"Medium" matching MCP tools:

• smithsonian_browse_categoryA

  smithsonian-mcp-serverA

  Browse Smithsonian objects within one exact category — a single museum (mode "museum"), culture, indexed date term (mode "period"), object type (mode "medium"), or subject term (mode "topic"). The value must be an exact indexed category term, not free text: resolve museum, culture, period, and topic vocabulary with smithsonian_list_terms first (object_type is not enumerable there — harvest it from smithsonian_search_objects results, and treat each casing as its own category, since a harvested object_type covers only the casing it was written in). Returns the category total count, a page of matching objects, and a museum breakdown of that page; page the full category with start and rows. For open-ended or topic discovery, start with smithsonian_search_objects instead.

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• met_search_collectionsA

  met-museum-mcp-serverA

  Search the Metropolitan Museum of Art collection by keyword and optional filters. Returns the total match count and a page of matching object IDs, which met_get_object resolves to full records. Relevance is keyword-based, not semantic; department and geographic filters narrow results more than a longer query. The medium parameter maps to the classification field (pass "Paintings", "Drawings", etc., not material descriptions like "Oil on canvas"). isPublicDomain guarantees CC0-licensed images; hasImages also includes copyrighted works. isOnView restricts results to works currently on display in a Met gallery.

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• url.qaA

  DocImprintA

  Fetch a public HTTPS URL and answer a specific question about its content. Lean mode — no bundle stored. Use when you have a precise question about a web page. For a broad summary, use url.summarize. For multi-document Q&A, use collection.ask instead. Returns: { url, answer, answer_cited: { value, confidence, citations[] }, confidence: "high"|"medium"|"low", truncated } Example prompts: - "What is the refund policy at https://docs.example.com/policy?" - "Look at [URL] and tell me what the delivery terms are." - "Answer this question based on the content of [URL]: [question]."

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• document.check_claimsA

  apiA

  Verify a list of factual claims against document text. Uses a quality AI model with citation-level evidence. Use after document.extract_text or url.extract when you need to validate specific factual assertions. For open-ended questions about a document, use url.qa instead. For multi-document investigation, use collection.ask. Typical workflow: document.extract_text/url.extract → document.check_claims. Returns: { claims: [{ claim, status: "supported"|"contradicted"|"not_found", evidence: { quote, paragraphs[] }, confidence: "high"|"medium"|"low" }], truncated: boolean } Example prompts: - "Check whether this contract mentions a liability cap of $1M." - "Verify these claims against the document: [claims list]." - "Does the report actually say revenue grew 23%?"

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• explain_scoreA

  yieldhunterA

  Agent-readable explanation of why an opportunity received its risk-adjusted score. Chain: pass list_yields opportunities[].id as poolId, opportunityId, or id (aliases for the same key). Returns a summary paragraph, factor bullets with weighted contributions, net-yield notes, and an exitRisk heuristic line (liquidity-only — not on-chain withdraw or transfer/eligibility; medium often confidence-driven, not pool size; extra bullet when knownIssues note transfer restrictions). sourceChainKey is ethereum|base|arbitrum only (unknown → VALIDATION_ERROR; no invented bridge fee). Prefer this when you need to justify a ranking to a human or another agent. Research only — non-custodial. Example: { "opportunityId": "43641cf5-a92e-416b-bce9-27113d3c0db6", "horizonDays": 30 } Also accepts poolId or id with the same value.

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• call_creative_worldsA

  HeistaA

  Heista's creative direction engine — same engine the Creative Director specialist runs internally, exposed over MCP. ONE-SHOT: give a brief, get N finished creative outputs. For back-and-forth refinement, or output shapes the `medium` enum below does not cover, use chat_with_creative_worlds instead. OUTPUT SHAPE switches on the `medium` arg: • omitted → N territory cards (default exploration). Each card sits on different psychology / craft / feel / world axis coordinates so the set spans the creative space rather than orbiting one insight. Card has: name, campaign line, 5-8 sentence pitch, one-sentence strategic bet, resolved axis state names, creative-director rationale. • `tvc` → N TVC scripts (15-90s — hook, arc, resolve, sound design, end line). • `billboard` / `ooh` / `print` → N out-of-home concepts (visual concept + line + placement rationale). • `social` → N social-video concepts (hook + format type + middle beat + payoff, optimised for Reels / TikTok / Shorts). • `activation` / `experiential` → N activation concepts (space design + user journey + peak moment + takeaway artifact). • `audio` → N sonic / radio concepts (sonic scene + voice + audio arc). • `campaign` → N full campaign platforms (insight → big idea → strategy → visual world → production roadmap). The engine can also produce manifesto / copy, naming, packaging, PR stunts, content series, brand positioning, partnerships — these output shapes are NOT in the medium enum, so use chat_with_creative_worlds when the user wants one of those. USE WHEN: user says "give me ideas / options / directions / territories", "what angles work for...", "show me three / five ways to...", "write a TVC for...", "draft billboard concepts for...", "I need fresh thinking on...". DO NOT USE to refine one existing direction (use chat tool), to critique work, for OKRs / internal docs / strategy decks, or anything outside advertising creative direction. INPUTS: brief (the creative problem, free text), count (2-6 concepts), optional brand_id (from list_brands or any create_powersource_* — when provided the engine grounds output in the brand's buyer tensions, voice, and selling points), optional medium (above), optional lens_hint (apply a playbook or signature move as a creative constraint), idempotency_key (safely retryable for 5 minutes). Returns the finished creative output as narrative text PLUS a structured array of resolved axis coordinates for programmatic use. Metered — typically 3-15 credits per call depending on count and brand context size. Charged after success on actual token usage.

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• utm_builderA

  Moltline Outbound EngineA

  Build a UTM-tagged URL for campaign tracking. FREE. Typical input {"url": "https://example.com/pricing", "source": "newsletter", "medium": "email", "campaign": "spring-launch"} returns {"tagged_url": "https://example.com/pricing?utm_source=newsletter& utm_medium=email&utm_campaign=spring-launch"}. Use to build one tagged tracking URL. Not for analyzing campaign results and not for anything that belongs in the message body (audit_copy). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "url must start with http(s)://"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

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• alphai_actionable_nowA

  AlphaAIA

  Breaking, decision-grade news from the last few hours. The primary filter is the enricher's actionability score, and the gate is strict: by default only actionability='high' (a time-sensitive development — fresh guidance cut, halted trading, breaking M&A, surprise print) qualifies. Big-but-not-urgent stories scored 'medium' (shape a position over days/weeks) never appear at the default floor no matter how high their novelty — pass min_actionability='medium' to include them, or use alphai_trending / alphai_ticker_news for the broader tape. Market-wide macro releases (an FOMC decision, a CPI/jobs print) qualify and carry no tickers. An empty list on quiet nights/weekends is expected — it means no high-actionability prints in the window, not an error; widen hours or min_actionability before concluding nothing happened. The time window is over each article's PUBLICATION time, not the underlying event's date, so a fresh pick-up of an older event can appear; min_novelty (not the window) is what drops post-event recaps of already-public stories. Ordered novelty-first; syndicated reprints collapsed by story (dedupe=false to keep all), and each collapsed item reports story_id (the story root's uid — the same key alphai_trending and the search tools report for that story), sources_count and sources. Most stories run at a single outlet, so sources_count is usually 1; a value above 1 is the signal, not the number itself. Each item carries the full AI analysis inline — no follow-up alphai_article call needed for depth. Informational and AI-generated — not investment advice.

  ConnectorAPI key

• competitive_deep_dive_asyncA

  mcp-knowledgeC

  Async variant of competitive_deep_dive. Returns immediately (<200ms) with a job_id. The research runs in the background (p50≈25s, p95≈30s for depth=medium). Poll the result with competitive_deep_dive_result(job_id) after the eta_seconds hint. Use this instead of competitive_deep_dive when the agent cannot wait >15s for a response. Inputs: same as competitive_deep_dive — company (required), competitors (optional list, max 5), depth (easy/medium/hard, default medium). Async tool — register a webhook via `webhooks_manage(register, url, [job.completed])` to receive callbacks instead of polling. Faster + lighter.

  ConnectorNo auth

• document.extract_structuredA

  apiA

  Extract typed fields from document text using a caller-defined schema. Uses a quality AI model with retry logic. Use when you need specific data points from a document rather than full text. For invoices with known fields, document.parse_invoice (prebuilt schema) may be simpler. For general summarization, use document.summarize instead. Schema format: { "field_name": "type hint or description" } — e.g. { "contract_date": "ISO date", "party_a": "string", "penalty_usd": "number" }. Returns: { data: { <field>: value }, data_cited: { <field>: { value, confidence: "high"|"medium"|"low", citations: [{ quote, paragraphs[] }] } } } Example prompts: - "Extract the contract date, parties, and penalty amount from this agreement." - "Pull the vendor name, PO number, and total from this document." - "Get me all named fields from this form using my custom schema."

  ConnectorNo auth

• utm_builderA

  Moltline Outbound EngineA

  Build a UTM-tagged URL for campaign tracking. FREE. Typical input {"url": "https://example.com/pricing", "source": "newsletter", "medium": "email", "campaign": "spring-launch"} returns {"tagged_url": "https://example.com/pricing?utm_source=newsletter& utm_medium=email&utm_campaign=spring-launch"}. Use to build one tagged tracking URL. Not for analyzing campaign results and not for anything that belongs in the message body (audit_copy). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "url must start with http(s)://"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• competitive_deep_diveA

  gapup-mcpC

  Gold-standard competitive deep dive — STRUCTURED multi-source data (no LLM narrative). Pair tool: `competitor_intel` for LLM-narrated board briefing + slide script. Aggregates Wikipedia, Yahoo Finance, SEC EDGAR, Wayback Machine, DuckDuckGo, HackerNews, domain scraping — all keyless. Returns agent-shaped JSON: KPIs (funding, employees, revenue, market cap), P0/P1/P2 competitive signals, pricing radar, competitor comparison matrix, Wayback timeline, positioning (sector/industry/icp_hypothesis/moat_signals), quality score. Every field is sourced or marked unavailable — no hallucinated figures. SLA: p50 ~25s, p95 ~30s · score 80+ on listed targets (US/EU/foreign) · score ~40 on private companies (no EDGAR/Yahoo data). Use sync for batch agents (≤30s tolerance). Use `competitive_deep_dive_async` + `competitive_deep_dive_result(job_id)` for conversational agents. Inputs: company name or domain (required), optional competitor list (≤5), optional depth (easy/medium/hard).

  ConnectorNo auth

• competitive_deep_dive_asyncA

  gapup-mcpC

  Async variant of competitive_deep_dive. Returns immediately (<200ms) with a job_id. The research runs in the background (p50≈25s, p95≈30s for depth=medium). Poll the result with competitive_deep_dive_result(job_id) after the eta_seconds hint. Use this instead of competitive_deep_dive when the agent cannot wait >15s for a response. Inputs: same as competitive_deep_dive — company (required), competitors (optional list, max 5), depth (easy/medium/hard, default medium). Async tool — register a webhook via `webhooks_manage(register, url, [job.completed])` to receive callbacks instead of polling. Faster + lighter.

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• calculate_risk_scoreA

  ContrastAPIA

  Composite CVE risk score (0-100) — fuses CVSS, EPSS, KEV, and PoC into a single agent-ready triage signal. Formula: CVSS*0.20 + EPSS*0.35 + KEV*0.30 + PoC*0.15 (each component rescaled to 0-100 before weighting). Multiplicative boosters applied in order: KEV+PoC combo (*1.15), critical-severity-with-high-EPSS (CVSS>=9 AND EPSS>0.7, *1.10), recently published (within last 7 days, *1.05). Final score clamped to [0, 100]. Label bands: CRITICAL>=90, HIGH>=70, MEDIUM>=40, LOW<40. Urgency text encodes patch SLA (immediate when KEV; 24h/72h/30d by label). Use to triage a single CVE without orchestrating cve_lookup + exploit_lookup separately. PoC signal here is the local ExploitDB mirror only — for full multi-source exploit detail (GitHub Advisory + Shodan refs + ExploitDB), call exploit_lookup separately. Methodology adapted from mukul975/cve-mcp-server (Apache-2.0): https://github.com/mukul975/cve-mcp-server. Free: 30/hr, Pro: 500/hr. Returns {cve_id, score (0-100), label (CRITICAL/HIGH/MEDIUM/LOW), urgency, has_public_poc, components (cvss_v3, epss_score, in_kev, has_public_poc, weighted_breakdown), boosters_applied, recommendation, summary, verdict, next_calls}.

  ConnectorNo auth

• verify_recipeA

  Guardian EngineA

  Verify a candidate recipe against a Guardian master recipe. Uses deterministic graph-based verification to check technique, temperature, timing, cooking medium, and required ingredients. **Verdict**: `verdict` is strictly PASSED or FAILED and is policy-driven — any CRITICAL finding fails the recipe; more than 5 WARNINGs also fail. There is no score in the response (ADR-013): gate on `verdict` and explain failures from `findings`. **Field audience**: `issue` is a machine-readable code for programmatic handling — never show it to end users. Use `title` and `suggested_correction` as the user-facing fields. Returns structured JSON by default (machine-actionable findings and patches); response_format="text" renders a human-readable report. Both formats are transparent (ADR-009 / ADR-018): exact values and ingredient names included.

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• get_masterA

  Guardian EngineA

  Return the canonical master recipe for a dish (read-only, no LLM). Enables compare-then-verify agentic loops: fetch the master, diff it against the user's recipe, then call verify_recipe — instead of verifying blind. Pure knowledge-base lookup, no LLM in the hot path. Master content is transparent by default (ADR-009 / ADR-010): exact temperatures, timings, and EU FIC 1169/2011 allergen codes are returned verbatim, never obfuscated. No score is included (ADR-013) — this is reference data, not a verdict. Returns ingredients, steps (technique/temperature/timing/medium), and the EU FIC allergens derived from the required ingredients. Unknown dishes return a structured UNKNOWN_DISH error.

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• get_token_approvalsA

  TeardropA

  Audit ERC-20 token allowances for a wallet address. By default, returns all non-zero approvals for assets in the shared tracked-asset registry across curated DeFi protocol spenders (Uniswap, Aave, Compound, 1inch, 0x, OpenSea). Flags unlimited approvals with risk levels: high=unknown spender, medium=trusted protocol, low=bounded amount. Use before swaps to verify approval state, or after security incidents to detect active exploit vectors. Ethereum mainnet and Base only.

  ConnectorNo auth

• niche_voice_profile_ingestA

  Niche — Editorial IntelligenceA

  Extract voice primitives (register / sentence rhythm / lexicon preferences / punctuation habits) from post-shaped text and persist onto the user's VoiceProfile. The voice primitives thread into content generation so generated copy matches the user's actual writing voice. Two input shapes: pass `posts` (list of pre-collected text snippets, ≥80 chars each) or pass `url` (the server scrapes post-shaped snippets from the page: Substack / Medium / blog / X profile). Inline posts win when both are given. Inline post-shaped snippets need to be the user's own writing, not press articles or marketing copy. Returns the extracted primitives + a diff of what changed on the stored VoiceProfile.

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• verify_supplier_frA

  eu-verifyA

  One-call due diligence on a French company by SIREN or SIRET: checksum, existence and status (SIRENE), insolvency proceedings (BODACC), VAT validity (VIES) and sanctions screening across EU/OFAC/UN/UK/FR lists, condensed into a risk verdict (low, medium, high or critical) with reasons and a 0-100 score. Query: siren=NNNNNNNNN or siret=NNNNNNNNNNNNNN. Sub-checks degrade gracefully; buyers are never charged on failure. Price: $0.05 USDC per call (x402).

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• GetEconomicCalendarA

  equiblesB

  Get the economic release calendar — scheduled (upcoming) and recent publication dates of US macro data releases, with the FRED series each release updates and an importance tier per release (High = the tier-1 scheduled market movers: CPI, PPI, Employment Situation, GDP, PCE, retail sales; Medium = other genuine scheduled prints; Low = daily rate/market levels like SOFR or VIX). FOMC meetings are NOT included — FRED's release feed has no real FOMC meeting dates; use the Federal Reserve's published meeting calendar for those. Defaults to the next 30 days. Use minImportance=high to see only the market movers, and GetEconomicIndicator to fetch a series' data after it prints.

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• seo_keyword_researchA

  mcp-knowledgeC

  SEO keyword research from a seed keyword or topic. Uses Google Suggest (public, keyless) to discover related queries at 2 expansion levels, then clusters them by intent: informational / commercial / transactional / navigational — via heuristic pattern matching. Search volume is bucketed (very_high / high / medium / low / very_low) and clearly labelled as ESTIMATED — no fabricated precise numbers. Returns all keywords, intent clusters, quality scores (0-100), and top 10 opportunities. Supports country (gl) and language (hl) targeting. 100% keyless. Cache TTL 6h. ICP: SEO managers, content strategists, SaaS founders, agency teams.

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