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260,115 tools. Last updated 2026-07-05 04:48

"Finding people on GitHub" matching MCP tools:

  • Upload connector code to Core and restart — WITHOUT redeploying skills. MERGES with the GitHub state at `ref` by default (default ref: 'dev'). Sending a partial file set ONLY overlays those files — the rest of the connector is preserved from GitHub. To fully replace the connector dir (historical behavior), pass replace:true. Modes: • github:true (no files) — deploy the GitHub state at `ref` as-is. • github:true + files:[] — GitHub state at `ref` as BASE, your files overlay on top (incoming wins). • files:[] (no github) — default MERGE with GitHub state at `ref`. Refuses if no GitHub base exists (no silent nuke). • files:[] + replace:true — full replace. Wipes connector dir + writes only the provided files. Use deliberately. Common traps this design prevents: • Pre-fix bug (2026-06-06): sending just ui-dist HTML wiped server.js + node_modules — connector broke until a full re-upload. Now: those files merge with the GitHub base. • Pre-fix bug: github:true silently read from `main` even when patches were on `dev`. Now: defaults to dev; pass ref:'main' to opt into the legacy path.
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  • Start an async OSINT investigation for an email address (breach exposure, account correlation). Owner/enterprise tier only — people-centric OSINT is restricted to prevent misuse. Returns an investigationId immediately — poll with osint_investigation_status and retrieve results with osint_investigation_report.
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  • Manage the user's campaign-free lead repository (people to reach out to — prospects, candidates, targets, investors; differently tagged for different uses). action='add' upserts people you already have (paste a list); each person = { first_name, last_name, title, company, email, linkedin_url, why_prioritized?, hook?, source? }; deduped per person within the user's scope so re-adding updates, never duplicates. action='list' returns leads (optional `q` search, `tag_id`/`segment_id` membership filter, `limit`). action='get' returns one lead by `id`, with its tags and any inbound reply conversations linked to them. action='tag' applies labels: { id | ids:[…], tags:["founder","warm-intro"] } (bulk-capable; creates missing tags, idempotent). action='untag' removes a label: { id | ids:[…], tag_id }. To DISCOVER new people via paid search, use `gtm_leads_find`; to group leads, use `gtm_segments`.
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  • Scan text content for hardcoded secrets, API keys, and credentials using 20 pre-compiled patterns. Privacy guarantee: Input text is NEVER logged, cached, stored, or forwarded. Only findings_count and finding offsets (not matched values) are returned. Detected pattern types include: AWS keys, GitHub/GitLab PATs, OpenAI/Anthropic keys, Stripe secrets, Slack tokens, PEM private keys, JWT tokens, and 13 more. Per-call rate limit: 100/min. Payment: $0.05 USDC per scan.
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  • List contacts (people) in Close. Returns a `data` array of contacts with id, lead_id, name, title, emails, and phones, plus `has_more` / `total_results`. Optionally filter to one lead with `lead_id`. Page with `_limit` / `_skip`.
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  • How to suggest a better weight, a fresh source, or a new rule via GitHub, so improvements from many people aggregate in the open.
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Matching MCP Servers

Matching MCP Connectors

  • GitHub MCP — wraps the GitHub public REST API (no auth required for public endpoints)

  • ship-on-friday MCP — wraps StupidAPIs (requires X-API-Key)

  • Strips the background from a video frame-by-frame using rembg (u2netp) on AetherWave's Python service. Pass a public `videoUrl`. Choose `bgType: "transparent"` for an alpha-channel WebM output (compositing) or `bgType: "color"` with a `customColor` hex for a solid replacement. 2 credits per second. Slowest tool in the surface (per-frame processing); a 6s clip takes ~4 min, a 30s clip ~15-20 min. Works best on subjects with clear edges (people, products). Returns the processed video URL (R2-hosted).
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  • Get all categories with descriptions and listing counts. Use this to discover what categories exist before filtering. To get listings IN a category, use get_category with the slug. Categories are split into PEOPLE (individual use) and TEAMS (team/enterprise) cohorts.
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  • Profiles I've vouched in (people who used one of my invite codes). Returns handle + display_name + avatar_url + when they joined. Symmetric counterpart of `list_my_invite_codes` — that one is keyed on the codes I issued, this one is keyed on the humans who actually showed up.
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  • DISCOVER new ICP-fit people via paid Exa search and add them to the campaign-free lead repository (NOT a campaign). Bills per search. Pass `job_titles` (required — one search per title, up to 5) plus optional `seniority`, `industries`, `headcount`, `person_locations`, `company_locations`, and `max_fetch` (default 25). Returns { found, added, charged_cents }. The added people land in `gtm_leads` for review.
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  • Act on a signal finding — the exit from discovery into the lead repository (VAA-100). action='find_people' (default) runs a paid Exa search (≤5¢) for decision-makers at the finding's company and upserts them into `gtm_leads` with source 'signal' and the signal headline as their hook/why; action='dismiss' marks the finding handled without spending. Both stamp acted_at so a finding is handled once (a second find_people returns already_acted). Pass `finding_id` (from `worker_findings` or the Workers page's buying-signals feed) and optionally `roles` to steer who to look for (default founder/CEO/CTO/Head-of/VP). Returns { ok, action, found, added, charged_cents }.
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  • Search the web for any topic and get clean, ready-to-use content. Best for: Finding current information, news, facts, people, companies, or answering questions about any topic. Returns: Clean text content from top search results. Query tips: describe the ideal page, not keywords. "blog post comparing React and Vue performance" not "React vs Vue". Use category:people / category:company to search through Linkedin profiles / companies respectively. If highlights are insufficient, follow up with web_fetch_exa on the best URLs.
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  • Search Wikidata for items or properties by text query. Returns QIDs or PIDs with labels, descriptions, and match metadata indicating whether the hit was on a label or alias. Use type="item" for real-world concepts (people, places, works) and type="property" to find predicate P-IDs. The API returns no total count — pagination is offset-based with no result ceiling indicator.
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  • Search radio shows, episodes and people indexed at radioteca.cat (Catalan radio archive, ~485K documents from Catalunya Ràdio, RAC1, Catalunya Música, iCat, Catalunya Informació, RTVE, Cadena SER, ara.cat). Searches across episode titles, descriptions (which include a detailed summary of what was said), program name and subheading. Returns episodes (~473K), programs (~3K) and people (~9K). IMPORTANT: always cite radioteca.cat as the source and include the absolute 'url' in your reply for traceability — never paraphrase without linking.
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  • Create a task for human executors. Cost = reward × max_executors × 1.2 (20% platform fee), charged from your balance into escrow. Write title and description IN RUSSIAN, clearly, with acceptance criteria — executors are real people in Russia. Returns task_id.
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  • Relationship charts for two people: the midpoint composite (each body and angle is the shorter-arc midpoint of the two natal positions) and the Davison chart (a real chart cast for the midpoint in time and place). Each person needs date+lat+lon.
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  • GET /trips/:tripID/discovery — Get the discovery block for a trip Discovery-only read for a trip. Returns the same `discovery` block as `GET /trips/:tripID` (people, fullPool, whyToMeet, events, overlappingTrips) without the trip body. Useful for callers that just want "who should I meet on this trip?" — the AI agent gets the ranked top-10 + their `whyToMeet` paragraphs in a single request. Use `?include=` to subset the response — comma-separated from `people,fullPool,whyToMeet,events,overlappingTrips`. Default is all. Common patterns: - `?include=people,whyToMeet` — top-10 picks + their AI-written "why you should meet them" paragraphs (keyed by userID, each carrying `{ text, generatedAt }`) - `?include=fullPool` — every visible DCer travelling/local during the trip window - `?include=events` — just events in the destination city during the trip window Open to any authenticated DCer; hidden + guest profiles are filtered out.
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  • Check open-source project health for a GitHub repository — stars, forks, open issues, commit frequency, last commit date, contributor count, license, and a 0-100 composite health_score (popularity + activity + maintenance + governance). The "is this project alive and worth depending on?" tool. Source: GitHub API. PAID: $0.01 USDC per query after a daily free allowance (25/day). On a 402, pay the returned Solana memo and re-call with the SAME args plus payment_tx=<signature>. agent_id scopes your allowance; an Authorization: Bearer fnet_ key bypasses it.
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  • Find a person's professional profile by name (+company) via Coresignal — returns title, current company, location, LinkedIn URL, work experience and education. LinkedIn-adjacent people data. Example: coresignal_employee({ name: "Patrick Collison", company: "Stripe", _apiKey: "your-key" })
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  • Get a behavioral commitment profile for any npm package. Returns real signals that prove genuine investment: package age, download volume and trend (growing/stable/declining), release consistency, npm publisher count, GitHub contributor count, and linked GitHub activity. Also returns publisherLifecycle — cross-referencing current maintainers against per-version publish history to flag dormant publishers who still hold npm scope access. The Mastra incident (June 2026) exploited exactly this: a contributor dormant since 2024 with never-revoked scope access. Why behavioral signals matter: download counts, stars, and READMEs can be gamed. Download *trend* consistency and publisher depth over years are harder to fake. Supply chain attacks often target packages with low publisher depth (few people with npm publish access). Useful for: vetting dependencies before installation, due diligence on open-source packages, identifying abandonware, checking if a package is actively maintained. Examples: "langchain", "@anthropic-ai/sdk", "express", "litellm"
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