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Tribal Knowledge Service

Name: Tribal Knowledge Service
Author: agentience

by agentience

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

Tribal - Servicio de Conocimiento

Tribal es una implementación de servidor MCP (Protocolo de Contexto de Modelo) para el seguimiento y la recuperación de conocimiento de errores. Ofrece API REST e interfaces MCP nativas para la integración con herramientas como Claude Code y Cline.

Características

Almacenar y recuperar registros de errores con contexto completo
Búsqueda de similitud de vectores usando ChromaDB
API REST (FastAPI) e interfaces MCP nativas
Autenticación JWT con claves API
Almacenamiento local (ChromaDB) e integración con AWS
Implementación de Docker-compose
Integración del cliente CLI

Related MCP server: MemoCall

Descripción general

Tribal ayuda a Claude a recordar y aprender de los errores de programación. Al iniciar una sesión de Claude Code, Tribal está disponible automáticamente a través de MCP sin necesidad de importaciones adicionales.

Claude querrá:

Errores de programación de la tienda y soluciones
Busque errores similares cuando encuentre problemas
Cree una base de conocimientos específica para sus patrones de codificación

Embalaje e instalación de Tribal con uv

Prerrequisitos

Python 3.12+
Gestor de paquetes uv (recomendado)

Pasos de construcción e instalación

Opción 1: Instalación directa con uv

El enfoque más simple es instalar directamente desde el directorio actual:

# From the project root directory
cd /path/to/tribal

# Install using uv
uv pip install .

Opción 2: Instalación de desarrollo

Para trabajos de desarrollo donde desea que los cambios se reflejen inmediatamente:

# From the project root directory
cd /path/to/tribal

# Install in development mode
uv pip install -e .

Opción 3: Construya el paquete primero

Si desea crear un paquete distribuible:

# Make sure you're in the project root directory
cd /path/to/tribal

# Install the build package if needed
uv pip install build

# Build the package
python -m build

# This creates distribution files in the dist/ directory
# Now install the wheel file
uv pip install dist/tribal-0.1.0-py3-none-any.whl

Opción 4: Usar el comando `uv tool install`

También puede utilizar el método de instalación de herramientas:

# Install as a global tool
cd /path/to/tribal
uv tool install .

# Or install in development mode
uv tool install -e .

Verificación

Después de la instalación, verifique que la herramienta esté correctamente instalada:

# Check the installation
which tribal

# Check the version
tribal version

Integración con Claude

Después de la instalación, puedes integrarlo con Claude:

# Add Tribal to Claude Code
claude mcp add tribal --launch "tribal"

# Verify the configuration
claude mcp list

# For Docker container
claude mcp add tribal http://localhost:5000

Uso

Herramientas MCP disponibles

Tribal proporciona estas herramientas MCP:

add_error - Crea un nuevo registro de error (POST/errors)
get_error - Recuperar error por UUID (GET /errors/{id})
update_error - Modificar el error existente (PUT /errors/{id})
delete_error - Eliminar registro de error (DELETE /errors/{id})
search_errors - Buscar errores por criterios (GET /errors)
find_similar - Búsqueda de similitud semántica (GET /errors/similar)
get_token - Obtener el token JWT (POST /token)

Ejemplo de uso con Claude

Cuando Claude encuentra un error:

I'll track this error and look for similar problems in our knowledge base.

Cuando Claude encuentra una solución:

I've found a solution! I'll store this in our knowledge base for next time.

Órdenes para Claude

Puedes pedirle a Claude que:

Busque errores similares en nuestra base de conocimientos tribales.
"Guarde esta solución en nuestra base de datos de errores"
"Comprobar si hemos visto este error antes"

Ejecución del servidor

Usando el comando tribal

# Run the server
tribal

# Get help
tribal help

# Show version
tribal version

# Run with options
tribal server --port 5000 --auto-port

Uso de módulos de Python

# Run the Tribal server
python -m mcp_server_tribal.mcp_app

# Run the FastAPI backend server
python -m mcp_server_tribal.app

Uso de puntos de entrada heredados

# Legacy MCP server
mcp-server

# Legacy FastAPI server
mcp-api

Opciones de la línea de comandos

# Development mode with auto-reload
mcp-api --reload
mcp-server --reload

# Custom port
mcp-api --port 8080
mcp-server --port 5000

# Auto port selection
mcp-api --auto-port
mcp-server --auto-port

El servidor FastAPI estará disponible en http://localhost:8000 con la documentación de la API en /docs. El servidor MCP estará disponible en http://localhost:5000 para Claude y otros LLM compatibles con MCP.

Variables de entorno

Servidor FastAPI

PERSIST_DIRECTORY : Ruta de almacenamiento de ChromaDB (predeterminado: "./chroma_db")
API_KEY : Clave de autenticación (predeterminada: "dev-api-key")
SECRET_KEY : Clave de firma JWT (valor predeterminado: "insecure-dev-key-change-in-production")
REQUIRE_AUTH : Requisito de autenticación (predeterminado: "falso")
PORT : Puerto del servidor (predeterminado: 8000)

Servidor MCP

MCP_API_URL : URL del servidor FastAPI (predeterminado: " http://localhost:8000 ")
MCP_PORT : puerto del servidor MCP (predeterminado: 5000)
MCP_HOST : Host al que vincular (predeterminado: "0.0.0.0")
API_KEY : Clave de acceso de FastAPI (predeterminada: "dev-api-key")
AWS_ACCESS_KEY_ID , AWS_SECRET_ACCESS_KEY , AWS_S3_BUCKET : para la integración con AWS

Puntos finales de API

POST /errors : Crear nuevo registro de errores
GET /errors/{error_id} : Obtener el error por ID
PUT /errors/{error_id} : Actualizar registro de errores
DELETE /errors/{error_id} : Eliminar error
GET /errors : Buscar errores por criterios
GET /errors/similar : Encuentra errores similares
POST /token : Obtener el token de autenticación

Uso del cliente

# Add a new error record
mcp-client --action add --error-type ImportError --language python --error-message "No module named 'requests'" --solution-description "Install requests" --solution-explanation "You need to install the requests package"

# Get an error by ID
mcp-client --action get --id <error-id>

# Search for errors
mcp-client --action search --error-type ImportError --language python

# Find similar errors
mcp-client --action similar --query "ModuleNotFoundError: No module named 'pandas'"

Cómo funciona

Tribal utiliza ChromaDB para almacenar registros de errores y soluciones
Cuando Claude encuentra un error, envía los detalles del error a Tribal.
Tribal vectoriza el error y busca otros similares
Claude recupera soluciones relevantes para sugerir
Las nuevas soluciones se almacenan para futuras referencias.

Desarrollo

Ejecución de pruebas

pytest
pytest tests/path_to_test.py::test_name  # For specific tests

Comprobación de tipo y desbastado

ruff check .
mypy .
black .

Flujo de trabajo de GitHub

Este proyecto utiliza GitHub Actions para la integración y el despliegue continuos. El flujo de trabajo ejecuta automáticamente pruebas, análisis de errores y verificación de tipos en las solicitudes de inserción y extracción.

Pasos del flujo de trabajo

Prueba : ejecuta pelusa, verificación de tipos y pruebas unitarias
- Utiliza Python 3.12
- Instala dependencias con uv
- Ejecuta ruff, black, mypy y pytest
Construir y publicar : construye y publica el paquete en PyPI
- Se activa solo al enviar a la rama principal
- Utiliza el sistema de compilación de Python
- Publica en PyPI usando twine

Pruebas locales

Puede probar el flujo de trabajo de GitHub localmente utilizando el script proporcionado:

# Make the script executable
chmod +x scripts/test-workflow.sh

# Run the workflow locally
./scripts/test-workflow.sh

Este script simula los pasos del flujo de trabajo de GitHub en su máquina local:

Comprueba la versión de Python (se recomienda 3.12)
Instala dependencias usando uv
Se corre con pelusa
Comprueba el formato con negro
Ejecuta la verificación de tipos con mypy
Ejecuta pruebas con pytest
Construye el paquete

Nota: el script omite el paso de publicación para las pruebas locales.

Estructura del proyecto

tribal/
├── src/
│   ├── mcp_server_tribal/      # Core package
│   │   ├── api/                # FastAPI endpoints
│   │   ├── cli/                # Command-line interface
│   │   ├── models/             # Pydantic models
│   │   ├── services/           # Service layer
│   │   │   ├── aws/            # AWS integrations
│   │   │   └── chroma_storage.py # ChromaDB implementation
│   │   └── utils/              # Utility functions
│   └── examples/               # Example usage code
├── tests/                      # pytest test suite
├── docker-compose.yml          # Docker production setup
├── pyproject.toml              # Project configuration
├── VERSIONING.md               # Versioning strategy documentation
├── CHANGELOG.md                # Version history
├── .bumpversion.cfg            # Version bumping configuration
└── README.md                   # Project documentation

Control de versiones

Tribal sigue el control de versiones semántico . Consulte VERSIONING.md para obtener más información sobre:

Numeración de versiones (MAYOR.MINOR.PATCH)
Control de versiones del esquema para compatibilidad con bases de datos
Convenciones de nombres de ramas
Procedimientos de lanzamiento y revisión

Compruebe la versión con:

# Display version information
tribal version

Gestión de dependencias

# Add a dependency
uv pip add <package-name>

# Add a development dependency
uv pip add <package-name>

# Update dependencies
uv pip sync requirements.txt requirements-dev.txt

Despliegue

Implementación de Docker

# Build and start containers
docker-compose up -d --build

# View logs
docker-compose logs -f

# Stop containers
docker-compose down

# With custom environment variables
API_PORT=8080 MCP_PORT=5000 REQUIRE_AUTH=true API_KEY=your-secret-key docker-start

Claude para integración de escritorio

Opción 1: Dejar que Claude for Desktop inicie el servidor

Abra ~/Library/Application Support/Claude/claude_desktop_config.json
Agregue la configuración del servidor MCP (supone que la herramienta Tribal ya está instalada):
```
{
  "mcpServers": [
    {
      "name": "tribal",
      "launchCommand": "tribal"
    }
  ]
}
```
Reiniciar Claude para escritorio

Opción 2: Conectarse al contenedor Docker en ejecución

Iniciar el contenedor:
```
cd /path/to/tribal
docker-start
```

Configurar Claude para escritorio:

{
  "mcpServers": [
    {
      "name": "tribal",
      "url": "http://localhost:5000"
    }
  ]
}

Integración CLI de Claude Code

# For Docker container
claude mcp add tribal http://localhost:5000

# For directly launched server
claude mcp add tribal --launch "tribal"

# Test the connection
claude mcp list
claude mcp test tribal

Solución de problemas

Verificar la instalación tribal: which tribal
Comprobar configuración: claude mcp list
Estado del servidor de prueba: tribal status
Busque mensajes de error en la salida de Claude
Verifique que el directorio de la base de datos exista y tenga los permisos adecuados

Implementación en la nube

El proyecto incluye implementaciones de marcador de posición para los servicios de AWS:

S3Storage : para almacenar registros de errores en Amazon S3
DynamoDBStorage : para utilizar DynamoDB como base de datos

Licencia

Licencia MIT

Available Tools

6 tools

delete_errorB

Delete an error record.

Args:
    error_id: UUID of the error record

Returns:
    True if deleted, False if not found

ParametersJSON Schema

Name	Required	Description	Default
`error_id`	Yes

TDQS

B3.1/5.0

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 the action ('Delete') and return values, but lacks critical details: it doesn't specify if deletion is permanent/reversible, mention authentication or permission requirements, indicate side effects (e.g., cascading deletions), or note rate limits. For a destructive tool, 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 well-structured and front-loaded with the core purpose. It uses three concise sentences (purpose, args, returns) with zero wasted words. Each sentence earns its place by adding distinct value: action, parameter meaning, and outcome.

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 tool's complexity (destructive operation with no annotations and no output schema), the description is incomplete. It lacks context on safety (e.g., confirmation prompts, irreversible effects), error handling beyond 'not found', and integration with sibling tools. The return value explanation is minimal, but without an output schema, more detail on responses would be helpful.

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 adds essential semantics: it clarifies that 'error_id' is a 'UUID of the error record', which is not in the schema (which only lists 'Error Id' as the title). This provides meaningful context beyond the schema, though it doesn't elaborate on UUID format or sourcing.

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 ('Delete') and resource ('an error record'), making the purpose unambiguous. It distinguishes itself from siblings like 'get_error_by_id' (read) and 'track_error' (create). However, it doesn't explicitly differentiate from potential destructive siblings beyond the verb, so it's not a perfect 5.

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 'search_errors' or 'find_similar_errors'. It doesn't mention prerequisites (e.g., needing the error_id from another operation) or warn against misuse. The only implied usage is when you have an error_id to delete, but this is basic and insufficient for a destructive operation.

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

find_similar_errorsC

Find errors similar to the given query.

Args:
    query: Text to search for in the knowledge base
    max_results: Maximum number of results to return

Returns:
    List of similar error records

ParametersJSON Schema

Name	Required	Description	Default
`query`	Yes
`max_results`	No

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 full burden. It mentions returning a list of similar error records but doesn't disclose behavioral traits such as how similarity is determined, whether it's read-only, performance characteristics, or error handling. This is a significant gap for a search tool with zero 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded with the core purpose. The Args and Returns sections are structured clearly, though the 'Returns' part could be more specific. Every sentence adds value, but it could be slightly more concise by integrating the sections more fluidly.

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 a similarity search tool with no annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't explain what 'similar' means, the return format beyond 'List of similar error records', or how results are ordered, leaving gaps for the agent to infer behavior.

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 schema provides no parameter descriptions. The description adds basic semantics for 'query' ('Text to search for in the knowledge base') and 'max_results' ('Maximum number of results to return'), which compensates partially but lacks details like format constraints or default behavior beyond the schema's default value.

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 'Find errors similar to the given query' which provides a clear verb ('Find') and resource ('errors'), but it's vague about what constitutes 'similar' and doesn't distinguish from sibling tools like 'search_errors'. It's not tautological but lacks specificity about the similarity mechanism.

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 like 'search_errors' or 'get_error_by_id'. The description implies usage for finding similar errors but doesn't specify contexts, prerequisites, or exclusions, leaving the agent to guess 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.

get_api_statusC

Check the API status.

Returns:
    API status information

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

C2.7/5.0

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. It states the tool 'Check[s] the API status' and 'Returns API status information', but doesn't disclose behavioral traits like whether it's read-only, requires authentication, has rate limits, or what specific information is included in the return. For a tool with zero annotation coverage, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two sentences, but it's not front-loaded effectively. The first sentence states the purpose, but the second ('Returns: API status information') is redundant and doesn't add value beyond what's implied. It could be more structured to emphasize key details.

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. It doesn't explain what 'API status information' includes, how it's formatted, or any behavioral context. For a tool that might be critical for monitoring, this leaves too many gaps for effective agent use.

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 tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add parameter semantics, but this is appropriate given the lack of parameters, warranting a baseline score of 4.

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 tool 'Check[s] the API status', which is a clear verb+resource combination. However, it doesn't differentiate this from sibling tools like 'track_error' or 'get_error_by_id' that might also provide status-related information, nor does it specify what aspects of API status are checked (health, uptime, version, etc.).

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. The description doesn't mention prerequisites, timing, or how it differs from sibling tools like 'track_error' or 'search_errors' that might overlap in monitoring contexts. This leaves the agent with no usage context.

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

get_error_by_idA

Get an error record by its ID.

Args:
    error_id: UUID of the error record

Returns:
    The error record or None if not found

ParametersJSON Schema

Name	Required	Description	Default
`error_id`	Yes

TDQS

A3.6/5.0

Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves an error record or returns None if not found, which clarifies the read-only nature and potential outcomes. However, it lacks details on permissions, rate limits, or error handling beyond the basic return behavior, leaving gaps 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 front-loaded with the core purpose, followed by structured sections for Args and Returns, making it highly efficient and easy to parse. Every sentence adds value without redundancy, adhering to best practices for conciseness.

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 low complexity (one parameter, no output schema, no annotations), the description is adequate but has clear gaps. It covers the basic operation and parameter semantics but lacks details on behavioral aspects like authentication or error scenarios. Without annotations or output schema, more context would improve completeness.

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 adds meaningful context by specifying that 'error_id' is a UUID, which clarifies the parameter's format beyond the schema's generic string type. Since there's only one parameter, this is sufficient to elevate the score above the baseline.

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 ('Get') and resource ('an error record by its ID'), making it immediately understandable. It distinguishes this from siblings like 'search_errors' or 'find_similar_errors' by focusing on direct ID-based retrieval rather than search operations. However, it doesn't explicitly contrast with 'delete_error' or 'track_error', keeping it from a perfect score.

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 when you have a specific error ID, as opposed to searching or finding similar errors, but it doesn't provide explicit guidance on when to use this tool versus alternatives like 'delete_error' or 'track_error'. There's no mention of prerequisites or exclusions, leaving some ambiguity in context.

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

search_errorsC

Search for errors in the knowledge base.

Args:
    error_type: Type of error to filter by
    language: Programming language to filter by
    framework: Framework to filter by
    error_message: Error message to search for
    code_snippet: Code snippet to search for
    task_description: Task description to search for
    max_results: Maximum number of results to return

Returns:
    List of matching error records

ParametersJSON Schema

Name	Required	Description	Default
`error_type`	No
`language`	No
`framework`	No
`error_message`	No
`code_snippet`	No
`task_description`	No
`max_results`	No

TDQS

C2.9/5.0

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 searches and returns a list, but lacks critical details: whether it's read-only, how results are ordered, if there's pagination, error handling, or performance characteristics. For a search tool with 7 parameters, 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections ('Args:', 'Returns:') and uses bullet points for parameters, making it easy to scan. It's appropriately sized for a tool with 7 parameters, though the parameter explanations are minimal. There's no wasted text, but it could be more front-loaded with key usage information.

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 (7 parameters, no annotations, no output schema), the description is incomplete. It covers the basic purpose and parameters but lacks behavioral details, usage guidelines, and output specifics. For a search tool in a knowledge base context, this leaves the agent with insufficient information to use it effectively without trial and error.

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 description lists all 7 parameters with brief labels, but schema description coverage is 0%, so the schema provides no additional documentation. The description adds basic semantic context (e.g., 'error_type: Type of error to filter by'), which helps interpret parameters beyond their names. However, it doesn't explain formats, constraints, or interactions between parameters, leaving room for ambiguity.

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: 'Search for errors in the knowledge base.' This specifies the verb ('search') and resource ('errors in the knowledge base'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'find_similar_errors' or 'get_error_by_id', which prevents a perfect score.

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 sibling tools like 'find_similar_errors' or 'get_error_by_id', nor does it specify contexts where this search tool is preferred. The agent must infer usage from the tool name alone, which is insufficient for optimal selection.

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

track_errorA

Track an error and its solution in the knowledge base.

Args:
    error_type: Type of error (e.g., ImportError, TypeError)
    error_message: The error message
    language: Programming language (e.g., python, javascript)
    framework: Framework used (e.g., fastapi, react)
    code_snippet: The code that caused the error
    task_description: What the user was trying to accomplish
    solution_description: Brief description of the solution
    solution_code_fix: Code that fixes the error
    solution_explanation: Detailed explanation of why the solution works
    solution_references: List of reference links

Returns:
    The created error record

ParametersJSON Schema

Name	Required	Description	Default
`error_type`	Yes
`error_message`	Yes
`language`	Yes
`framework`	No
`code_snippet`	No
`task_description`	No
`solution_description`	No
`solution_code_fix`	No
`solution_explanation`	No
`solution_references`	No

TDQS

A3.5/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool creates a record ('track an error'), implying a write operation, but doesn't mention permissions needed, whether the operation is idempotent, rate limits, or what happens on failure. The return statement is minimal ('The created error record') without format details.

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 well-structured with clear sections (purpose, args, returns) and uses bullet-like formatting for parameters. Every sentence earns its place by explaining functionality or parameters. It could be slightly more concise in the parameter explanations but remains efficient overall.

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?

For a 10-parameter creation tool with no annotations and no output schema, the description provides good parameter documentation but lacks behavioral context. It explains what data to provide but not how the tool behaves operationally (e.g., error handling, authentication). The return value is mentioned but not described in detail.

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

Parameters5/5

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

With 0% schema description coverage and 10 parameters, the description provides comprehensive parameter semantics beyond the schema. It clearly explains each parameter's purpose with examples (e.g., 'error_type: Type of error (e.g., ImportError, TypeError)'), adding significant value that compensates for the schema's lack of descriptions.

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 with a specific verb ('track') and resource ('error and its solution in the knowledge base'). It distinguishes from sibling tools like 'delete_error', 'find_similar_errors', and 'search_errors' by focusing on creation rather than deletion, retrieval, or search 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 like 'search_errors' or 'find_similar_errors'. It mentions no prerequisites, exclusions, or specific contexts for usage, leaving the agent to infer when this creation tool is appropriate versus other operations.

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.

6 tool updatesAug 18, 2025
- First observeddelete_error
- First observedfind_similar_errors
- First observedget_api_status
- First observedget_error_by_id
- First observedsearch_errors
- First observedtrack_error

Deploy Server

Context All Your Agents Can ShareAgents share what they learn, hand off work, and hold it when you switch models. Start with 5 GB free.fulcradynamics.comAd

TDQS

B3.4/5.0

Scored 2026-04-18 10:45 across 6 tools

Disambiguation4/5

Most tools have distinct purposes: track_error creates records, get_error_by_id retrieves single records, delete_error removes them, search_errors filters by multiple criteria, and find_similar_errors performs semantic similarity searches. However, search_errors and find_similar_errors could potentially be confused as both search for errors, though their approaches differ (filtering vs. similarity).

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with snake_case: delete_error, find_similar_errors, get_api_status, get_error_by_id, search_errors, and track_error. The naming is predictable and readable throughout the set.

Tool Count5/5

With 6 tools, this is well-scoped for a knowledge base service focused on error tracking. Each tool serves a clear purpose (CRUD operations, searching, and status checks), and none feel redundant or missing given the domain.

Completeness4/5

The toolset covers core CRUD operations (create via track_error, read via get_error_by_id, delete via delete_error) and searching (search_errors, find_similar_errors), with get_api_status for monitoring. A minor gap is the lack of an update tool to modify existing error records, which agents might need to work around by deleting and recreating.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

Reddit Discussion

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-

Tribal - Servicio de Conocimiento

Características

Descripción general

Embalaje e instalación de Tribal con uv

Prerrequisitos

Pasos de construcción e instalación

Opción 1: Instalación directa con uv

Opción 2: Instalación de desarrollo

Opción 3: Construya el paquete primero

Opción 4: Usar el comando uv tool install

Verificación

Integración con Claude

Uso

Herramientas MCP disponibles

Ejemplo de uso con Claude

Órdenes para Claude

Ejecución del servidor

Usando el comando tribal

Uso de módulos de Python

Uso de puntos de entrada heredados

Opciones de la línea de comandos

Variables de entorno

Servidor FastAPI

Servidor MCP

Puntos finales de API

Uso del cliente

Cómo funciona

Desarrollo

Ejecución de pruebas

Comprobación de tipo y desbastado

Flujo de trabajo de GitHub

Pasos del flujo de trabajo

Pruebas locales

Estructura del proyecto

Control de versiones

Gestión de dependencias

Despliegue

Implementación de Docker

Claude para integración de escritorio

Opción 1: Dejar que Claude for Desktop inicie el servidor

Opción 2: Conectarse al contenedor Docker en ejecución

Integración CLI de Claude Code

Solución de problemas

Implementación en la nube

Licencia

Available Tools

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Appeared in Searches

Opción 4: Usar el comando `uv tool install`