"A service or resource for finding people" matching MCP tools:

• list_investment_thesesA

  crashtestyourstrategyA

  Discover the investment-thesis catalog. Each entry is a descriptive case study that pairs an economic framework with a rule-based portfolio and the synthetic + historical stress evidence for that allocation. Returns one compact summary per thesis (slug, title, one-liner, tags, risk tiers, framework summary, headline finding). Call get_investment_thesis(slug) for the full framework / portfolio / stress evidence, or read the thesis://{slug} resource. Descriptive, not advisory — the agent decides what is suitable.

  ConnectorNo auth

• get_resourceA

  Plato's KallipolisA

  Retrieve one complete prepared resource profile, including evidence-backed facts and primary-source provenance, by its stable resource ID.

  ConnectorNo auth

• explain_findingA

  MalinoisA

  Returns the plain-language meaning and step-by-step fix for one Malinois finding. Use it while helping the user fix an issue reported by scan_app, or when they ask what a finding means. Pass the rule_id exactly as scan_app returned it. Read-only, no network, instant.

  ConnectorNo auth

• baltimore_layersA

  Data BaltimoreA

  List the layers of a Baltimore ArcGIS service (for discovery). Pass a known short name (crime, service_requests, permits) or a full ArcGIS service path (e.g. "311_Customer_Service_Requests_current/FeatureServer"). Omit `service` to list the known Baltimore services. Returns layer id + name to use with baltimore_query.

  ConnectorNo auth

• awsinspect_batchA

  InsideOut (Riley)A

  BATCH INSPECTION: run up to 32 AWS inspect probes in one call. ⚠️ **PREREQUISITE**: Same as awsinspect — deploy attempt required. Check convostatus for hasDeployAttempt=true before calling. Use this when you need to check more than ~3 resources. The backend fetches Oracle credentials ONCE per batch and fans out probes against a single AWS config — for a 12-resource health check this is ~5–8× faster and 12× fewer Oracle round-trips than calling awsinspect 12 times. BUDGETS: - Up to 32 sub-probes per call (subs array length). - 30s per-sub timeout; 60s total batch wall-clock. - Concurrency cap 8 — sub-probes run in parallel but never saturate AWS. - 512 KB response cap: subs past the cap keep their envelope (index/service/action/ok) but have result replaced with truncated=true. PARTIAL FAILURE IS EXPECTED. The response is an ordered results array; each entry has {index, service, action, ok, result, error}. Inspect each result — do NOT abort on the first error. A credential fetch failure leaves cred-less probes (list-actions, list-metrics) succeeding anyway. REQUIRES: session_id from convoopen response (format: sess_v2_...). Supported services: account, acm, alb, apigateway, apprunner, backup, bedrock, cloudfront, cloudwatchlogs, cognito, cost-explorer, dynamodb, ebs, ec2, ecs, eks, elasticache, kms, lambda, msk, opensearch, rds, route53, s3, sagemaker, secretsmanager, sqs, vpc, waf For a specific service's actions, use awsinspect (singular) with action="list-actions" — batch is not the place for discovery. Batch responses are always summarized (no detail/raw per-sub); use singular awsinspect when you need full metadata or raw API output for one resource. EXAMPLES: - awsinspect_batch(session_id=..., subs=[ {"service":"ec2","action":"describe-instances"}, {"service":"rds","action":"describe-db-instances"}, {"service":"vpc","action":"describe-vpcs"}, {"service":"s3","action":"list-buckets"}]) - awsinspect_batch(session_id=..., subs=[ {"service":"ec2","action":"get-metrics","filters":"{\"hours\":6}"}, {"service":"rds","action":"get-metrics","filters":"{\"hours\":6}"}])

  ConnectorNo auth

• gcpinspect_batchA

  InsideOut (Riley)A

  BATCH INSPECTION: run up to 32 GCP inspect probes in one call. ⚠️ **PREREQUISITE**: Same as gcpinspect — deploy attempt required. Check convostatus for hasDeployAttempt=true before calling. Use this when you need to check more than ~3 resources. The backend fetches Oracle credentials ONCE per batch and fans out probes against a single GCP credentials blob — a 12-resource health check is ~5–8× faster and 12× fewer Oracle round-trips than calling gcpinspect 12 times. BUDGETS: - Up to 32 sub-probes per call (subs array length). - 30s per-sub timeout; 60s total batch wall-clock. - Concurrency cap 8. - 512 KB response cap: subs past the cap keep their envelope (index/service/action/ok) but have result replaced with truncated=true. PARTIAL FAILURE IS EXPECTED. The response is an ordered results array; each entry has {index, service, action, ok, result, error}. Inspect each result — do NOT abort on the first error. A credential fetch failure leaves cred-less probes (list-actions, list-metrics) succeeding anyway. REQUIRES: session_id from convoopen response (format: sess_v2_...). Supported services: apigateway, bastion, billing, certificatemanager, cloudarmor, cloudbuild, cloudcdn, clouddeploy, clouddns, cloudfunctions, cloudkms, cloudlogging, cloudmonitoring, cloudrun, cloudsql, compute, firestore, gcs, gke, iam, identityplatform, loadbalancer, memorystore, pubsub, secretmanager, vertexai, vpc For a specific service's actions, use gcpinspect (singular) with action="list-actions" — batch is not the place for discovery. Batch responses are always summarized (no detail/raw per-sub); use singular gcpinspect when you need full metadata or raw API output for one resource. EXAMPLES: - gcpinspect_batch(session_id=..., subs=[ {"service":"compute","action":"list-instances"}, {"service":"gke","action":"list-clusters"}, {"service":"cloudsql","action":"list-instances"}]) - gcpinspect_batch(session_id=..., subs=[ {"service":"compute","action":"get-metrics","filters":"{\"hours\":6}"}, {"service":"cloudrun","action":"get-metrics","filters":"{\"hours\":6}"}])

  ConnectorNo auth

• query_power_interconnection_queue_isone_v1A

  exascale.buildA

  Query the ISO-NE generator interconnection queue — ISO New England's public IRTT "public queue" report, the waiting line of projects that have REQUESTED to connect to the New England grid (CT, MA, ME, NH, RI, VT). Returns cited, project-level records with ISO-NE's full published structure (all 31 columns of the rendered report): the three megawatt readings kept SEPARATE (`net_mw`, `summer_mw` = max summer output, `winter_mw` = max winter output — `net_mw` is 0 for the many Capacity-Network-Resource-only requests, a real value, not missing), the request `request_type` (G = Generation / ETU = Elective Transmission Upgrade / TS = Transmission Service), the space-delimited multi-value `fuel_type` (EIA energy-source codes, e.g. "SUN BAT"), the `unit_type` and service code `serv` (CNR = Capacity Network Resource / NR = Network Resource), the `jurisdiction` (F = FERC / N = Non-FERC), the ISO-NE load `zone`, the per-stage study statuses (`fs_status` … `ia_status`) with their study-document links, location (`state`, `county`, derived `county_fips`, `poi`), and lifecycle dates. Group or filter by `application_status`, `project_status`, `request_type`, `unit_type`, `fuel_type`, `serv`, `jurisdiction`, `zone`, `state`, `county_fips`, or `cluster`; filter `requested_date` by the `requested_date_from` / `requested_date_to` range. Pass each parameter as a top-level key of `params` (flat — not nested). Example: `{"application_status": "A", "request_type": "G", "state": "MA"}` for active generation requests in Massachusetts; `{"group_by": ["application_status"]}` for requested MW and project counts by status. Returns JSON aggregates with citations and optional row-level records when `include_records` is true; every value carries `source`, `as_of`, and a `source_row` verifiable with get_source_evidence_v1. `net_mw` / `summer_mw` / `winter_mw` are REQUESTED capacity, not built: historically the large majority of queued megawatts withdraw before they are built, so the queue is withdrawn-dominated. NEVER read a queue-MW total as installed or operating capacity — it is additive across distinct rows but is a REQUESTED total only. ISO-NE publishes net, summer-peak and winter-peak MW separately — served under ISO-NE's own labels and never blended into one nameplate. Scope by `application_status`: `A` = Active (the live pipeline), `C` = Commercial (built and in service — the built reading), `W` = Withdrawn. ISO-NE ALSO carries a separate build-progress `project_status` (Under Study / Under Construction / In Service / …) — kept distinct from `application_status`, never collapsed. For built/operating capacity use query_power_capacity_v1. ISO-NE only — never summed, deduped, or compared across ISOs. For the MISO interconnection queue use query_power_interconnection_queue_v1; for PJM use query_power_interconnection_queue_pjm_v1 (or query_power_interconnection_queue_pjm_cycle_v1 for PJM's cluster/cycle grid); for the CAISO (California) queue use query_power_interconnection_queue_caiso_v1; for the NYISO (New York, incl. load interconnection requests) queue use query_power_interconnection_queue_nyiso_v1; for the ERCOT (Texas) queue use query_power_interconnection_queue_ercot_v1; for the SPP (central US) queue use query_power_interconnection_queue_spp_v1. ISO-NE publishes no data-center / load request type (all rows are generation or transmission), and this tool does not infer one — that interpretation is the analyst's, from cited rows.

  ConnectorNo auth

• getInstagramPostInteractingUsersA

  Social Media Search API — Twitter, Instagram, Reddit, TikTok (XPOZ)A

  Get USER PROFILES of people who interacted with an Instagram post. Returns full user data (bio, followerCount, followingCount, etc.). RETURNS USER PROFILES: id, username, fullName, biography, followerCount, followingCount, isVerified, profilePicUrl. Use for analyzing WHO engaged with a post. NOT FOR COMMENT TEXT: To read the actual comment content (what people wrote), use getInstagramCommentsByPostId instead. INTERACTION TYPES: "commenters" (users who commented), "likers" (users who liked). WHEN TO USE THIS TOOL: Analyzing commenters/likers demographics, finding influencers who engaged, building audience profiles, network analysis of who interacts with posts. WHEN TO USE getInstagramCommentsByPostId: Reading comment text, sentiment analysis of what was said, analyzing discussion content. FAST (default, omit responseType or responseType="fast"): Returns up to 300 results directly (use limit param to reduce, e.g. limit=5). Auto API fallback for commenters when stale. PAGING (responseType="paging"): Async paginated results (1000 users per page with default fields), returns operationId - IMMEDIATELY call checkOperationStatus to get results. CSV export included via dataDumpExportOperationId. Supports pageNumber/tableName for subsequent pages. Optional fields (default: ["id", "username", "fullName"]). Available: biography, isPrivate, isVerified, followerCount, followingCount, mediaCount, profilePicUrl. This is a safe, read-only tool for analyzing searchable information.

  ConnectorOAuth

• awsinspect_batchA

  InsideOut (Riley)A

  BATCH INSPECTION: run up to 32 AWS inspect probes in one call. ⚠️ **PREREQUISITE**: Same as awsinspect — deploy attempt required. Check convostatus for hasDeployAttempt=true before calling. Use this when you need to check more than ~3 resources. The backend fetches Oracle credentials ONCE per batch and fans out probes against a single AWS config — for a 12-resource health check this is ~5–8× faster and 12× fewer Oracle round-trips than calling awsinspect 12 times. BUDGETS: - Up to 32 sub-probes per call (subs array length). - 30s per-sub timeout; 60s total batch wall-clock. - Concurrency cap 8 — sub-probes run in parallel but never saturate AWS. - 512 KB response cap: subs past the cap keep their envelope (index/service/action/ok) but have result replaced with truncated=true. PARTIAL FAILURE IS EXPECTED. The response is an ordered results array; each entry has {index, service, action, ok, result, error}. Inspect each result — do NOT abort on the first error. A credential fetch failure leaves cred-less probes (list-actions, list-metrics) succeeding anyway. REQUIRES: session_id from convoopen response (format: sess_v2_...). Supported services: account, acm, alb, apigateway, apprunner, backup, bedrock, cloudfront, cloudwatchlogs, cognito, cost-explorer, dynamodb, ebs, ec2, ecs, eks, elasticache, kms, lambda, msk, opensearch, rds, route53, s3, sagemaker, secretsmanager, sqs, vpc, waf For a specific service's actions, use awsinspect (singular) with action="list-actions" — batch is not the place for discovery. Batch responses are always summarized (no detail/raw per-sub); use singular awsinspect when you need full metadata or raw API output for one resource. EXAMPLES: - awsinspect_batch(session_id=..., subs=[ {"service":"ec2","action":"describe-instances"}, {"service":"rds","action":"describe-db-instances"}, {"service":"vpc","action":"describe-vpcs"}, {"service":"s3","action":"list-buckets"}]) - awsinspect_batch(session_id=..., subs=[ {"service":"ec2","action":"get-metrics","filters":"{\"hours\":6}"}, {"service":"rds","action":"get-metrics","filters":"{\"hours\":6}"}])

  ConnectorNo auth

• gcpinspect_batchA

  InsideOut (Riley)A

  BATCH INSPECTION: run up to 32 GCP inspect probes in one call. ⚠️ **PREREQUISITE**: Same as gcpinspect — deploy attempt required. Check convostatus for hasDeployAttempt=true before calling. Use this when you need to check more than ~3 resources. The backend fetches Oracle credentials ONCE per batch and fans out probes against a single GCP credentials blob — a 12-resource health check is ~5–8× faster and 12× fewer Oracle round-trips than calling gcpinspect 12 times. BUDGETS: - Up to 32 sub-probes per call (subs array length). - 30s per-sub timeout; 60s total batch wall-clock. - Concurrency cap 8. - 512 KB response cap: subs past the cap keep their envelope (index/service/action/ok) but have result replaced with truncated=true. PARTIAL FAILURE IS EXPECTED. The response is an ordered results array; each entry has {index, service, action, ok, result, error}. Inspect each result — do NOT abort on the first error. A credential fetch failure leaves cred-less probes (list-actions, list-metrics) succeeding anyway. REQUIRES: session_id from convoopen response (format: sess_v2_...). Supported services: apigateway, bastion, billing, certificatemanager, cloudarmor, cloudbuild, cloudcdn, clouddeploy, clouddns, cloudfunctions, cloudkms, cloudlogging, cloudmonitoring, cloudrun, cloudsql, compute, firestore, gcs, gke, iam, identityplatform, loadbalancer, memorystore, pubsub, secretmanager, vertexai, vpc For a specific service's actions, use gcpinspect (singular) with action="list-actions" — batch is not the place for discovery. Batch responses are always summarized (no detail/raw per-sub); use singular gcpinspect when you need full metadata or raw API output for one resource. EXAMPLES: - gcpinspect_batch(session_id=..., subs=[ {"service":"compute","action":"list-instances"}, {"service":"gke","action":"list-clusters"}, {"service":"cloudsql","action":"list-instances"}]) - gcpinspect_batch(session_id=..., subs=[ {"service":"compute","action":"get-metrics","filters":"{\"hours\":6}"}, {"service":"cloudrun","action":"get-metrics","filters":"{\"hours\":6}"}])

  ConnectorNo auth

• create_userA

  mcpA

  Create a database user for a Cloud SQL instance. * This tool returns a long-running operation. Use the `get_operation` tool to poll its status until the operation completes. * When you use the `create_user` tool, specify the type of user: `CLOUD_IAM_USER`, `CLOUD_IAM_SERVICE_ACCOUNT`, or `BUILT_IN`. * By default the newly created user is assigned the `cloudsqlsuperuser` role, unless you specify other database roles explicitly in the request. * You can use a newly created user with the `execute_sql` tool if the user is a currently logged in IAM user. The `execute_sql` tool executes the SQL statements using the privileges of the database user logged in using IAM database authentication. The `create_user` tool has the following limitations: * To create a built-in user with password, use the `password_secret_version` field to provide password using the Google Cloud Secret Manager. The value of `password_secret_version` should be the resource name of the secret version, like `projects/12345/locations/us-central1/secrets/my-password-secret/versions/1` or `projects/12345/locations/us-central1/secrets/my-password-secret/versions/latest`. The caller needs to have `secretmanager.secretVersions.access` permission on the secret version. * The `create_user` tool doesn't support creating a user for SQL Server. To create an IAM user in PostgreSQL: * The database username must be the IAM user's email address and all lowercase. For example, to create user for PostgreSQL IAM user `example-user@example.com`, you can use the following request: ``` { "name": "example-user@example.com", "type": "CLOUD_IAM_USER", "instance":"test-instance", "project": "test-project" } ``` The created database username for the IAM user is `example-user@example.com`. To create an IAM service account in PostgreSQL: * The database username must be created without the `.gserviceaccount.com` suffix even though the full email address for the account is`service-account-name@project-id.iam.gserviceaccount.com`. For example, to create an IAM service account for PostgreSQL you can use the following request format: ``` { "name": "test@test-project.iam", "type": "CLOUD_IAM_SERVICE_ACCOUNT", "instance": "test-instance", "project": "test-project" } ``` The created database username for the IAM service account is `test@test-project.iam`. To create an IAM user or IAM service account in MySQL: * When Cloud SQL for MySQL stores a username, it truncates the @ and the domain name from the user or service account's email address. For example, `example-user@example.com` becomes `example-user`. * For this reason, you can't add two IAM users or service accounts with the same username but different domain names to the same Cloud SQL instance. * For example, to create user for the MySQL IAM user `example-user@example.com`, use the following request: ``` { "name": "example-user@example.com", "type": "CLOUD_IAM_USER", "instance": "test-instance", "project": "test-project" } ``` The created database username for the IAM user is `example-user`. * For example, to create the MySQL IAM service account `service-account-name@project-id.iam.gserviceaccount.com`, use the following request: ``` { "name": "service-account-name@project-id.iam.gserviceaccount.com", "type": "CLOUD_IAM_SERVICE_ACCOUNT", "instance": "test-instance", "project": "test-project" } ``` The created database username for the IAM service account is `service-account-name`.

  ConnectorNo auth

• random_usersA

  Synthetic Data — Random UserA

  Generate synthetic / fake user profiles via the MwVideos random_users API (POST /mwvideos/api/random_users). Returns name, gender, location, email, login, dob, phone, picture, and nat. Use when the user asks for random users, fake people, sample contacts, demo personas, UI fixtures, or test profiles. Pass `results` for how many profiles (default 1, minimum 1) and `isPro` as 0 or 1 when relevant (default 0). Authenticated user_id is injected server-side — do not invent profiles; always call this tool. These are FAKE people for demos and testing, not real PII.

  ConnectorOAuth

• create_playlistA

  OffStereoA

  Create one playlist from a natural-language idea or explicit track list. Call exactly once per listener turn: do not inspect the result and make a corrective second call or stack another playlist card in the same response. previewOnly=true returns one authoritative playlist that can be heard before saving and needs no music-service connection. After explicit approval, previewOnly=false with the approved tracks and the same concise editorial name creates one private playlist in the listener's active music service. Pass service only when the listener explicitly names Spotify or Apple Music. requiredArtists carries every artist the listener named as required. allowSecondPlaylist is true only for an explicit request for a separate additional playlist. Sign-in recovery preserves the exact request and pendingActionId for a safe retry.

  ConnectorNo auth

• send_feedbackA

  agtrepo-memoryB

  Free. Sends a message to the human operators of this service: a bug report, a problem with these tools or the documentation, or a suggestion. It is read by people, not processed by software, and nothing is returned beyond an acknowledgement. Do NOT use it for status updates, progress notes, task narration, error logs, or as a fallback when another tool is unavailable or a payment cannot be made -- it cannot complete, retry or work around any task. Name is optional (omit for anonymous feedback); message is limited to 1000 characters.

  ConnectorNo auth

• siteborne_get_quoteA

  SITEBORNE Utility NetworkA

  Build a canonical x402 payment quote for one SITEBORNE service and exact request input. Use when: an agent needs the governed price, payee, network, asset, resource, expiry, and payment requirement before deciding whether to invoke a paid service. Do not use when: evidence work is required now (use the matching siteborne_build_company_evidence_graph, siteborne_retrieve_verified_web_context, siteborne_extract_document_evidence_json, or siteborne_verify_agent_output tool), or only availability is needed (use siteborne_get_service_health). Parameters: scheme must be the one scheme the service is offered under (company_evidence_graph.v2 exact, web_context_verified.v2 exact, document_evidence_json.v2 upto, verify_agent_output.v2 exact, company_evidence_graph.v3 exact, web_context_verified.v3 exact, document_evidence_json.v3 upto, verify_agent_output.v3 exact); any other scheme is rejected with scheme_not_offered. input is hashed, so the quote binds only that exact request. Behavior: quote-only and read-only; it hashes the proposed input, and does not execute the underlying paid service, verify payment, call a provider, create a Workflow, or settle. Economics: free of charge. The returned amount is the exact price, or for upto an authorized maximum whose actual_amount is null because the real charge is measured at settlement and never exceeds it. Failure: a mode that has a governed price but is unavailable returns retrieval_mode_unavailable or verification_mode_unavailable and no quote; a quote expires at expires_at. Returns: an expiring input-bound quote with its x402 payment requirement and the canonical economics block.

  ConnectorNo auth

• get_utility_service_statusA

  Ardaro Agent Runtime UtilitiesA

  Check the selected service's current runtime readiness, input limits, pricing and retention before use. Returns service and details; ready=false means the underlying service is unavailable. Free cached runtime read; no external probe, payment, analysis or historical document retrieval. Use get_utility_example for a fixed integration sample instead.

  ConnectorNo auth

• add_lead_finder_prospectsA

  EmailchaserA

  SPENDS CREDITS, AND BY DEFAULT METERED EMAIL VERIFICATION. Adds people from the Lead Finder contact database to a campaign as leads, revealing their contact details, in the background. Pass refs (from search_lead_finder results; up to 1,000 different people by default) to add exactly those people, or pass filters and count (1 to 5,000 by default) to add count people matching the filters who are not in the workspace yet. A filters add always walks the matches from the top and skips anyone already a lead, free and not counted, so repeating the same add adds the NEXT count people and charges again. Never repeat an add to retry: after an error or a timeout, call list_lead_finder_imports to see whether it started. A filters add stops early when the audience runs out or once it has looked at five people for every one requested (at least 1,000). People already added count toward that limit even though skipping them is free, so after an add of more than about 1,000 people a small follow-up can stop with nobody added. Every person a filters add looks at, added or skipped, also uses the account's daily allowance for filters adds (25,000 people by default). Each person added costs 1 credit ($0.033 at list), reported as creditsPerProspect; the response reports estimatedCredits, the most the add can cost in credits, and the wallet must hold that much for it to start. People already in the workspace, blocklisted, without a usable address, on a personal mailbox when the campaign only takes business addresses, or marked invalid by verification are skipped and cost no credits. verifyEmails (on by default) checks every screened address with the paid email verification waterfall, including the ones it marks invalid: one or two checks per address, billed as metered usage on the next invoice (price per check: GET /email-verification/rates in the REST API), within the account's monthly verification spend limit. Catch-all and unconfirmed addresses are added and charged, and so is every address once that limit is reached; verification.limitReached and verification.unknown on get_lead_finder_import count them. Leads added to a running or paused campaign get their emails at once (a paused campaign sends them when resumed); adding to a completed campaign resumes it, so it starts sending again; in a draft campaign the emails are created at launch. At most 3 adds run at once in a workspace. A refusal names its reason, and a 429 also says when to retry (reason running_imports, daily_import_limit, daily_budget, monthly_budget or fair_use_floor). Poll get_lead_finder_import with the importId until finishedAt is set. Check get_credit_balance first; buy credits with purchase_credits.

  ConnectorNo auth

• classify_segment_groupA

  SliqB

  <summary>Kick off classification of the group's in-scope canonical people, in the background. The scope is the group's criteria (campaigns and teammates). People in scope are tagged automatically within about 15 minutes of being looked up or contacted, so call this right after creating a group to tag the people already there now, or with `force=True` to re-classify everyone (a re-tag after editing the group or its tags). Without `force`, only people not yet classified for this group are processed. Results are not immediate — read them afterward with get_segment_rates / query_segment_people. Classification costs credits (a flat per-person rate). The return carries the count of people the run will process and the credits it will charge (exact — the same population the run bills), so you can tell the user the cost; a run never charges more than the balance. If the balance can't cover the estimate the run is not enqueued — the return says so, and you should relay it.</summary> <returns> <description>Dict with the in-scope person count and estimated credit cost, plus whether the run was enqueued or blocked for insufficient credits. Enqueued -> {"enqueued": true, "group_id", "person_count", "estimated_credits"}. Blocked -> {"enqueued": false, "reason": "insufficient_credits", "group_id", "person_count", "estimated_credits", "balance"}.</description> </returns>

  ConnectorOAuth

• get_pricingA

  WattCoinA

  WattCoin: How to pay for WATT services: live per-service prices (WATT only), the payment wallet, the pay-per-use flow, the free scrape tier and whether the LLM service is currently available. Use before web_scrape or before paying for anything; use get_watt_token_info for market data. No auth needed; read-only and live. Returns JSON.

  ConnectorNo auth

• tiktok_search_videosA

  Manifold MCPA

  TikTok videos matching a keyword, ranked by the platform's own search. Use when: "what is TikTok saying about X", finding creators in a category, watching a product or a brand name. Not for: one account's own videos, use tiktok_get_videos. A complete window of everything posted (TikTok search is ranked and never complete). Returns: List, ranked. rows[]: videos with author and author_followers, created_at, text (cut at 2000 characters), likes, comments, shares, saves, views, media, duration_s, links, transcript and top_comments[]. saves is how many people saved it, music the sound, and author_followers sizes each creator without another call. null or empty = the platform does not publish it. Cost: 1 credit per page. Cached 6 hours. Example: {"query": "creatine gummies", "since": "month", "sort": "popular"}

  ConnectorAPI key

• search_lacunaA

  Lacuna Research MCPA

  Search Lacuna's ML/AI corpus for papers, research directions, authors, venues, institutions, novel research hypotheses, and research resources (code repositories, datasets, models, and demos linked to papers). For novel ML/AI research ideas, use search_type="hypothesis", then get_hypothesis on promising results. search_type accepts all, cluster, paper, author, institution, venue, hypothesis, or resource. Use paper for literature and get_paper to read a result. Use other sources for biographies, news, and non-research web content. Resources: search_type="resource" searches GitHub repositories, Hugging Face datasets/models, and Zenodo records that Lacuna has linked to at least one paper. To find benchmark or evaluation datasets, use search_type="resource" with resource_kind="dataset" (for example query "image classification benchmark" or a dataset name like "ImageNet"). resource_kind accepts codebase, dataset, model, or demo (one value or a list); provider accepts github, huggingface, or zenodo. Both filters require search_type "resource" or "all" (with either filter set, only resources are returned). Each result has an external `url`, a Lacuna `context_url`, and `linked_paper_count`; call get_resource on its id to list the linked papers (with their relationship to the resource), then get_paper on those papers for reported results and numbers. Resource search does not support date_from/date_to, venue, year sorting, or semantic ranking. ranking_profile accepts: - default / lexical (default): production ranking; relevance-sorted paper searches combine lexical and semantic retrieval when fields is unset. - semantic: conceptual paper retrieval; supported for paper and all. - bm25_title_abstract / bm25: lexical paper matching over those fields. sort accepts relevance (default), year_desc, or year_asc. Semantic ranking cannot use year sorting; constrain recency with date_from/date_to instead. date_from and date_to are inclusive YYYY, YYYY-MM, or YYYY-MM-DD bounds. author_id_or_url constrains a paper search to one author. Pass an author ID or Lacuna author page URL; it requires search_type="paper". fields optionally restricts and weights lexical fields, for example "title^4,abstract". Supported names are title, abstract, summary, concepts, name, top_names, and venue, plus description, topics, provider_key, and paper_titles for resources (a resource's title already includes its description, topics, README text, and linked paper titles). Fields must exist on the selected search_type, weights must be within 0 < weight <= 100, and fields cannot be combined with semantic ranking.

  ConnectorNo auth