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Tribal Knowledge Service

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Python
Hybrid

Tribal - Knowledge Service

Tribal is an MCP (Model Context Protocol) server implementation for error knowledge tracking and retrieval. It provides both REST API and native MCP interfaces for integration with tools like Claude Code and Cline.

Features

  • Store and retrieve error records with full context

  • Vector similarity search using ChromaDB

  • REST API (FastAPI) and native MCP interfaces

  • JWT authentication with API keys

  • Local storage (ChromaDB) and AWS integration

  • Docker-compose deployment

  • CLI client integration

Related MCP server: MemoCall

Overview

Tribal helps Claude remember and learn from programming errors. When you start a Claude Code session, Tribal is automatically available through MCP without additional imports.

Claude will:

  1. Store programming errors and solutions

  2. Search for similar errors when you encounter problems

  3. Build a knowledge base specific to your coding patterns

Packaging and Installing Tribal with uv

Prerequisites

  • Python 3.12+

  • uv package manager (recommended)

Build and Install Steps

Option 1: Direct installation with uv

The simplest approach is to install directly from the current directory:

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

# Install using uv
uv pip install .

Option 2: Development Installation

For development work where you want changes to be immediately reflected:

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

# Install in development mode
uv pip install -e .

Option 3: Build the package first

If you want to build a distributable package:

# 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

Option 4: Using the uv tool install command

You can also use the tool installation approach:

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

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

Verification

After installation, verify that the tool is properly installed:

# Check the installation
which tribal

# Check the version
tribal version

Integration with Claude

After installation, you can integrate with 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

Usage

Available MCP Tools

Tribal provides these MCP tools:

  1. add_error - Create new error record (POST /errors)

  2. get_error - Retrieve error by UUID (GET /errors/{id})

  3. update_error - Modify existing error (PUT /errors/{id})

  4. delete_error - Remove error record (DELETE /errors/{id})

  5. search_errors - Find errors by criteria (GET /errors)

  6. find_similar - Semantic similarity search (GET /errors/similar)

  7. get_token - Obtain JWT token (POST /token)

Example Usage with Claude

When Claude encounters an error:

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

When Claude finds a solution:

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

Commands for Claude

You can ask Claude to:

  • "Look for similar errors in our Tribal knowledge base"

  • "Store this solution to our error database"

  • "Check if we've seen this error before"

Running the Server

Using the tribal command

# Run the server
tribal

# Get help
tribal help

# Show version
tribal version

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

Using Python modules

# Run the Tribal server
python -m mcp_server_tribal.mcp_app

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

Using legacy entry points

# Legacy MCP server
mcp-server

# Legacy FastAPI server
mcp-api

Command-line Options

# 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

The FastAPI server will be available at http://localhost:8000 with API documentation at /docs. The MCP server will be available at http://localhost:5000 for Claude and other MCP-compatible LLMs.

Environment Variables

FastAPI Server

  • PERSIST_DIRECTORY: ChromaDB storage path (default: "./chroma_db")

  • API_KEY: Authentication key (default: "dev-api-key")

  • SECRET_KEY: JWT signing key (default: "insecure-dev-key-change-in-production")

  • REQUIRE_AUTH: Authentication requirement (default: "false")

  • PORT: Server port (default: 8000)

MCP Server

  • MCP_API_URL: FastAPI server URL (default: "http://localhost:8000")

  • MCP_PORT: MCP server port (default: 5000)

  • MCP_HOST: Host to bind to (default: "0.0.0.0")

  • API_KEY: FastAPI access key (default: "dev-api-key")

  • AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_S3_BUCKET: For AWS integration

API Endpoints

  • POST /errors: Create new error record

  • GET /errors/{error_id}: Get error by ID

  • PUT /errors/{error_id}: Update error record

  • DELETE /errors/{error_id}: Delete error

  • GET /errors: Search errors by criteria

  • GET /errors/similar: Find similar errors

  • POST /token: Get authentication token

Using the Client

# 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'"

How It Works

  1. Tribal uses ChromaDB to store error records and solutions

  2. When Claude encounters an error, it sends the error details to Tribal

  3. Tribal vectorizes the error and searches for similar ones

  4. Claude gets back relevant solutions to suggest

  5. New solutions are stored for future reference

Development

Running Tests

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

Linting and Type Checking

ruff check .
mypy .
black .

GitHub Workflow

This project uses GitHub Actions for continuous integration and deployment. The workflow automatically runs tests, linting, and type checking on push to main and pull requests.

Workflow Steps

  1. Test: Runs linting, type checking, and unit tests

    • Uses Python 3.12

    • Installs dependencies with uv

    • Runs ruff, black, mypy, and pytest

  2. Build and Publish: Builds and publishes the package to PyPI

    • Triggered only on push to main branch

    • Uses Python's build system

    • Publishes to PyPI using twine

Testing Locally

You can test the GitHub workflow locally using the provided script:

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

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

This script simulates the GitHub workflow steps on your local machine:

  • Checks Python version (3.12 recommended)

  • Installs dependencies using uv

  • Runs linting with ruff

  • Checks formatting with black

  • Runs type checking with mypy

  • Runs tests with pytest

  • Builds the package

Note: The script skips the publishing step for local testing.

Project Structure

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

Versioning

Tribal follows Semantic Versioning. See VERSIONING.md for complete details about:

  • Version numbering (MAJOR.MINOR.PATCH)

  • Schema versioning for database compatibility

  • Branch naming conventions

  • Release and hotfix procedures

Check the version with:

# Display version information
tribal version

Managing Dependencies

# 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

Deployment

Docker Deployment

# 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 for Desktop Integration

Option 1: Let Claude for Desktop Launch the Server

  1. Open ~/Library/Application Support/Claude/claude_desktop_config.json

  2. Add the MCP server configuration (assumes Tribal tool is already installed):

    {
  "mcpServers": [
    {
      "name": "tribal",
      "launchCommand": "tribal"
    }
  ]
}

  3. Restart Claude for Desktop

Option 2: Connect to Running Docker Container

  1. Start the container:

    cd /path/to/tribal
docker-start

  2. Configure Claude for Desktop:

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

Claude Code CLI Integration

# 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

Troubleshooting

  1. Verify Tribal installation: which tribal

  2. Check configuration: claude mcp list

  3. Test server status: tribal status

  4. Look for error messages in the Claude output

  5. Check the database directory exists and has proper permissions

Cloud Deployment

The project includes placeholder implementations for AWS services:

  • S3Storage: For storing error records in Amazon S3

  • DynamoDBStorage: For using DynamoDB as the database

License

MIT License

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
NameRequiredDescriptionDefault
error_idYes

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
NameRequiredDescriptionDefault
queryYes
max_resultsNo

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
NameRequiredDescriptionDefault

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
NameRequiredDescriptionDefault
error_idYes

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
NameRequiredDescriptionDefault
error_typeNo
languageNo
frameworkNo
error_messageNo
code_snippetNo
task_descriptionNo
max_resultsNo

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
NameRequiredDescriptionDefault
error_typeYes
error_messageYes
languageYes
frameworkNo
code_snippetNo
task_descriptionNo
solution_descriptionNo
solution_code_fixNo
solution_explanationNo
solution_referencesNo

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.

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TDQS

B3.4/5.0
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

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