backgen-mcp

Server Quality Checklist

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Latest release: v1.14.1
Disambiguation4/5
Most tools have clearly distinct purposes (e.g., init_project, add_plugin, remove_plugin, generate_*). However, doctor and project_info both run 6-category diagnostics with overlapping descriptions, potentially causing confusion for an agent despite doctor having auto-fix capability.
Naming Consistency4/5
The majority of tools follow a verb_noun pattern in snake_case (add_plugin, generate_resource, list_plugins). Two tools break the pattern: doctor (just a noun) and project_info (noun_noun). This minor inconsistency is manageable.
Tool Count5/5
With 10 tools covering project initialization, plugin management, resource generation, and diagnostics, the count is well-scoped for the server's purpose. Each tool serves a distinct role without unnecessary bloat.
Completeness4/5
Core workflows are covered: init, add/remove plugins, generate resources/factories/seeds, and diagnostics. A minor gap is the lack of a dedicated tool to list installed plugins (only available through diagnostics/manifest), but this does not significantly hinder typical usage.
Average 4.6/5 across 10 of 10 tools scored. Lowest: 4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 117 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses ORM-awareness, output paths, faker usage, prerequisite (generate_resource), and how to run the seed. It does not mention error handling for missing resource or invalid dir, nor idempotency. Overall, it covers key behaviors well with minor gaps.
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 a single paragraph of about 150 words, adequately concise but could be better structured (e.g., bullet points for counts, output paths). It front-loads the main action but repeats count guidance. Some sentences could be trimmed for brevity without loss.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description covers purpose, prerequisite, usage, output paths, and execution steps. It lacks return value description and error scenarios, but overall it is fairly complete for a 3-parameter tool with clear schema descriptions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The tool description adds little beyond the schema: it repeats count usage and prerequisite for resource but does not introduce new semantic details. The parameter information in description is largely redundant with the schema.
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: creating a database seed file for a resource with realistic data. It uses specific verbs ('Creates a database seed file') and distinguishes itself by mentioning the prerequisite generate_resource, which is a sibling tool. The purpose is unambiguous and differentiates from other tools like generate_factory implicitly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context: use after generate_resource, for development or load testing, with count recommendations. However, it does not explicitly state when not to use this tool or compare it to alternatives like generate_factory. The prerequisite is clearly stated, but more explicit guidance on when to choose seed vs factory would improve clarity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that the tool is non-destructive to user-owned files, stating 'never touches user-owned files — only framework and shared files.' It also explains what --fix does. This is good transparency, though it could mention if any side effects exist beyond the listed fixes.
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 moderately long but well-organized with a numbered list of the 6 checks. The first sentence provides a summary. It is front-loaded and each sentence adds information, though a few minor redundancies exist (e.g., repeated mention of 'ownership').
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 categories) and no output schema, the description covers the input parameters, the checks performed, and the --fix behavior. It does not detail the output format beyond 'structured pass/fail report', which is a minor gap. Overall, it provides sufficient context for an AI agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description adds value beyond the schema. For the 'fix' parameter, it clarifies safety ('Safe to enable — never touches user-owned files'). For 'dir', it provides an example path. This helps the agent understand parameter semantics beyond the basic 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 explicitly states the tool runs 6 health check categories and returns a structured pass/fail report. It lists each category in detail, making the purpose crystal clear. The tool's name 'doctor' is vague, but the description provides a specific verb and resource, and it is clearly distinguished from sibling tools like 'init_project' or 'generate_resource'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises to run this tool before telling the user a project is ready, providing clear context on when to use it. It also mentions the --fix flag for auto-resolving issues. However, it does not explicitly state when not to use it or mention any alternatives, though the sibling tools are unrelated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses creation of multiple files, interactive prompting, and relation behavior (belongsTo/hasMany). However, it does not specify whether existing files are overwritten or if the tool has destructive tendencies, which is a minor gap.
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 comprehensive but slightly verbose. It is well-structured with the main purpose first, followed by details on relations and prerequisites. Every sentence adds value, though some consolidation could improve conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description thoroughly explains what is generated (endpoints, layers, validation, tests) and how relations work. It also cross-references add_plugin for auth, making the tool self-contained in context. No major gaps identified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides 100% coverage with clear descriptions for all four parameters. The description adds value by explaining relation semantics (singular vs. plural) and interactive mode, but this is supplemental rather than essential, as the schema already explains the format.
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 uses a specific verb 'Generates' and identifies the resource as a full CRUD module, listing the specific components (controller, service, repository, etc.). It clearly distinguishes itself from sibling tools like add_plugin or generate_factory by focusing on resource generation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use the tool (to generate a CRUD module) and provides an alternative instruction: 'Run add_plugin first if you need auth protection.' Also explains interactive vs. non-interactive mode, giving clear guidance on when to provide fields.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses generation time (10–30 seconds), longest step (npm install), and mentions presets auto-installing JWT. Could be more explicit about overwrite behavior if directory exists, but otherwise transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is informative but slightly verbose; however, every sentence adds value. Well structured with clear flow: what it does, what to run next, special commands. Could be tightened without losing clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 parameters, no output schema), the description covers all necessary context: project contents, post-creation steps, preset usage, and typical duration. Complete enough for an agent to understand the tool's role in the workflow.
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 coverage is 100% with adequate descriptions for all 6 parameters. Description adds workflow context but does not significantly enhance parameter meaning beyond what schema already provides. Baseline score of 3 is appropriate.
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 explicitly states it creates a backend project with specific tools (Express.js, TypeScript, ORM, etc.), and distinguishes from sibling tools like add_plugin and generate_resource by naming them and their purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Clearly states when to use this tool (create a new project) and when to use siblings (add_plugin for auth/payments, generate_resource for CRUD). Advises calling list_presets first to see domain preset contents.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden and does well, detailing output location, use of faker, ORM-awareness, and schema dependency. However, it doesn't mention whether the tool overwrites existing files or has other side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the main purpose, each sentence adds value, and there is no redundancy or irrelevant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description is fairly complete, covering prerequisites, naming, output location, and usage. Minor gap: no mention of overwrite behavior, but overall sufficient.
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?
Schema description coverage is 100%, and the description adds value by reinforcing the PascalCase constraint and providing example usage. It provides context beyond the schema's field 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 it creates a test factory file for a resource, explains what a factory is with an example, and distinguishes from sibling tools like generate_resource by specifying it as a prerequisite.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells when to use the tool (after generate_resource) and provides a naming constraint (PascalCase only), offering clear guidance and preventing common mistakes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Declaration that the tool is read-only and never modifies files is explicit. With no annotations provided, the description carries the full burden and addresses the key behavioral trait for a diagnostic tool.
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?
Three concise sentences front-load the purpose, include usage guidance, and note read-only nature. No unnecessary words or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Description covers input, purpose, and usage context well. Lacks detail on the returned report structure beyond listing categories, but given no output schema, this is a minor gap. Still adequately complete for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the schema already describes the 'dir' parameter with example and default. The description does not add additional meaning beyond what the schema provides, so a baseline score of 3 is appropriate.
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 it runs 6-category diagnostics on a BackGen-generated project and returns a structured report. It lists specific checks (Node.js version, npm, .env, etc.) and distinguishes itself from sibling tool 'doctor' by noting it is read-only, while doctor has auto-fix capability.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says to use this tool before or after making changes to verify project state, and directs to 'doctor' for a focused health check with auto-fix. Provides clear when-to-use and when-not-to-use guidance with alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description discloses that the tool lists plugins and returns descriptions and categories, indicating read-only behavior. It does not mention potential pagination or auth requirements, but for a simple list operation this is sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences convey the purpose, usage, and relationship to siblings with no redundancy. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters, no output schema, and low complexity, the description fully covers what the tool does, what it returns, and how to use it in a workflow. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so baseline is 4. The description adds no parameter information, but this is appropriate since there are no parameters to describe.
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 specifies 'List all available BackGen plugins with their descriptions and categories,' clearly indicating the verb (list), resource (plugins), and data returned. It distinguishes from siblings by naming list_presets and add_plugin.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states 'Call this first to show the user what features they can add, then call add_plugin with the chosen plugin name.' Also mentions list_presets as an alternative for domain presets, providing clear when-to-use and sequence guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes all actions: injects files, registers routes, adds dependencies, injects env vars, updates manifest. No annotations provided, so description fully covers 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?
Single dense paragraph, but all sentences are necessary and informative. Slightly more structure (e.g., bullets) could improve scannability, but still concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Completely describes effects and prerequisites given no output schema. Covers plugin options, conflicts, dir default, and post-step (doctor).
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?
Schema coverage is 100% (baseline 3). Description adds value beyond schema by explaining categories, mutual exclusivity, and devops shorthand for the plugin parameter.
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 installs a feature plugin into a BackGen-generated project, with specific verb and resource. It distinguishes from siblings like list_plugins and remove_plugin.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance: call list_plugins first, run doctor afterwards. Warns about jwt/clerk conflict and explains devops shorthand. Provides clear when-to-use and alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavioral traits: it details exactly what is deleted (files, npm deps, env vars, route registrations, .backgenrc.json entry) and what is safe (user-owned files untouched). Clearly labels it as destructive but safe.
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 comprehensive but slightly lengthy. However, it is well-structured with front-loaded action, then details, then usage context. Each sentence adds value, no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description explains all behavioral effects, parameter semantics, and provides post-removal guidance (use doctor). Complete for a destructive tool with two parameters and no output.
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?
Schema coverage is 100%, but description adds significant value: explains plugin categories, mutual exclusivity between jwt/clerk, removes all with shorthand, gives an example for dir, and clarifies project validity requirements.
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 removes a plugin from a BackGen project, listing specific actions (deletes files, dependencies, env vars) and distinguishes itself from siblings like add_plugin and list_plugins.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly describes when to use (undo add_plugin, switch auth providers, clean up features) and provides pre/post steps (run list_plugins first, run doctor after). Clearly recommends add_plugin as alternative for installation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that presets generate multiple interconnected resources with database models, CRUD endpoints, and Swagger docs. It also states that all presets auto-install JWT authentication and wire resources together. Since no annotations are provided, this description fully covers behavioral aspects such as side effects and capabilities.
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 concise with three sentences. The first sentence states the core action, the second provides detailed output description, and the third gives usage instructions. Every sentence adds value, and there is no wasted verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the tool (no parameters, no output schema), the description is complete. It explains what the tool returns (list of presets with their generated resources), its role in the workflow, and its integration with init_project. No gaps remain.
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?
The tool has no parameters, so the input schema provides no information. The description implicitly confirms that no inputs are needed by stating 'Call this first' without any input requirements. This is sufficient and adds no confusion.
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 starts with 'List all available domain presets', which is a specific verb and resource. It clearly distinguishes its role from sibling tools like init_project by explaining that it provides presets for the user to choose before calling init_project.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool: 'Call this first to help the user choose the right domain'. It also provides a clear follow-up action: 'then call init_project with --preset to generate it'. This gives direct guidance on usage context.
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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