Provides tools to fetch the Astronomy Picture of the Day (APOD) from NASA's API, including getting today's photo, photos by date, and random photos from the archive.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Factory get today's space photo"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An extensible Model Context Protocol (MCP) server framework with a service plugin architecture. Built with FastMCP and Python 3.13. Ships with a NASA APOD (Astronomy Picture of the Day) service as a reference implementation.
Add new API services by implementing a plugin -- your tools appear automatically in every MCP client. Fork the repo to build your own production MCP server from a solid, SOLID foundation.
The included APOD service demonstrates the plugin pattern with these features:
Get Today's Photo — Fetch the current Astronomy Picture of the Day with title, explanation, and media link.
Get Photo by Date — Retrieve any APOD entry from the archive (1995-06-16 to today) by providing a date.
Get Random Photo — Discover a random APOD from NASA's 30+ year archive.
Famous Space Dates Resource — A curated list of iconic space exploration dates to explore in the APOD archive.
Related MCP server: NASA MCP Server
Tool | Description |
| Returns today's APOD with title, explanation, media type, and URL |
| Returns the APOD for a specific date ( |
| Returns a random APOD from the full archive |
URI | Description |
| Curated list of famous space exploration dates |
uv (recommended package manager)
git clone <your-repo-url>
cd mcp-factory
uv syncuv run main.pyThe server communicates over stdio transport, which is the standard for MCP client integrations.
Add the following to your Cursor MCP settings (.cursor/mcp.json):
{
"mcpServers": {
"mcp-factory": {
"command": "uv",
"args": [
"run",
"--directory",
"/absolute/path/to/mcp-factory",
"main.py"
]
}
}
}Replace /absolute/path/to/mcp-factory with the actual path to this project on your machine.
Add the following to your Claude Desktop config (claude_desktop_config.json):
{
"mcpServers": {
"mcp-factory": {
"command": "uv",
"args": [
"run",
"--directory",
"/absolute/path/to/mcp-factory",
"main.py"
]
}
}
}The bundled APOD service uses NASA's DEMO_KEY by default, which has limited rate limits (30 requests/hour, 50 requests/day per IP). For heavier usage, request a free API key and set the NASA_API_KEY environment variable:
export NASA_API_KEY="your-key-here"mcp-factory/
├── main.py # Thin entry point — starts the MCP server
├── mcp_factory/ # Core package
│ ├── __init__.py
│ ├── config.py # Global config (server name)
│ ├── server.py # Builds FastMCP via ServiceRegistry
│ └── services/ # Service plugin architecture
│ ├── __init__.py
│ ├── base.py # ABCs: BaseAPIClient, BaseFormatter, ServicePlugin
│ ├── registry.py # ServiceRegistry factory
│ └── apod/ # APOD service plugin
│ ├── __init__.py # ApodService (registers tools + resources)
│ ├── config.py # APOD-specific constants and API key
│ ├── client.py # ApodClient (extends BaseAPIClient)
│ ├── formatter.py # ApodFormatter (extends BaseFormatter)
│ └── validation.py # APOD date validation
├── tests/ # Unit + E2E tests (pytest)
│ ├── test_base.py # ABC contract tests
│ ├── test_registry.py # ServiceRegistry factory tests
│ ├── test_apod_config.py # APOD config constants
│ ├── test_apod_client.py # ApodClient with mocked HTTP
│ ├── test_apod_formatter.py # ApodFormatter output
│ ├── test_apod_validation.py # Date validation
│ └── test_e2e.py # Full server bootstrap + tool execution
├── templates/ # Copy-paste service boilerplate
│ ├── README.md # Template usage instructions
│ └── service_template/ # Complete service plugin skeleton
├── docs/ # Guides, architecture, and workshop
│ ├── ARCHITECTURE.md # System design and SOLID mapping
│ ├── ADDING-A-SERVICE.md # Step-by-step new service guide
│ ├── ADDING-TOOLS.md # Tool and resource creation guide
│ ├── FORKING-GUIDE.md # Using this repo as a template
│ ├── DEVELOPMENT-WORKFLOW.md # Git, testing, and review workflow
│ ├── LETS-BUILD-AN-MCP-SERVER.md # Original step-by-step tutorial
│ └── index.html # Slide deck presentation
├── CLAUDE.md # AI agent context (Claude Code, etc.)
├── pyproject.toml # Project metadata and dependencies
├── uv.lock # Locked dependency versions
├── .python-version # Python version (3.13)
├── .gitignore # Git ignore rules
└── README.md # This fileThis server uses a service plugin architecture. Each API is a self-contained plugin under mcp_factory/services/. To add a new API:
Create a new directory: mcp_factory/services/your_api/
Implement a client extending BaseAPIClient (handles HTTP)
Implement a formatter extending BaseFormatter (handles Markdown output)
Create a service class with a register(mcp) method that registers your tools
Add it to the registry in mcp_factory/server.py:
from mcp_factory.services.your_api import YourApiService
registry.add(YourApiService())Your new tools appear automatically -- no other files need to change.
Guide | Description |
System design, plugin lifecycle, data flow, SOLID principles, module map | |
Step-by-step guide to adding a new API service plugin | |
How to add tools and resources to an existing service | |
Using this repo as a template for a new MCP server | |
Git workflow, testing strategy, code review checklist | |
Original step-by-step MCP server tutorial |
Resource | Description |
Root-level context file for Claude Code and agentic frameworks | |
| Always-on architecture context for Cursor AI |
| Step-by-step instructions triggered when extending services |
| Tool routing rules for bundled APOD service |
Copy-paste-ready service skeleton at templates/service_template/. See templates/README.md for usage.
mcp[cli] — Model Context Protocol SDK with CLI support
httpx — Async HTTP client for external API calls
This project is for educational and personal use. The bundled APOD service uses data from NASA's APOD API.
Get a random Astronomy Picture of the Day from NASA's archives.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden for behavioral disclosure. It only states the tool fetches a random photo from NASA's archives, omitting details on rate limits, error handling, or side effects. This is minimal disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no extraneous words. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters and an existing output schema (presumably documenting return values), the description is largely complete. It identifies the source and randomness, though it lacks details on caching or the selection algorithm. Still sufficient for a simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so schema coverage is 100%. The description adds no parameter info, but none is needed. Baseline 4 applies as the description suffices for a parameterless tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a random Astronomy Picture of the Day from NASA's archives, using a specific verb and resource. It naturally distinguishes from siblings like get_space_photo_by_date and get_todays_space_photo.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
While the description implies use for random selection, it provides no explicit guidance on when to use this over siblings, nor any conditions or exclusions. Usage context is implied but not clarified.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the Astronomy Picture of the Day for a specific date (YYYY-MM-DD).
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 states the tool 'gets' data, implying a read-only operation, but does not mention side effects, authentication, rate limits, or error conditions. Given the existence of an output schema, return structure is not needed in description, but other behavioral details are missing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, well-formed sentence with no extraneous words. The description is concise and front-loaded with the key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple retrieval tool with one parameter and an output schema, the description covers the essential purpose and a critical formatting detail. Missing elements like error handling or availability restrictions are minor gaps given the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has no description for the date parameter (0% coverage). The description compensates by specifying the required format 'YYYY-MM-DD', adding useful semantic beyond the plain string type. However, it does not provide example values, valid ranges, or constraints (e.g., no future dates).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and the resource ('Astronomy Picture of the Day for a specific date'). It includes the required date format (YYYY-MM-DD), distinguishing it from siblings like get_todays_space_photo and get_random_space_photo.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus its siblings (get_random_space_photo, get_todays_space_photo). The date format hint is present, but there is no mention of prerequisites, typical use cases, or situations where this tool is preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get today's Astronomy Picture of the Day from NASA.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It states the tool returns the APOD, but does not disclose potential failure scenarios (e.g., if no photo is available for today), data source, or side effects. For a simple read operation, this is adequate but not fully transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that is front-loaded and contains no extraneous information. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has zero parameters and an output schema (providing return value details), the description is complete. It explains the core action and result without needing further elaboration.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so schema coverage is 100%. With no parameters, the description does not need to add param details. The baseline for 0 parameter tools is 4, indicating the description adds value through clear purpose without needing parameter explanations.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool retrieves 'today's Astronomy Picture of the Day from NASA.' This clearly identifies the verb ('get'), resource ('astronomy picture of the day'), and scope ('today's'), distinguishing it from siblings like get_random_space_photo and get_space_photo_by_date.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
While no explicit when-to-use or when-not-to-use guidance is provided, the tool's name and description inherently imply it is intended for the current day's photo. Siblings exist for other temporal queries, providing implicit context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run a full multi-pass security and quality scan on a codebase directory.
Executes all analyzers: secret detection, OWASP security patterns, debug statement detection, code quality checks, style linting, and dependency vulnerability scanning via OSV.dev.
Use this when asked to review, audit, or scan a codebase for issues.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 explains the tool runs multiple analyzers, implying a comprehensive but potentially heavy operation. However, it does not disclose side effects, required permissions, or whether it modifies files, leaving some behavioral uncertainty.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise at five sentences, with the purpose front-loaded. Every sentence adds value, listing analyzers and usage context without unnecessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a multi-pass scan and the existence of sibling tools for individual checks, the description adequately covers what the tool does and when to use it. It mentions specific analyzers and an external service (OSV.dev), but could add more about output format or performance impacts.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter 'path' with no description, and schema coverage is 0%. The tool description implies the path is a codebase directory, but it does not explicitly define the parameter's format or constraints, providing only marginal added meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it runs a full multi-pass security and quality scan, listing all included analyzers (secrets, OWASP, debug statements, code quality, style linting, dependencies). This distinguishes it from sibling tools like scan_secrets or scan_code_quality, which are more specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description says 'Use this when asked to review, audit, or scan a codebase for issues', providing clear context for when to use it. However, it does not explicitly mention when not to use it or recommend alternative tools for narrower scans.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Scan a codebase for code quality and style issues.
Checks for debug/print statements, oversized files, long lines, deep nesting, too many function parameters, trailing whitespace, TODO/FIXME comments, mixed indentation, superfluous comments, and naming convention violations (PEP 8 for Python, camelCase for JS/TS).
Use this when asked to check code quality, readability, or style.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Lists many specific checks but does not disclose error behaviors, supported languages beyond Python and JS/TS, or limitations. No annotations to supplement, so description carries partial burden.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Concise two paragraphs, front-loaded with purpose, list of checks efficiently formatted. Slight redundancy but overall clean.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given an output schema exists (not shown), description covers core purpose and parameters fairly well. Could mention supported languages beyond two, but sufficient for basic use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The path parameter is not described in the schema (0% coverage). The description only implies it's the codebase path, lacking details on file/directory expectation or format.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it scans for code quality and style issues, listing specific checks. It distinguishes from sibling tools like scan_secrets or scan_dependencies by focusing on style and readability.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use this when asked to check code quality, readability, or style.' Provides clear context, though does not mention when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Scan a project's dependency manifests for known CVEs via OSV.dev.
Parses package.json, requirements.txt, pyproject.toml, go.mod, Cargo.toml, and Gemfile.lock, then queries the OSV vulnerability database for each dependency.
Use this when asked to check dependencies for vulnerabilities or CVEs.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the tool queries an external database (OSV.dev) and parses specific manifests, implying a read-only operation. However, without annotations, it doesn't elaborate on possible side effects, auth needs, or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise paragraphs with front-loaded purpose. No redundant information; every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers purpose, supported file types, database, and usage triggers. An output schema exists, so return format is not needed. Could add parameter details and error scenarios, but overall sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description should explain the 'path' parameter. It only implies it's a project path by context ('Scan a project's dependency manifests'), but does not clarify whether it's a file or directory, absolute or relative.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool scans dependency manifests for CVEs via OSV.dev, listing specific file types. This distinguishes it from siblings like scan_codebase (code scanning) and scan_secrets (secret scanning).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use this when asked to check dependencies for vulnerabilities or CVEs.' While it doesn't specify when not to use, the listed file types and context provide sufficient guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Scan a codebase directory for exposed secrets, API keys, and credentials.
Detects AWS keys, GitHub tokens, GCP keys, Slack tokens, JWTs, private keys, database connection strings, and generic hardcoded secrets.
Use this when asked to check for leaked credentials or secrets.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully convey behavior. It lists detectable secret types (AWS keys, GitHub tokens, etc.) but does not describe scanning behavior (e.g., recursion, file size limits, handling of non-code files). This is adequate for a simple tool but lacks behavioral depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short paragraphs. The first sentence immediately states the core purpose. Examples and usage are presented efficiently. No unnecessary words or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only one parameter and an output schema (assumed to document return values), the description covers purpose and usage adequately. It does not mention edge cases (e.g., path not found) but is complete for typical use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter is 'path', which the description clarifies as 'a codebase directory'. With 0% schema description coverage, the description adds essential meaning. However, it does not explain expected format (absolute/relative) or constraints (must be directory), which would improve semantic clarity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Scan a codebase directory') and the resource ('exposed secrets, API keys, and credentials'). It lists specific examples, making the purpose unmistakable. Among siblings like scan_codebase, scan_code_quality, and scan_security_patterns, the focus on credentials distinguishes it well.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use this when asked to check for leaked credentials or secrets.' This provides a clear when-to-use clause. However, it does not mention when not to use it or provide alternatives among siblings, which would strengthen guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Scan a codebase for OWASP-style security antipatterns.
Detects SQL injection vectors, XSS sinks, eval/exec usage, insecure cryptographic primitives, path traversal, insecure deserialization, and shell injection patterns.
Use this when asked about security vulnerabilities or OWASP compliance.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as read-only vs. mutation, permission requirements, or side effects. It only lists what patterns are detected, leaving safety and operational context unclear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is relatively concise, using a brief sentence and a bullet-like list to enumerate detected patterns. It front-loads purpose and avoids unnecessary repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the existence of an output schema (not shown), return values are partially covered. However, the description omits prerequisites, limitations (e.g., false positive rates), and whether the scan is static or dynamic, leaving gaps in understanding the tool's behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage for the single 'path' parameter, the description adds minimal value by mentioning 'codebase,' implying the path is a codebase location. It does not specify accepted formats or constraints, so it partially compensates but remains basic.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool scans a codebase for OWASP security antipatterns and lists specific vulnerability types. It distinguishes from siblings like scan_codebase, scan_code_quality, and scan_secrets by focusing explicitly on security vulnerabilities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description says 'Use this when asked about security vulnerabilities or OWASP compliance,' providing explicit usage context. It does not mention when not to use, but the sibling list implies boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0get_random_space_photoget_space_photo_by_dateget_todays_space_photoscan_code_qualityscan_codebasescan_dependenciesscan_secretsscan_security_patternsThe space photo tools are clearly distinct (random, specific date, today). The scanning tools are also distinct, though the comprehensive scan_codebase overlaps with the individual scanners; descriptions help clarify when to use each. The two domains are unrelated but clearly named, reducing confusion.
All tool names follow a consistent verb_noun pattern (get_*_space_photo for photos, scan_* for code scans). No mixing of conventions, making them predictable and easy to navigate.
With 8 tools across two domains, the count is well-scoped. The space photos domain has 3 tools (covers essential queries) and the scanning domain has 5 tools (comprehensive coverage without excess).
The space photo tools cover random, specific date, and today—sufficient for basic retrieval. The scanning tools cover quality, dependencies, secrets, security patterns, and a full scan, leaving few gaps. Missing features like search or description retrieval for photos are minor.
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