orchestrui
Use this read-only MCP server to determine, validate, and audit a minimal UI stack for frontend projects, returning library metadata and quality-gate evidence without executing installs or mutations.
list_libraries: list supported UI ecosystems and optionally filter by role such as charts or marketing-ui.recommend_stack: compute a minimal UI composition from structured host/task profiles, constraints, and existing stack, with task text as a legacy inference path.get_library_guidance: return per-library usage, avoid, compatibility, legal, and official-source guidance.search_components: search a selected library's public official component metadata, with a verified fallback.get_install_instructions: return official install commands as inert text without running them.audit_plan: score a proposed plan across quality, accessibility, motion, licensing, and Rive-lifecycle categories.
Integrates DaisyUI as a supported UI ecosystem, allowing the server to recommend or select Tailwind-based DaisyUI components, provide library guidance, search components, and audit the resulting UI stack for compatibility and quality.
Integrates Rive as a supported UI ecosystem for interactive vector/state-machine graphics, enabling the server to recommend Rive for motion and animation roles and include it in composable UI plans with evidence.
Click on "Deploy 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., "@orchestruirecommend a minimal UI stack for my React admin dashboard"
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.
OrchestrUI inspects a structured project profile, preserves compatible owners, and returns the smallest admissible UI composition with evidence for every decision.
Quick start
Requires Node.js 20+. Add the published read-only server to an MCP host:
{
"mcpServers": {
"orchestrui": {
"command": "npx",
"args": ["-y", "orchestrui@latest"]
}
}
}git clone --depth 1 https://github.com/ECD5A/OrchestrUI.git
cd OrchestrUI
bash scripts/install-codex.shgit clone --depth 1 https://github.com/ECD5A/OrchestrUI.git
Set-Location OrchestrUI
powershell -ExecutionPolicy Bypass -File scripts\install-codex.ps1git clone https://github.com/ECD5A/OrchestrUI.git
cd OrchestrUI
npm ci && npm run check
npm run benchmarkContributions are welcome — see CONTRIBUTING.md for the short path from fork to PR.
Use them from a frontend project:
$ui-library-router plan the smallest compatible UI stack.
$ui-orchestrator implement the approved plan.
$ui-quality-audit audit the rendered result.Related MCP server: UI Toolkit MCP Server
What it returns
{
"input_mode": "structured-profiles",
"selected": ["bklit-ui"],
"owners": {
"base-system": "host:shadcn-ui",
"data-visualization": "bklit-ui"
},
"rejected": [{ "id": "daisyui", "rule": "base-system-conflict" }]
}The committed suite passes 50/50 structured scenarios, 8/8 independently specified adversarial goldens, and three project fixtures in CI.
Skills and MCP
ui-library-router— inspects the host and assigns role ownership.ui-orchestrator— integrates approved pieces into one visual contract.ui-quality-audit— checks coherence, accessibility, responsiveness, motion, performance, and licensing.Read-only MCP —
inspect_project,list_libraries,recommend_stack,get_library_guidance,search_components,get_install_instructions, andaudit_plan.
Published through npm, the official MCP Registry, and versioned GitHub Releases. Local plugin configuration is in .mcp.json; setup, architecture, security, licensing, and contribution details live in docs/, SECURITY.md, THIRD_PARTY.md, and CONTRIBUTING.md.
The MCP surface is read-only and uses public upstream metadata; implementation code and third-party assets remain in the host project.
Ecosystems
Ecosystem | Role |
Kokonut UI | selective React/shadcn product polish |
React Bits | one signature creative effect |
daisyUI | intentional Tailwind semantic base |
Bklit UI | charts and data visualization |
Anime.js | bespoke timeline, SVG, or scroll motion |
Rive | licensed interactive vector/state-machine graphics |
Magic UI | animated marketing enhancement |
OrchestrUI is independent and is not affiliated with or endorsed by these upstream projects.
Support
If OrchestrUI helps your work, you can support its ongoing maintenance:
TON:
pointoncurve.tonBitcoin (BTC):
1ECDSA1b4d5TcZHtqNpcxmY8pBH1GgHntNUSDT (TRC20):
TUF4vPdB6QkjCvZq18rBL4Qj4dK5ihCN75
Contact
For questions about OrchestrUI, integration, consulting, or collaboration:
Available Tools
7 toolsaudit_planAudit an OrchestrUI planCRead-onlyIdempotent
Score a proposed library plan against the nine OrchestrUI quality and licensing categories.
| Name | Required | Description | Default |
|---|---|---|---|
| verifications | No | ||
| existing_stack | No | ||
| rive_asset_rights | No | not-applicable | |
| selected_libraries | No | ||
| includes_paid_content | No | ||
| redistributes_react_bits | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description is read-only and idempotent per annotations, and it does not contradict them. It implies a non-destructive analysis action ('score a proposed library plan') without adding extra behavioral context such as side effects, error conditions, or dependencies. Since annotations already cover the safety profile, the description is minimally sufficient but does not disclose anything beyond them.
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, information-dense sentence with no fluff. It front-loads the core action and object, and every word contributes to the meaning. This is a model of conciseness.
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 tool with six parameters, including a nested structure (verifications) and multiple enums, the description is far too sparse. It does not clarify the expected format of the 'proposed library plan,' what constitutes a 'score,' or how the nine categories relate to the inputs. While annotations confirm safety, the description lacks crucial operational detail for an agent to use it effectively.
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 was expected to compensate by explaining parameters, but it does not mention any of the six parameters (verifications, existing_stack, etc.). The sentence offers no clues about what 'verifications' or 'selected_libraries' mean, leaving the agent without useful semantic grounding for the inputs.
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 uses an active verb ('score') targeting a specific resource ('proposed library plan') and specifies the criteria ('nine OrchestrUI quality and licensing categories'). This distinguishes it from sibling tools like list_libraries or get_install_instructions, though it does not explicitly name a sibling for contrast. The purpose is clear and non-tautological.
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 provides no guidance on when to use this tool versus alternatives. There is no mention of use cases, prerequisites, exclusions, or references to sibling tools like recommend_stack or search_components. Users are left to infer the tool's role in their workflow without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_install_instructionsGet install instructionsBRead-onlyIdempotent
Return an official install command as inert text. This tool never executes shell or package-manager commands.
| Name | Required | Description | Default |
|---|---|---|---|
| component | No | ||
| library_id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, but the description adds valuable context: the tool returns inert text and never executes shell or package-manager commands. This reinforces the safety profile and goes beyond the annotations. No contradiction exists.
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 only two sentences and both earn their place. The primary purpose is front-loaded, and the important behavioral safety note is included without filler.
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, read-only tool, the description conveys the essential purpose and safety behavior. However, the lack of parameter explanation, especially for component, and no specification of what 'official install command' looks like in the response make it only minimally complete.
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?
Schema description coverage is 0%, so the description must compensate for the lack of parameter documentation. It does not: library_id is somewhat self-explanatory, but component's role is ambiguous, and no parameter details are provided beyond schema property names.
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 returns an official install command as inert text, identifying the resource and the tool's non-executing nature. It does not explicitly differentiate itself from sibling tools like get_library_guidance, which would earn a 5.
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?
There is no guidance about when to use this tool versus alternatives. The description explains what the tool does but gives no context about when an agent should select it over get_library_guidance or recommend_stack.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_library_guidanceGet library guidanceBRead-onlyIdempotent
Return use, avoid, compatibility, legal and official-source guidance for one ecosystem.
| Name | Required | Description | Default |
|---|---|---|---|
| library_id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful context about the returned guidance categories and 'official-source' framing, but does not disclose behavioral details such as lookup failure handling, authentication, or rate limits. With annotations present, this is adequate but not exceptional.
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 front-loads the core action ('Return'), names the target ('one ecosystem'), and lists the content categories without filler. Every element contributes meaning and the length is appropriate for the tool's simplicity.
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 one parameter, openWorldHint=false, and read-only annotations, the description is nearly sufficient. However, there is no output schema, so the exact return structure is not documented, and library_id semantics are left implicit. The listed guidance categories give a reasonable overview, making this minimally complete but with clear gaps.
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?
Schema description coverage is 0%, so the description must compensate for explaining library_id. The description only says 'for one ecosystem' and does not clarify that library_id identifies the library/ecosystem, how to obtain it, or what format is expected. This is a meaningful gap given there is no schema-level parameter description.
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 identifies the verb ('Return') and the resource ('guidance for one ecosystem'), and enumerates the content categories (use, avoid, compatibility, legal, official-source). It is reasonably distinct from siblings like get_install_instructions and list_libraries, though it does not explicitly differentiate itself and 'ecosystem' versus 'library' is slightly imprecise.
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 tells what the tool returns but does not give any guidance on when to use it versus alternatives such as get_install_instructions, recommend_stack, or search_components. There is no when-to-use, when-not-to-use, or alternative routing, leaving the agent to infer context from the tool name and sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
inspect_projectInspect local project metadataARead-onlyIdempotent
Read bounded package.json, package-lock.json and components.json within the server working directory. Return a HostProfile with evidence; never execute project code.
| Name | Required | Description | Default |
|---|---|---|---|
| relative_path | No | . |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnlyHint, idempotentHint, destructiveHint, so the description's job is lighter. The description adds value beyond annotations by naming the explicit file set, stating that it returns a HostProfile with evidence, and clearly declaring it never executes project code. A point is lost for not adding detail on what 'evidence' includes or path constraints like symlink handling.
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 sentences, zero filler. The first sentence states the capability and deliverables; the second adds the critical safety constraint. No repetition of schema or annotations, and the structure is front-loaded.
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 one-parameter, read-only tool with annotations covering safety and idempotence, the description is complete enough. A little more detail on the returned HostProfile shape or 'evidence' would be useful given there is no output schema, but the tool's simplicity and the 'never execute' disclaimer make it adequate.
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?
Schema description coverage is 0% and semantics must come from the description. The description implicitly associates relative_path with the working directory via 'within the server working directory', but it does not explicitly explain that relative_path selects a subdirectory or that 'bounded' prevents escape. Since there is only one parameter, the description mostly compensates but still does not fully clarify relative_path semantics.
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 states a specific verb ('Read'), names the exact resources ('bounded package.json, package-lock.json and components.json'), and defines the scope ('within the server working directory'). It also distinguishes from siblings by being the only read/metadata tool among the listed alternatives.
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 clearly communicates what the tool covers and does not cover: only the three named files, and no code execution. This gives an agent a strong signal for when to use it and what it cannot do. It does not explicitly name sibling alternatives to defer to, but given the sibling list, no alternative clearly overlaps with this read function.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_librariesList OrchestrUI librariesARead-onlyIdempotent
List the seven supported UI ecosystems and optionally filter by role. Returns metadata only.
| Name | Required | Description | Default |
|---|---|---|---|
| role | No | Optional role fragment such as charts or marketing-ui. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false, so the agent knows this is a safe read-only operation. The description adds 'Returns metadata only'—a useful disclaimer that goes beyond the schema. However, it does not detail what 'metadata' includes (e.g., supported versions, installation status), but the annotations carry most of the safety burden. This is slightly better than a baseline 3 because it explicitly limits scope, yet not rich enough for a 5.
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, compact sentence that names the purpose, the fixed set (seven ecosystems), the optional filter, and the return type. Every word earns its place; it is front-loaded with the core action and avoids fluff.
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 read-only list tool with one optional parameter and no output schema, the description is mostly complete. It tells the agent what the tool does, the scope (seven ecosystems), and that it returns metadata only. The only minor gap is that it does not explain what happens if the role filter does not match—but given the trivial complexity and rich annotations, this does not significantly impair usability.
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?
Schema description coverage is 100%, and the single optional parameter's description ('Optional role fragment such as charts or marketing-ui') is already explicit in the schema. The tool description merely mentions 'optionally filter by role' without adding new semantic detail (e.g., whether filtering is case-sensitive, partial match, or returns all if empty). Since the schema fully covers the param, a baseline 3 is appropriate.
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 verb ('List'), the resource ('the seven supported UI ecosystems'), and adds a key distinguishing detail ('optionally filter by role. Returns metadata only'). This clearly differentiates it from siblings like get_library_guidance or recommend_stack by emphasizing a fixed set (seven) and metadata-only scope.
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 indicates when to use it (to list UI ecosystems) and hints at a filtering option (by role), but it does not explicitly state when not to use it or name alternatives. Sibling names suggest related tools, but the description does not explicitly route to them, so it misses the 'when-not' guidance that would earn a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recommend_stackRecommend a minimal UI stackBRead-onlyIdempotent
Compute a minimal UI composition from structured host/task profiles, with task text supported as a legacy inference path.
| Name | Required | Description | Default |
|---|---|---|---|
| task | No | ||
| constraints | No | ||
| host_profile | No | ||
| task_profile | No | ||
| existing_stack | No | ||
| rive_asset_rights | No | not-applicable |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered and the bar is lower. The description adds one genuinely useful behavioral trait: task text is a 'legacy inference path,' signaling it is a deprecated/fallback input rather than the primary one. No contradiction with annotations exists, but the description does not disclose output shape, ranking behavior, or handling of conflicting inputs.
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 21-word sentence with zero filler. The primary function is front-loaded ('Compute a minimal UI composition from structured host/task profiles') and the legacy qualifier trails naturally. 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?
This is a high-complexity tool: 6 parameters, two nested objects containing roughly 18 fields total, no output schema, and 0% schema coverage. A single sentence cannot carry that weight. The agent is left without any indication of what the tool returns, how existing_stack or constraints influence the recommendation, or how the duplicated fields relate. Significant gaps remain for correct invocation and interpretation.
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 carries the burden and partially compensates: it maps 'structured host/task profiles' to host_profile and task_profile, and 'task text' to the task parameter, clarifying the primary vs legacy input paths. However, constraints, existing_stack, and rive_asset_rights are entirely unmentioned, and the duplication of constraints/rive_asset_rights at both top level and inside task_profile is not clarified. Meaningful but incomplete compensation.
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 states a specific action ('Compute a minimal UI composition') with a clear input source ('structured host/task profiles'), and the title anchors the resource as a 'minimal UI stack'. It is distinguishable from siblings — list_libraries, search_components, and get_install_instructions are about discovery/guidance, while this composes a recommendation. Minor ambiguity: 'composition' vs 'stack' terminology isn't unified, and the exact nature of the output is left abstract.
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 phrase 'with task text supported as a legacy inference path' is an implicit usage signal: structured profiles are the preferred path and free-text task is a fallback. However, there is no explicit when-to-use vs alternatives (e.g., when to pick this over search_components or list_libraries), and no exclusion criteria or prerequisites are stated. The guidance is implied rather than spelled out.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_componentsSearch official component metadataBRead-onlyIdempotent
Search a selected library's public official registry when available, with a verified metadata-only fallback.
| Name | Required | Description | Default |
|---|---|---|---|
| live | No | ||
| limit | No | ||
| query | No | ||
| library_id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds that it uses a 'verified metadata-only fallback,' which is useful behavioral context beyond annotations. However, it does not disclose quirks like rate limits, parameter defaults, or what happens with empty results. With annotations covering safety, the description adds moderate value.
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 one sentence, concise and front-loaded with the primary action. It includes a key fallback detail efficiently. However, it could be more structured with a second sentence about when to use it, earning a 4.
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?
The tool has 4 parameters, no output schema, and one required parameter. The description adds minimal context about the fallback but does not explain parameter usage, return format, or how it relates to sibling tools like get_install_instructions. For a search tool, an agent needs to know what 'metadata' means, how query filtering works, and the impact of 'live' parameter. The description leaves too much unspecified.
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?
Schema description coverage is 0%, so the description must compensate, but it mentions no parameters at all. It does reference 'selected library', which implies library_id, but does not explain the purpose of live, limit, or query. Given 4 parameters and 0% coverage, the description should provide more detail on parameters. Baseline for zero coverage is low, so a score of 3 is generous given the lack of parameter explanation.
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 states a clear verb ('Search') and resource ('a selected library's public official registry'), distinguishing it from sibling tools like get_install_instructions and list_libraries. It also notes a metadata-only fallback, adding specificity. However, it does not explicitly mention the returned component metadata or differentiate from all sibling tools explicitly.
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 implies when to use it (searching for components in a library's official registry) and hints at a fallback for libraries without one. But it does not provide explicit 'when-not-to-use' guidance or mention alternatives like get_library_guidance. It gives some context but lacks explicit routing among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v0.4.0- Added
inspect_project
6 tool updates
v0.3.0- First observed
audit_plan - First observed
get_install_instructions - First observed
get_library_guidance - First observed
list_libraries - First observed
recommend_stack - First observed
search_components
TDQS
Scored across 7 tools
Each tool targets a distinct concern: listing ecosystems, inspecting projects, auditing plans, retrieving install commands or guidance, recommending stacks, and searching components. There is no meaningful overlap that would cause an agent to misselect.
All tool names follow a consistent snake_case verb_noun pattern (list_libraries, inspect_project, audit_plan, recommend_stack, search_components). Even the 'get_*' tools fit the pattern naturally and remain predictable.
Seven tools is well-scoped for a UI orchestration assistant. Each tool covers a distinct part of the workflow—discovery, inspection, planning, guidance, installation instructions, and component search—without bloat or redundancy.
The set covers the core workflow of inspecting a project, planning/recommending a stack, auditing it, and retrieving install/usage guidance. Minor gaps exist, such as no explicit library comparison or plan persistence/export tool, but these are workable around and not critical to the server's advisory purpose.
Maintenance
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