@trigvale/mcp
Pulls real-time evidence from dev.to to validate startup ideas.
Pulls real-time evidence from GitHub to validate startup ideas.
Pulls real-time evidence from Reddit to validate startup ideas.
Pulls real-time evidence from Stack Overflow to validate startup ideas.
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., "@@trigvale/mcpvalidate my idea for a SaaS that uses AI to summarize meetings"
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.
@trigvale/mcp
MCP server for Trigvale — validate startup ideas before your build agent scaffolds code.
This is the official Model Context Protocol server for Trigvale. It exposes one tool, validate_idea, to MCP-aware build agents (Cursor, Claude Code, Codex, Claude Desktop). The intended UX is straightforward: your agent calls validate_idea before it writes a single line of scaffolding. A green or amber verdict greenlights coding; a red verdict surfaces the assumption you should test first.
What validate_idea returns
10-dimension scorecard — Venture Readiness Score (VRS, 0–100), computed deterministically in code from the model's per-dimension scores against published anchors. The model never picks the VRS itself.
Evidence brief — every item tagged by
sourceKind∈ {observed,inferred,missing,ai,user-claim} so you know what's grounded vs assumed.Verdict —
kill,pivot,test, orbuild. Build is rare by construction.Founder-fit calibration — per-dimension deltas applied from the user's declared skill graph (declared inputs only — never extrapolates from past failures).
1–3 archetype assignments — clusters the idea into known patterns (e.g. "vertical-ai-saas", "agentic-workflow") with structural cautions for the cluster.
Live evidence — real-time pull from Reddit, GitHub, Hacker News, Stack Overflow, and dev.to for the idea's dominant archetype, refreshed every 6 hours.
Full output schema and methodology: https://trigvale.com/methodology
Related MCP server: product-spec-mcp
Install
npx -y @trigvale/mcpYou'll need a Trigvale agent token (Integrate plan, $99/mo). Get one at https://trigvale.com/settings.
Configuration
Drop this into your MCP client's config (mcpServers block):
Cursor (~/.cursor/mcp.json)
{
"mcpServers": {
"trigvale": {
"command": "npx",
"args": ["-y", "@trigvale/mcp"],
"env": {
"TRIGVALE_AGENT_TOKEN": "tvk_xxx"
}
}
}
}Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json on macOS)
Same shape — replace tvk_xxx with your token from /settings.
Claude Code
Add the snippet to .mcp.json at the repo root, or use claude mcp add to register it interactively.
Self-hosted / dev environments
Override the API base via env:
TRIGVALE_API_BASE_URL=https://api-dev.trigvale.com npx -y @trigvale/mcpDefault base is https://api.trigvale.com (production).
Example usage in an agent prompt
"Before you scaffold this project, call
validate_ideawith the user's pitch. If the verdict iskillorpivot, surface the weakest assumption and ask the user whether to proceed anyway. Iftest, suggest the validation sprint Trigvale recommends before any code."
Why a separate validation step?
Building has gotten cheap. The bottleneck is no longer execution — it's deciding what to execute. Most ideas should be killed, pivoted, or tested before code is written. validate_idea makes that decision an explicit, repeatable step in your agent flow, with a deterministic verdict and source-tagged evidence you can show the user.
Pricing & plans
Plan | Monthly | Includes |
Free | $0 | 1 verdict / month, lite preview |
Starter | $12 | 10 verdicts / month, full brief |
Validate | $39 | Live evidence pipeline + sharpening + sprint generator |
Integrate | $99 | Everything above + agent tokens for this MCP server |
The agent token required by this MCP is gated to the Integrate plan. Per-call entitlement is re-checked server-side on every /agent/v1/evaluate call — downgrades take effect immediately.
Full pricing: https://trigvale.com/pricing
Source
This is a read-only mirror of the MCP server source from Trigvale's private monorepo (the rest of which contains the proprietary rubric, infrastructure, and billing logic). Releases are cut on the private side and synced here. PRs are welcome — please open issues at https://trigvale.com/contact instead of GitHub Issues so we route them through the support workflow.
License
MIT — sydacos GmbH, 2026.
Links
Homepage: https://trigvale.com
Integration docs: https://trigvale.com/integrations
Methodology: https://trigvale.com/methodology
Sample brief: https://trigvale.com/sample
Contact: https://trigvale.com/contact
Available Tools
1 toolvalidate_ideaA
Validate a startup idea before scaffolding code for it. Returns a scorecard (10-dimension Venture Readiness Score, 0–100), an evidence brief tagged by source (observed / inferred / AI / claim), a kill / pivot / test / build verdict, founder-fit calibration deltas if a skill graph is on file, and 1–3 archetype assignments with structural cautions for that cluster. Call this BEFORE writing project scaffolding when the user is greenfield-building from a raw idea.
| Name | Required | Description | Default |
|---|---|---|---|
| rawIdea | Yes | The idea to validate. Free-form text; mess is fine — Trigvale normalizes it. Be specific about who has the pain, who pays, and why now. | |
| save | No | When true, persist the brief to the user's vault so they can revisit it at trigvale.com/ideas/{id}. Default true. Set false for ephemeral checks. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully carries the burden of behavioral disclosure. It lists exact return values (scorecard, evidence brief, verdict, deltas, archetypes) and mentions conditional behavior ('if a skill graph is on file'). It also notes the persistence behavior tied to the `save` parameter.
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 compact but includes necessary detail. It front-loads the core action ('Validate a startup idea before scaffolding code for it.'), then lists outputs efficiently. A very slight verbosity in listing all outputs prevents a perfect 5, but it remains well-structured.
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's moderate complexity (2 parameters, no output schema, no annotations), the description provides sufficient context: it explains the full return structure, conditional behavior, and persistence options. No obvious gaps remain for an LLM to select and invoke the tool correctly.
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 description adds valuable usage hints beyond the schema: for `rawIdea` it advises specificity and normalizes messiness; for `save` it explains default behavior and ephemeral usage. This enriches the agent's understanding of each parameter.
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 opens with a clear action-verb+resource: 'Validate a startup idea before scaffolding code for it.' It unambiguously states the tool's function and context, distinguishing it from potential siblings by specifying the prerequisite step.
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 tells when to use the tool: 'Call this BEFORE writing project scaffolding when the user is greenfield-building from a raw idea.' While it doesn't mention when not to use it or alternatives, the guidance is clear and actionable given no sibling tools exist.
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. Dates show when Glama detected each change.
1 tool update
v0.1.0- First observed
validate_idea
TDQS
With only one tool, there is no possibility of confusion between tools. The single tool has a distinct and clear purpose.
The single tool 'validate_idea' follows a clear verb_noun pattern and is descriptive. However, with only one tool, consistency across multiple tools cannot be fully assessed.
One tool is borderline for the implied domain of startup building. While the validation step is specific, a broader lifecycle (e.g., scaffolding) is suggested but not covered, making the count feel thin.
The tool covers only idea validation. Obvious gaps exist, such as tools for scaffolding, iteration, or testing, which are referenced in the description but not provided. This will likely cause agent failures after validation.
Maintenance
Resources
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If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Score, validate, and pressure-test startup ideas with AI from Claude or any MCP agent.
Deterministic sealed verdicts on public claims and startup ideas (0-LLM claim-safety guardian).
Actwise Ideation helps founders benchmark startup and product ideas before they build.
Test the idea before you build it. Rapidly works inside your AI agent.
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