, and the workspace parameter is implied. It could mention edge cases (e.g., invalid workspace) or what happens when no drift is found, but the output schema likely covers the return structure, so the description is nearly complete.

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 describes 'workspace' with a default but no description, and schema description coverage is 0%. The description's mention of 'project files' and 'installed locally' gives context that the workspace parameter specifies which directory to inspect, but it does not explicitly describe the parameter's purpose or acceptable values. It adds some meaning but does not fully compensate for the missing schema documentation.

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 (compare), resource (expected vs installed project state), and outcome (reports mismatch as structured drift items). It distinguishes itself from sibling tools by specifying the exact comparison of required runtime/tool versions against local installations, which is not ambiguous.

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 the intended use case (detecting version drift) but does not explicitly state when to prefer this tool over alternatives, nor does it mention any exclusions or conditions. With many siblings, explicit routing would be helpful, but the core purpose is clear enough that an agent can infer appropriate usage.

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

dev_explain_failureA

Diagnose a development failure. Give it the error/output text (and optionally the command that produced it); it cross-references project type, runtimes, services, ports, and dependencies to rank likely root causes with evidence -- never asserting certainty beyond the evidence.

ParametersJSON Schema
NameRequiredDescriptionDefault
commandNo
workspaceYes
error_messageYes

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4.6/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool 'cross-references project type, runtimes, services, ports, and dependencies' and that it 'ranks likely root causes with evidence'. It also clarifies a behavioral trait: 'never asserting certainty beyond the evidence'. This is transparent about the tool's inductive reasoning and its limitation. The description doesn't mention any destructive actions or side effects, but the name suggests (and the description confirms) it is purely diagnostic. Given the lack of annotations, this is a good level of 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 two sentences long, with the main purpose and inputs at the front. It packs a lot of information: the input, the cross-referencing behavior, the output (ranked root causes with evidence), and a critical limitation ('never asserting certainty beyond the evidence'). No fluff, every clause earns its place. The structure is clear: first sentence states the action, second expands on internals and output.

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

Completeness4/5

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

Given the tool's complexity (diagnostic reasoning tool with multiple input parameters and an output schema), the description covers the core functionality well: it explains what the tool does, what it takes, and what it returns. The output schema exists (though not provided in the input schema here, but the signal says 'Has output schema: true'), so the description doesn't need to detail return values. The main missing piece is explicit mention of the 'workspace' parameter – it is implied by 'project type, runtimes, services' but not directly stated as an input. Other than that, the description is complete enough for an agent to invoke the tool correctly.

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 schema description coverage is 0%, so the description must compensate for the parameters. The description mentions 'error/output text' and 'command that produced it' and 'workspace' implicitly. It doesn't explicitly name the workspace parameter, but it says 'cross-references project type, runtimes, services, ports, and dependencies' which implies the workspace is the project context. The description adds value by explaining what the parameters are used for (error_message as the primary input, command as optional context). It does not cover all parameter semantics in detail (e.g., format of workspace), but given the high-level nature, it provides enough meaning beyond the schema's bare property names.

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 purpose: diagnose a development failure. It specifies the inputs (error/output text, optionally the command) and the output (ranks likely root causes with evidence). The verb 'diagnose' and the resource 'development failure' are specific, and the description distinguishes it from siblings by focusing on failure analysis rather than detection, health, or project info.

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

Usage Guidelines5/5

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

The description explicitly tells the agent when to use this tool: when you have error/output text and optionally the command that produced it. It implies that the tool is for diagnosing failures, not for general health checks or detection. While it doesn't explicitly name alternatives, the context signals and sibling names (dev_check, dev_detect) suggest it's for failure analysis, and the description gives a clear condition for invocation. This is a strong usage guideline, essentially 'use this when you have error text and want root cause analysis'.

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

dev_healthA

Full environment health check: combines project detection, runtime versions, dependency state, required services, ports, and Git state into a 0-100 health_score with concrete issues and recommendations.

ParametersJSON Schema
NameRequiredDescriptionDefault
workspaceNo.

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It does convey that the tool is non-mutating (a 'check' that produces a score and issues), implying a read-only operation, but it stops short of explicitly stating side-effect-free behavior, performance implications, or what happens if the workspace doesn't exist. It also doesn't describe the structure of the 'issues and recommendations' it returns, though an output schema is present. The overall intent is clear, but some behavioral specifics are left to inference.

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, densely informative sentence that front-loads the core purpose ('Full environment health check') and immediately enumerates the covered areas. Every element earns its place—no filler, no repetition. It is compact while conveying both scope and outcome, making it highly efficient for an agent to parse.

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?

The description provides a good high-level overview of functionality and output, and the presence of an output schema relieves it of fully explaining return values. However, it omits any guidance on the `workspace` parameter (its purpose and valid values), which is necessary for correct invocation. It also doesn't mention prerequisites (e.g., must be inside a project) or how to interpret the health score beyond 'issues and recommendations'. For a tool that combines many aspects, these gaps are moderate, leaving the description slightly incomplete.

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

Parameters2/5

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

There is one parameter, `workspace` (default '.'), and the schema provides zero description coverage (0%). The tool description never mentions this parameter or explains its meaning (e.g., directory path, project root). Although the name is somewhat self-explanatory and there is a sensible default, the description fails to compensate for the missing schema documentation. The agent is left to infer what value to pass, which is a notable gap for a parameter that affects the scope of the health check.

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 a specific verb ('health check') applied to the environment, lists the concrete aspects it combines (project detection, runtime versions, dependency state, services, ports, Git state), and defines the single 0-100 health_score output. It unambiguously distinguishes itself from narrower sibling tools like dev_dependencies or dev_services by being a 'Full environment health check' that aggregates many dimensions.

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 implies a comprehensive, one-stop check by explicitly enumerating what it covers, which signals when to use it (when a holistic overview is needed). However, it does not explicitly exclude alternatives (e.g., 'use dev_dependencies for dependency-only checks') nor call out situations where a narrower tool is preferred. This is clear context without exclusions, so a 4 is appropriate.

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

dev_precommitA

Read-only commit-readiness summary: Git status (dirty/staged/conflicts), project health, and staged files that look like secrets. Never commits, stages, or modifies anything.

ParametersJSON Schema
NameRequiredDescriptionDefault
workspaceNo.

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4.1/5.0
Behavior5/5

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

Since no annotations are provided, the description carries the full burden. It explicitly states 'Read-only' and 'Never commits, stages, or modifies anything.' This fully discloses the tool's side-effect-free nature, which is the critical behavioral trait. The description goes beyond a simple purpose statement by reassuring the agent of safety.

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 two concise sentences, with the core purpose front-loaded. The first sentence states what it does, the second reinforces the read-only guarantee. No fluff or redundant details; every word adds value.

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

Completeness4/5

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

The description covers the tool's purpose, scope, and behavioral safeguards. An output schema exists, so return values are presumed documented elsewhere. The only notable gap is the undocumented 'workspace' parameter, which prevents the description from being fully complete for a drop-in usage understanding.

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

Parameters2/5

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

The schema has one parameter ('workspace') with 0% description coverage, and the tool description does not mention it at all. This leaves the agent to infer that 'workspace' refers to a path, but there is no explicit guidance, default behavior, or allowed values. The description fails to compensate for the missing schema documentation.

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 a specific verb+resource: 'Read-only commit-readiness summary' and details the specific checks (Git status, project health, secrets). It also explicitly states what it never does, which distinguishes it from mutation tools. The purpose is unambiguous and distinct from sibling dev_* tools.

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?

Usage context is implied by the name and description: it is for checking commit readiness, presumably before committing. However, the description does not explicitly state when to use this tool versus its siblings (e.g., dev_health, dev_check) or provide any exclusion criteria. It offers clear context but no explicit comparisons.

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

dev_prepareA

Produce a preparation plan for a (likely newly-cloned) repository: ordered steps to align runtimes, start required services, install dependencies, and run the project's build/test commands. This tool NEVER executes anything -- it only plans, and classifies each step's blast radius (read_only/safe/requires_approval/dangerous) for the caller.

ParametersJSON Schema
NameRequiredDescriptionDefault
workspaceNo.

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4.2/5.0
Behavior5/5

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

With no annotations provided, the description carries full behavioral burden. It explicitly states 'This tool NEVER executes anything -- it only plans' — a critical guarantee that prevents an agent from expecting side effects. It also discloses the output behavior (classifying each step's blast radius). This is thorough and transparent for a planning tool with zero side effects.

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 two sentences, front-loaded with the core purpose and then the key behavioral caveat. Every clause adds value: scope, typical use case, and the critical non-execution guarantee. No filler, no redundancy. It is concise and well-structured for an agent to parse quickly.

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

Completeness4/5

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

Given its simplicity (one optional param, no execution), the description is almost complete. It covers what the plan includes (steps, blast radius classification) and what it never does. The output schema exists, so return details are covered implicitly. Minor omission: it doesn't specify the format or granularity of the plan, but that is not essential for invocation. Overall, an agent can confidently call this tool correctly.

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 has a single optional 'workspace' parameter with 0% description coverage, so the description must compensate. It does not directly explain 'workspace', but the purpose 'for a repository' implies the workspace is the repository path. Since the parameter is simple, optional, and has a sensible default, the lack of explicit parameter explanation is a minor gap. This warrants a middle score.

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 states a specific verb ('Produce a preparation plan'), a specific resource ('repository'), and the scope ('align runtimes, start services, install deps, run build/test'). It also explicitly disclaims execution, which clearly distinguishes it from sibling tools like dev_check or dev_detect that likely run commands. The purpose is unambiguous and differentiated without needing to name siblings.

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 — 'for a (likely newly-cloned) repository' — and clarifies it is for planning, not execution. However, it does not explicitly state when to prefer this over sibling tools (e.g., dev_check for actual validation, dev_services for service management). The context is present but no alternatives or exclusions are given, so guidance is implied rather than explicit.

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

dev_project_infoA

Detailed project inspection: detected ecosystems, runtimes (installed vs required), build tools, test/build commands, environment variables in use, OS info, and Git state. Broader and slower than dev_detect.

ParametersJSON Schema
NameRequiredDescriptionDefault
workspaceNo.

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the scope of inspection and a performance characteristic (slower), but does not explicitly state read-only behavior, required permissions, or potential side effects. The word 'inspection' implies non-mutating, yet it is not stated outright.

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?

Two sentences that are front-loaded with the purpose and a detailed list, followed by a comparative note. Every word earns its place, with no redundancy or filler.

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

Completeness4/5

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

An output schema exists, so return-value details are already covered. The description specifies a rich set of inspected aspects and includes a performance caveat. It does not explain the workspace parameter's role, but that is captured under parameter semantics. Overall, the description gives sufficient context for an agent to decide to call the tool and understand its high-level behavior.

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

Parameters2/5

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

Schema coverage is 0% and the description does not mention the 'workspace' parameter at all. The parameter name is self-explanatory, but the description adds no contextual meaning about what value to provide (e.g., project root path) or how it affects the inspection.

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?

States a specific verb 'inspection' with a specific resource ('project') and enumerates concrete content: ecosystems, runtimes, build tools, commands, environment variables, OS info, Git state. It also differentiates from dev_detect by noting 'broader and slower', making the purpose unmistakable.

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?

Explicitly contrasts with dev_detect ('Broader and slower than dev_detect'), giving a comparative usage cue. It implies choosing this tool when depth is needed over speed, though it does not list explicit when-not-to-use conditions or mention any other sibling tools.

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

dev_servicesA

Local service detection: which services (Postgres, Redis, and compose-defined services) this project appears to need, whether each is currently running/listening, and the evidence behind that conclusion.

ParametersJSON Schema
NameRequiredDescriptionDefault
workspaceNo.

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses that the tool performs local detection, checks whether services are running/listening, and provides evidence, which implies a read-only, non-destructive operation. However, it does not state that it has no side effects, does not start services, or what happens if services are missing. It is more transparent than a bare 'detect services' but still lacks explicit safety details.

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, dense sentence that front-loads the core purpose ('Local service detection') and packs in the three key outputs (needed services, running status, evidence). There is no filler or irrelevant information, making it highly efficient.

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

Completeness4/5

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

The output schema exists (though not detailed here), so return values need not be explained. The description covers the essential scope: which services, whether running, and evidence. It omits explicit clarification of the 'workspace' parameter and any limitations, but for a detection tool with a single optional parameter, it is close to complete.

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

Parameters2/5

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

The schema has one parameter 'workspace' with a default of '.' and zero description coverage (0%). The description does not mention the parameter at all, leaving the agent to infer that it refers to the project directory. The name and default give some hint, but the description adds no semantic value beyond the schema, and the schema itself provides no description.

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 states a specific verb ('detection') and resource ('services'): it identifies which services (Postgres, Redis, compose-defined) the project needs, whether they are running, and provides evidence. This clearly distinguishes it from sibling tools like dev_dependencies (dependencies) or dev_health (health status).

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 gives no explicit guidance on when to use this tool versus alternatives. It does not mention any conditions, alternatives, or exclusions. An agent would have to infer from the title and sibling names that this is about service detection, but there is no explicit routing or 'use this when' statement.

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. 10 tool updatesv0.1.0
    • First observeddev_check
    • First observeddev_dependencies
    • First observeddev_detect
    • First observeddev_drift
    • First observeddev_explain_failure
    • First observeddev_health
    • First observeddev_precommit
    • First observeddev_prepare
    • First observeddev_project_info
    • First observeddev_services

TDQS

A3.8/5.0

Scored across 10 tools

Disambiguation5/5

Each tool targets a distinct aspect of the development environment: detection, health, drift, failure diagnosis, project info, dependencies, services, checks, preparation, and precommit. Even similar tools like dev_detect and dev_project_info are clearly differentiated by scope and speed. There is no ambiguous overlap that would cause an agent to select the wrong tool.

Naming Consistency5/5

All tool names follow the consistent pattern `dev_` + lower_snake_case, using descriptive verbs or nouns (detect, health, drift, explain_failure, etc.). The naming convention is uniform and predictable, with no mixing of camelCase or inconsistent verb styles.

Tool Count5/5

With 10 tools, the server is well-scoped and each tool serves a clear purpose within the domain of development environment analysis and preparation. The count is within the ideal range and avoids both bloat and insufficient coverage.

Completeness5/5

The tool set covers the full lifecycle for a diagnostics/preparation server: detection, health assessment, drift checking, failure explanation, dependency and service checks, test execution, preparation planning, and precommit readiness. No obvious gaps exist for the stated purpose, and the tools work together to provide comprehensive environment insight.

Maintenance

ActivityMaintained
ResponsivenessNo issues

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