"Drone" matching MCP tools:

• johto-dot-data--faa-drone-dataA

  johto.data FAA Drone DataA

  This tool calls the Actor "johto.data/faa-drone-data" and retrieves its output results. Actor description: FAA drone data for AI agents: active Part 107 waivers (company, waiver number, waived rules like BVLOS 107.31, over people 107.39, night, altitude, dates) plus FRIA Remote ID flying sites with location. Filter by rule, keyword, state, date. $0.02/record. This tool requires an x402 payment. Include a valid x402 payment signature in the request metadata (_meta["x402/payment"]). Your MCP client must support the x402 payment protocol.

  Connector

  Destructive

  No auth

• goal-read-evidence-contentA

  Planner MCP — provable "done" for AI agentsA

  Read attached evidence through MCP without changing its format. Pass evidenceId from goal-get. Text (text/plain, JSON, NDJSON) returns newline-preserving redacted lines; offset/limit are zero-based line pagination. Images return a native MCP image with original MIME and bytes. For a complete video, PDF, ZIP or other binary, binaryDelivery=link (default) returns a resource_link to the authenticated original download — no base64 and no conversion. If the client cannot open external links, binaryDelivery=base64 embeds the exact original bytes in a resource blob; this expands data by about 33% plus JSON overhead and can consume the client context window, so use it only when needed. byteOffset/byteLimit require binaryDelivery=base64. Text secrets are redacted; binary evidence is unmodified.

  ConnectorNo auth

• goal-read-evidence-contentA

  Planner (proof-of-done)A

  Read attached evidence through MCP without changing its format. Pass evidenceId from goal-get. Text (text/plain, JSON, NDJSON) returns newline-preserving redacted lines; offset/limit are zero-based line pagination. Images return a native MCP image with original MIME and bytes. For a complete video, PDF, ZIP or other binary, binaryDelivery=link (default) returns a resource_link to the authenticated original download — no base64 and no conversion. If the client cannot open external links, binaryDelivery=base64 embeds the exact original bytes in a resource blob; this expands data by about 33% plus JSON overhead and can consume the client context window, so use it only when needed. byteOffset/byteLimit require binaryDelivery=base64. Text secrets are redacted; binary evidence is unmodified.

  ConnectorNo auth

• goal-add-criterionA

  Planner (proof-of-done)A

  Append an acceptance criterion to a goal. The text must describe an observable check over an artifact (e.g. "GET /api/health returns 200 with {status:ok}"), not a subjective approval. Each criterion has a class: pre-merge (default — proved in CI / by attached evidence) or post-deploy (proved by an executable probe against the deployed prod instance). A post-deploy criterion MUST carry probeSpec {method, url, expect:{http_code, body:{field: expectedValue}}} — the request the runner sends and the answer it must get; without it the call is rejected with error=probe_required. Passing probeSpec alone implies probeClass=post-deploy. Set visualEvidenceSuggested=true only when adopting visualAcSuggestion from goal-create, goal-get, or the ready_for_work advisory returned by goal-update; it remains an ordinary AC. Grove mode: AC (class and probe included) can only be added while goal is in backlog, except accepting a visual advisory in ready_for_work: that starts a direct checking_ac recheck, without intermediate backlog. Other edits are frozen once started; quality linter blocks high-severity issues. Standard mode: AC editable until goal is closed, linter is advisory. Returns criterion id, position, text, probeClass, probeSpec and any quality findings.

  ConnectorNo auth

• goal-attach-fileA

  Planner (proof-of-done)A

  Attach a small file (mockup, diagram, screenshot, PDF) to a GOAL as a description attachment — NOT evidence: it is not bound to any acceptance criterion, is never judged and never counts toward closing the goal. Returns {attachment.url, attachment.markdown}; paste `markdown` into the goal description (goal-update description) and the web UI renders images inline. contentBase64 is reliable only for payloads ≤ ~1 KB (long string arguments get truncated on the agent side before the request is sent). For anything bigger call goal-request-upload with goalId and stream the file with a single PUT — same result shape, no base64.

  ConnectorNo auth

• commentA

  Done BearA

  Write on a task's comment thread. `action: "add"` posts a comment, attributed to the connected user and visible to everyone in the workspace, so write it as a message to the user's teammates rather than a note to the user; nobody is notified. `action: "edit"` rewrites one of your own (only its author may, and it is then marked edited). `action: "remove"` deletes one permanently — comments have no Trash — so confirm with the user first. Read the thread with task_show and `include: ["comments"]`.

  Connector

  Destructive

  No auth

• commentA

  Done Bear MCPA

  Write on a task's comment thread. `action: "add"` posts a comment, attributed to the connected user and visible to everyone in the workspace, so write it as a message to the user's teammates rather than a note to the user; nobody is notified. `action: "edit"` rewrites one of your own (only its author may, and it is then marked edited). `action: "remove"` deletes one permanently — comments have no Trash — so confirm with the user first. Read the thread with task_show and `include: ["comments"]`.

  Connector

  Destructive

  No auth

• goal-add-criterionA

  Planner MCP — provable "done" for AI agentsA

  Append an acceptance criterion to a goal. The text must describe an observable check over an artifact (e.g. "GET /api/health returns 200 with {status:ok}"), not a subjective approval. Each criterion has a class: pre-merge (default — proved in CI / by attached evidence) or post-deploy (proved by an executable probe against the deployed prod instance). A post-deploy criterion MUST carry probeSpec {method, url, expect:{http_code, body:{field: expectedValue}}} — the request the runner sends and the answer it must get; without it the call is rejected with error=probe_required. Passing probeSpec alone implies probeClass=post-deploy. Set visualEvidenceSuggested=true only when adopting visualAcSuggestion from goal-create, goal-get, or the ready_for_work advisory returned by goal-update; it remains an ordinary AC. Grove mode: AC (class and probe included) can only be added while goal is in backlog, except accepting a visual advisory in ready_for_work: that starts a direct checking_ac recheck, without intermediate backlog. Other edits are frozen once started; quality linter blocks high-severity issues. Standard mode: AC editable until goal is closed, linter is advisory. Returns criterion id, position, text, probeClass, probeSpec and any quality findings.

  ConnectorNo auth

• goal-attach-fileA

  Planner MCP — provable "done" for AI agentsA

  Attach a small file (mockup, diagram, screenshot, PDF) to a GOAL as a description attachment — NOT evidence: it is not bound to any acceptance criterion, is never judged and never counts toward closing the goal. Returns {attachment.url, attachment.markdown}; paste `markdown` into the goal description (goal-update description) and the web UI renders images inline. contentBase64 is reliable only for payloads ≤ ~1 KB (long string arguments get truncated on the agent side before the request is sent). For anything bigger call goal-request-upload with goalId and stream the file with a single PUT — same result shape, no base64.

  ConnectorNo auth

• kirk_demo_uavA

  Kirk — Unsupervised Structural Change DetectionA

  Runs a curated demonstration of Kirk on a UAV example. Zero arguments. Returns real Kirk output against the same sealed engine that customer callers hit. Free, rate-limited. First-time users: call this to see what Kirk does before signing up. Purpose: Score n=30 jittered 50-element spectra per acoustic class (drone / bird / helicopter) through the sealed engine and surface per-class score-distribution statistics plus z-separations for the three class pairs. Demonstrates that the same sealed engine sha handles market microstructure and acoustic spectra with the same primitive. Use when: You want to see Kirk's cross-domain generalization without needing your own audio dataset. Do not use when: You have real feature vectors to score — use ``kirk_infer_legacy`` directly (arg: list of 50 floats). This tool's inputs are fixed synthetic spectra baked into the demo. Capability class(es): Demonstrates domain-agnostic mathematical primitive — the same engine sha handles kirk_score_book (L2) and kirk_infer_legacy (arbitrary 50-vector). Path fit: MCP demonstration surface only. Cost: 0 IU. Rate-limited 3/hour per IP. Returns: Dict with per-class ``drone`` / ``bird`` / ``helicopter`` blocks (each: ``mean``, ``sd``, ``n``, ``kirk_version``), ``z_separation`` (dict of drone_vs_bird / drone_vs_helicopter / bird_vs_helicopter in pooled-sd units), ``representative_scores`` (the three single-sample scores from the canonical un-jittered spectra), ``interpretation_hint``, ``provenance``, and ``synthetic_spectral`` flag.

  ConnectorNo auth

• list_coverageA

  Ansvar: EU Compliance & Legal IntelligenceA

  Show which countries, frameworks, and legal domains are available. Use this BEFORE calling `search` when the user's topic doesn't name a jurisdiction (e.g., 'what does the law say about consumer protection'); then present the returned jurisdictions to the user or ask which applies. Examples: • 'Which countries do you cover?' → list_coverage() • 'Do you have German law?' → list_coverage(jurisdiction='DE') • 'What jurisdictions for NIS2?' → list_coverage(domain='cybersecurity') • 'Which countries have drone law?' → list_coverage(domain='aviation') (also accepts 'drone' / 'uas') • 'Which countries have court decisions?' → list_coverage(content_kind='court-decisions') Returns a `jurisdictions` array (each with `code`, `name`, `region`, `laws`, `provisions`, `domains`, `content_kinds`, `counts_complete`, and `uncounted_sources`) plus framework and source listings. An incomplete-count row also carries `coverage_note` explaining why. NOTE: `laws`/`provisions` are WHOLE-JURISDICTION corpus totals — the response's `count_scope` is `whole_jurisdiction`. Under a `domain` filter the jurisdiction list is narrowed to that domain but the counts are NOT domain-scoped; do not report them as a per-domain count. The domain-specific signal is the (domain-filtered) `sources`/`frameworks`. Search in the corpus's primary language (en), then fetch the hits in the user's language with the `language` argument of get_provision (a code, or a short list of codes for versions side by side).

  ConnectorOAuth

• list_workflow_typesA

  Ansvar: EU Compliance & Legal IntelligenceA

  Find the authoritative live structured-workflow registry. Call this before start_workflow instead of guessing an id from examples or a static catalog. It covers every deployed workflow family, including risk/TARA, DPIA/FRIA, regulatory and medical-device gap analysis, procurement, document review, drone/OT workflows, and vulnerability decisions. Each entry carries workflow_type, base_type, display_name, description, produces (the final deliverable), required_slots, overridable_configurable, legal-review state, plus gateway-added minimum_tier and available_to_caller fields. Every tier sees the full directory; which rows are marked available depends on the caller — free and solo the seven included rows (threat_model, gap_analysis with its NIS2, DORA, CRA and AI Act variants, dpia), Premium the interview-grounded rows (adding LINDDUN, TARA, FRIA, the jurisdictional DPIA and gap variants, SORA, drone/OT, machinery conformity, enterprise risk), Team and Company every live row. A row locked by TIER carries tier_caveat: which tier runs it, what that tier adds, and the upgrade URL — so a tier-locked row is never a bare false. Discovery service failures and malformed responses fail explicitly; no static list is returned as if it were live.

  ConnectorOAuth

• task_listA

  Done BearA

  List tasks in the workspace, most recently updated first. Completed tasks are ordered by completion time, newest first. Filter by view (inbox, today, upcoming, anytime, someday, logbook, trash) and/or state (open, done, archived, all); `search` ranks matches within that filter. `view: "logbook"` reads completed tasks and `view: "trash"` archived ones, whatever `state` says. Returns up to `limit` tasks; `hasMore` is true when more matched than were returned, and passing `nextCursor` back as `cursor` reads the next page. To find a task by its words across every view use search instead, and task_show for one task's checklist, comments and files.

  ConnectorNo auth

• task_editA

  Done BearA

  Update fields on one task (`task`) or on several at once (`tasks`, max 50). Only the provided fields are changed; omitted fields are left untouched. To remove a value, list the field in `clear` (e.g. `clear: ["deadline"]`). Notes replace the existing notes (no append). `assignee` assigns the task; clear it to unassign. `add_labels` and `remove_labels` change which labels are on it. Editing several tasks applies the same changes to every one of them and cannot be undone in one call, so confirm the list with the user first. For status changes use task_done, task_reopen or task_archive, and for checklist items use the available checklist tools; this tool does not change either.

  Connector

  Destructive

  No auth

• searchA

  Done BearA

  Search tasks across the workspace. Ranked lexical search over title and description: exact and prefix matches rank first, then word-boundary, substring, and fuzzy matches, with recency and status factored in. Multi-word queries match in any order. Searches every state by default, completed and archived included. Use this when the user names a task by its words; use task_list to browse a view (Today, Inbox, …) and task_show once you have the task number.

  ConnectorNo auth

• task_listA

  Done Bear MCPA

  List tasks in the workspace, most recently updated first. Completed tasks are ordered by completion time, newest first. Filter by view (inbox, today, upcoming, anytime, someday, logbook, trash) and/or state (open, done, archived, all); `search` ranks matches within that filter. `view: "logbook"` reads completed tasks and `view: "trash"` archived ones, whatever `state` says. Returns up to `limit` tasks; `hasMore` is true when more matched than were returned, and passing `nextCursor` back as `cursor` reads the next page. To find a task by its words across every view use search instead, and task_show for one task's checklist, comments and files.

  ConnectorNo auth

• task_editA

  Done Bear MCPA

  Update fields on one task (`task`) or on several at once (`tasks`, max 50). Only the provided fields are changed; omitted fields are left untouched. To remove a value, list the field in `clear` (e.g. `clear: ["deadline"]`). Notes replace the existing notes (no append). `assignee` assigns the task; clear it to unassign. `add_labels` and `remove_labels` change which labels are on it. Editing several tasks applies the same changes to every one of them and cannot be undone in one call, so confirm the list with the user first. For status changes use task_done, task_reopen or task_archive, and for checklist items use the available checklist tools; this tool does not change either.

  Connector

  Destructive

  No auth

• searchA

  Done Bear MCPA

  Search tasks across the workspace. Ranked lexical search over title and description: exact and prefix matches rank first, then word-boundary, substring, and fuzzy matches, with recency and status factored in. Multi-word queries match in any order. Searches every state by default, completed and archived included. Use this when the user names a task by its words; use task_list to browse a view (Today, Inbox, …) and task_show once you have the task number.

  ConnectorNo auth

• attachmentA

  Done BearA

  Add a file to a task or remove one. `action: "add"` fetches the given HTTPS `url`: the server downloads it, verifies its size and type, and stores it, so the model never sends file bytes. Pass a stable idempotency_key and reuse it for retries so a lost response cannot upload the file twice. `action: "remove"` deletes the named `attachment`. Read a task's files with task_show and `include: ["attachments"]`. Limited to 10 attachments per task and 10 MB per file. Allowed types: images (jpeg, png, gif, webp, heic), PDF, CSV, Markdown, and plain text.

  Connector

  Destructive

  No auth

• attachmentA

  Done Bear MCPA

  Add a file to a task or remove one. `action: "add"` fetches the given HTTPS `url`: the server downloads it, verifies its size and type, and stores it, so the model never sends file bytes. Pass a stable idempotency_key and reuse it for retries so a lost response cannot upload the file twice. `action: "remove"` deletes the named `attachment`. Read a task's files with task_show and `include: ["attachments"]`. Limited to 10 attachments per task and 10 MB per file. Allowed types: images (jpeg, png, gif, webp, heic), PDF, CSV, Markdown, and plain text.

  Connector

  Destructive

  No auth

• goal-request-ac-changeA

  Planner MCP — provable "done" for AI agentsA

  Request a change to an acceptance criterion. grounds=unreachable (default): the AC cannot be proven — requires ≥3 failed evidence attempts (weak/mismatch) with 0 matches. grounds=wrong_criterion: the AC measures the wrong thing (owner never approved this wording, or a revealed fact shows it tracks something else) — no failed attempts required, but you MUST pass quotedFragment: a verbatim slice of the criterion text you claim is wrong (checked against the stored text), and the reason must name the unapproved wording or the finding; difficulty alone is rejected. An LLM judge screens the reason either way. On pass, creates an escalation for the goal owner to resolve (edit AC text, split to sub-goal, drop AC without creating a child, or reject). Grove mode only, goal must be in_progress.

  ConnectorNo auth