Agent integrations
Evidence Signal MCP
Evidence Signal is the upstream counterpart to Resident. It reads your support and incident tickets where they already live, clusters them into themes, and drafts candidate requirements with the evidence attached — so a requirement can always be traced back to the tickets that justified it.
Using Evidence Signal as a Claude Skill
The fastest way to run Evidence Signal. No connector setup — Claude runs the skill locally and your ticket content never leaves your machine.
- In Claude, go to Settings → Capabilities and enable Code execution and file creation.
- Go to Customize → Skills → “+” → Create skill and upload the Evidence Signal skill package.
- Toggle it on, start a conversation, and point it at your exported ticket file.
- When you’re ready to run a scored analysis, paste your license key directly into the chat message. Claude uses it only for that conversation and never stores or repeats it back — once per new conversation, not once ever.
Get a license to download the skill package.
Endpoint
https://requirementshub.ai/api/public/evidence/mcp
Speaks MCP Streamable HTTP (JSON-RPC 2.0 over POST). Stateless — clients carry the session token themselves across calls. The same handlers back the REST endpoints under /api/public/evidence/v1/*, so both paths behave identically.
Authentication
Evidence Signal uses bearer license-key / session-token authentication — not the Supabase OAuth flow used by the main SaaS MCP connector. Call evidence_start_session with your license key to mint a short-lived session token, then send that token as Authorization: Bearer <token> and in the token field of every subsequent tool input (the mismatch guard rejects mixed-identity calls).
The session is bound to a seat, and each seat is bound to a RequirementsHub user at provisioning time. That is how an accepted candidate gets a truthful author without a second confirmation step in the app.
What actually gets sent
Ticket bodies are parsed on your machine. What crosses the wire is a skeleton: counts, severities, timestamps, salted ticket-ID hashes and structural links. Two strictness modes are enforced server-side, chosen by the sharing mode stored on the evidence source — a client that claims a different mode is rejected rather than trusted.
- local_only — the strict schema rejects every free-text field. Themes and candidates are scored and ranked, and rendered locally from the templates pack. Nothing readable ever leaves the machine.
- share_accepted — theme and candidate titles are permitted, because materializing a requirement into a project requires a title. This is the only mode in which
evidence_accept_candidatecan write.
Run telemetry is aggregate-only (counts and finding-code rollups) and is retained for benchmarking; per-call usage events are purged on a 12-month rolling window.
Setup
- Obtain an Evidence Signal license key — see pricing.
- Register the endpoint above in your MCP client (Claude custom connector, Copilot agent, ChatGPT app, or any MCP-compatible runtime).
- Call
evidence_start_sessionwith the license key and a machine fingerprint. Store the returnedtokenclient-side. - Fetch the
keywords,clustering, andtemplatespacks, cluster your tickets locally, then submit the skeleton. - Review the ranked candidates and accept the ones worth keeping with
evidence_accept_candidate.
Available tools
evidence_start_session
Exchange an Evidence Signal license key and machine fingerprint for a short-lived session token used as the bearer credential on subsequent Evidence Signal tool calls.
evidence_get_catalog
Fetch the Evidence Signal catalog: available packs, finding codes, and engine / pack version metadata.
evidence_get_pack
Fetch one versioned, watermarked JIT pack — 'keywords', 'clustering', or 'templates' — used to cluster tickets and draft candidate requirements locally.
evidence_list_target_projects
List the RequirementsHub projects the seat's bound user may materialize candidate requirements into. Never auto-creates a project.
evidence_submit_skeleton
Submit an anonymized Evidence Signal skeleton (structured signals only, no ticket text in local_only mode) for scoring. Returns clusters, themes, ranked candidate requirements, and findings.
evidence_accept_candidate
Materialize a candidate requirement into a RequirementsHub project as a real requirement, optionally overriding type and priority. The only write tool; requires a share_accepted source.
Scoring requirements documents instead of tickets? See Resident MCP. Connecting an AI assistant to your account? See connecting an AI assistant.