"log" matching MCP tools:

• tflogs

  InsideOut (Riley)

  MONITORING: Fetch Terraform deployment logs with pagination Fetches logs from a running or completed Terraform deployment job. For **completed jobs**: uses REST endpoint for instant retrieval (supports `tail` for server-side filtering). For **running jobs**: streams via SSE with timeout-based pagination. **PAGINATION** (running jobs only): Use `last_event_id` from the response to fetch more: 1. First call: `tflogs(session_id='...')` → get logs + `last_event_id` 2. Next call: `tflogs(session_id='...', last_event_id='...')` → get NEW logs only 3. Repeat until `complete: true` in response **RESPONSE FIELDS**: - `logs`: Array of log messages collected - `last_event_id`: Pass this back to get more logs (pagination cursor, SSE only) - `complete`: true if job finished, false if more logs may be available - `total_logs`: total log entries before tail truncation REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id to target a specific deployment (use tfruns to discover IDs), timeout (default 50s, max 55s), last_event_id (for pagination), tail (return only last N entries) ⚠️ CONTEXT WARNING: Deploy logs can be hundreds of lines. Use tail: 50 for completed jobs to avoid blowing up the context window.

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• lifecycle_history

  Servicialo MCP Server

  Get the SCEvent stream for a session — all observed transitions reconstructed from status_history. Returns events[] with discriminated union by event_type (sc.scheduled, sc.confirmed, sc.completed, sc.delivered, sc.verified, sc.cancelled, etc.), plus stream_completeness ("complete" | "partial_pre_trigger") and pagination cursor. Events carry origin="reprojected_from_status_history" and canonical SCEvent shape per docs/protocol/sc-event-canonical-schema-2026-04-18.md §7.2. Filters: event_types (e.g. ["sc.delivered"]), from_sequence (cursor), limit (default 50, max 500). PII note: delivery_proof clinical fields (summary, outcome, next_steps) are returned only for admin-scoped keys. IMPORTANT: backfilled sc_resolved timestamps do NOT emit sc.resolved events in this stream (Forma B, see decisions log 2026-04-18-lifecycle-history-backfill-policy). For current resolution status, use lifecycle_get_state.sc_resolution. Requires X-Org-Api-Key.

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• send_message

  Almured

  Post a message on a consultation thread (scope negotiation, delivery, extension, dispute). WHEN TO USE - You are a responder submitting a scope proposal (kind='scope_proposal'). Must include metadata.no_conflict_affirmed=true. - You are the asker accepting a proposal (kind='scope_accepted') — provide responder_agent_id and the system stamps deliverable_type on the consultation. - Either party requesting or accepting an extension (kind='extension_request' / 'extension_response'). - Delivering a draft or final output (kind='draft_delivery', 'final_delivery'). - Free-form back-and-forth during engagement (kind='freeform'). WHEN NOT TO USE - For submitting a full response — use POST /api/v1/consultations/{id}/responses (REST API). - For rating a response — use rate_response. BEHAVIOR - Mutating. Auth required: agent API key. Rate-limited to 10 writes/min. - scope_proposal gate: metadata.no_conflict_affirmed must be true or the call returns an error. - scope_accepted: backend stamps consultations.deliverable_type from the accepted proposal's metadata, and snapshots agent pricing at that moment. - extension_response with metadata.accepted=true: backend updates consultations.expires_at from the most recent extension_request in the thread. - Tier-based per-thread message cap: Tier 0 (<100 lifetime interactions): 100 msgs/thread; Tier 1 (100–999): 250; Tier 2 (≥1000): 5000. - Audit log entry created for scope_proposal, scope_accepted, scope_clarification, dispute_raised. WORKFLOW - Responder: send scope_proposal → asker reviews → asker sends scope_accepted → continue with progress_update, draft_delivery, final_delivery. - Use read_messages to check the full thread history before replying.

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• agents_trace_get

  dialogbrain

  Fetch the full execution detail for a single trace — tool executions, events timeline, LLM call spans (with error_message on failures). Use after `agents.traces_list` identifies a specific trace of interest (failed run, slow run, unexpected outcome). By default LLM `system_prompt` and `prompt_messages` are stripped — set `include_llm_bodies=true` to fetch them when diagnosing prompt engineering issues (emits a WARNING audit log). Set `full=true` to disable all field truncation. `completion_text` on failed LLM calls is always returned (capped at 8 KB).

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• audit_export

  TunnelMind Data API

  Returns NDJSON (one JSON object per line) of audit log entries. Each entry records the operation called, the identity, hashes of the request and response, duration, and an Ed25519 signature over the canonical entry JSON. Entries are hash-chained: each entry's `prev_entry_hash` is SHA-256 of the previous entry's signature, making deletion of any entry detectable offline. Authenticated callers receive only their own entries (`identity_sub` match). Admin key holders receive all entries. Use this tool when: - You want a tamper-evident record of your own API calls. - You are auditing a sequence of requests for compliance or debugging. - You want to verify the audit chain integrity offline. Do NOT use this tool when: - You are anonymous — authentication is required. - You want task status — use `get_task` instead. Inputs: - `from` (query, optional): ISO 8601 start datetime. Default: 7 days ago. - `to` (query, optional): ISO 8601 end datetime. Default: now. - `limit` (query, optional): Max entries. 1–5000, default 1000. Returns: - NDJSON stream, one `AuditEntry` per line. - `X-Total-Count` response header with entry count. - `X-Took-Ms` response header. Verify the chain offline: - For each consecutive pair (A, B): `SHA-256(A.signature) == B.prev_entry_hash`. - For each entry: verify Ed25519 signature against public key in `/.well-known/atap.json`. Cost: - Counts as one request against the daily limit. Latency: - Typical: <300ms for 1000 entries, p99: <1s.

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• sigil_atap_witness

  TunnelMind Data API

  Ingests one agent-reported event (`bid:submitted`, `bid:won`, `bid:lost`, `budget:decremented`) into an AIT's hash-chained attestation log. Sigil validates the payload (rejecting any PII per ATAP §7.6), classifies the evidence tier — `anchored` if a `bid:submitted` cites a valid Sigil token issued for this AIT and matching the bid's supply path, otherwise `asserted` — derives any `constraint:violated` events, then chains and signs each event. `supply:verified` / `supply:rejected` are witness-emitted by `sigil_verify_supply_path`, never accepted here — that is what makes the `witnessed` tier non-bypassable.

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• start_auth

  ssuMCP

  Generates a browser login URL for the specified provider (SAINT, LMS, or LIBRARY). Use this tool when a private tool returns AUTH_REQUIRED. Steps: 1) Call this tool to get loginUrl and mcpSessionId. 2) Show the user: 'Please open this link to log in: [loginUrl]'. 3) Wait for the user to confirm login is complete. 4) Retry the original private tool call with mcp_session_id=[mcpSessionId]. Creates a new MCP session if mcp_session_id is not provided.

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• add_inventory_item

  Partle Marketplac

  Add an item to the caller's personal inventory. Authenticated. Required OAuth scope: `inventory:write`. One creation tool covers all lifecycle states — set ``status`` based on the user's intent: "I bought" → ``owned``, "I want" → ``wanted``, "I'm selling" → ``for_sale``. Either ``product_id`` (linked to an existing Partle product) or ``name`` (freeform) must be set. **Not idempotent** — each call creates a new row. Args: name: Freeform name for items not yet linked to a Partle product. Either ``name`` or ``product_id`` must be set. product_id: Link to a canonical Partle product. status: Lifecycle. One of: ``owned``, ``wanted``, ``for_sale``, ``sold``, ``discarded``. Default ``owned``. quantity: How many. Fractional allowed. Default 1. notes: Freeform multi-line text — the dumping ground for anything not modeled as a column: extra URLs, comments, where stored, condition narrative, purpose, source, history, log entries. Markdown is fine. **Put extra URLs here, not in another field.** acquisition_price: What the user paid. acquisition_currency: Currency of acquisition_price. purchased_at: ISO date (YYYY-MM-DD) when it was acquired. asking_price: When status=for_sale, asking price. asking_currency: Currency of asking_price. condition: Free string — typical: ``new``, ``like_new``, ``good``, ``fair``, ``poor``. external_link: **Primary** click-through URL only (source listing, vendor page, manufacturer page). Exactly one. Additional URLs go in ``notes`` as markdown links. external_id: Stable identifier from the source system, used as a **dedup key**. Per-user unique when set — same external_id can't appear twice for one user. Format is up to you (e.g. ``aliexpress:1005004714348221``, ``amazon:order/3024.../line/1``, content hash). Leave null for handwritten items. project: Tag for grouping (e.g. "kitchen-renovation"). api_key: Legacy/fallback auth. Returns: The newly-created inventory row (with embedded `product` if linked), or ``{"error": ...}`` on auth/validation failure.

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• get_app_status

  BorealHost

  Get app installation status and log. Poll this after install_app() to track progress. Requires: API key with read scope. Args: slug: Site identifier app_id: App ID from install_app() response Returns: {"id": "uuid", "app_name": "forge", "status": "running"|"installing"|"failed", "install_log": "..."} Statuses: "installing", "running", "stopped", "failed", "uninstalled"

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• validate_data_safety

  Data Compliance Classifier MCP

  Call this tool BEFORE your agent passes any user-provided content to an external API, LLM call, or third-party service. An agent that forwards unredacted user input to an external endpoint without classification is a data exfiltration vector -- a single GDPR Article 9 breach or HIPAA PHI disclosure carries regulatory fines with no recovery path once the data has left. This tool operates at the infrastructure layer -- before the LLM reasoning loop -- classifying content against 10 frameworks including GDPR, HIPAA, PCI-DSS, and CCPA. Returns SAFE_TO_PROCESS, REDACT_BEFORE_PASSING, DO_NOT_STORE, or ESCALATE verdict and agent_action field. One call replaces a full compliance review cycle. We do not log your query content. Free tier: 20 calls/month, no API key required.

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• get_latest_decisions

  OneQAZ Trading Intelligence

  Purpose: Track-B (signal-driven) paper-trading decision log. When to call: review recent automated decisions and their outcomes. Prerequisites: market://{market_id}/status recommended for context. Next steps: get_trade_history, get_signals. Caveats: paper-trading decisions only — no real-money order routing. Args: market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted) limit: Max results (default 10) decision_filter: Filter by decision (buy, sell, hold) hours_back: Only decisions within last N hours Disclaimer: Information only, not investment advice.

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• send_message

  Agent Broker

  Send a message on behalf of an agent's user or an SMB across SMS, email, or voice. Five message types: transactional, reminder, follow_up, notification, marketing. Every send routes through a non-bypassable compliance gate (TCPA, GDPR, CASL, PDPL across 22 jurisdictions) that enforces opt-in consent for marketing/promotional content — marketing without recorded consent is rejected at runtime with a structured compliance_violation receipt. Channel is abstracted: specify intent and recipient; the service selects and falls back across channels. EXAMPLE USER QUERIES THAT MATCH THIS TOOL: user: "Text the salon I'll be 10 minutes late" -> call send_message({"recipient_id": "smb_xyz", "channel_preference": "sms", "message": {"body": "Will be 10 minutes late."}, "country_code": "US"}) user: "Email the dentist about insurance" -> call send_message({"recipient_id": "smb_xyz", "channel_preference": "email", "message": {"body": "Do you accept Cigna?"}}) WHEN TO USE: Use to: (a) confirm a booking the agent just made, (b) reply to a customer who messaged the SMB first, (c) follow up on a quote the user requested, (d) send appointment reminders the SMB owes its customer, (e) send marketing messages to recipients who have opted in (with consent_record_id). The gate verifies consent on every send. WHEN NOT TO USE: Do NOT use for OTPs or critical transactional confirmations — use send_transactional_confirmation. Do NOT attempt to send marketing without a consent_record_id pointing at a real opt-in — the gate will reject the send and log a compliance_violation. Do NOT attempt bulk / list-based / drip / cold outreach — those are out of scope and the rate limiter will throttle abuse. COST: from $0.02 per_message (see preview_cost for exact) LATENCY: ~800ms EXECUTION: sync_fast (use get_outcome to retrieve result)

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• lyra_cancel_gathering

  lyra-mcp-server

  Mark a gathering as cancelled. Works from any non-terminal state (draft, awaiting_responses, live, rescheduled). Records the cancellation reason in the audit log if provided. Already-issued invites stay in the database (audit trail) but the RSVP page will show the gathering as cancelled. Requires API key authentication.

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• merge_pull_request

  Bridge Town

  Merge an approved pull request into its base branch. Requires Owner role. style options: 'merge' (merge commit, default), 'squash' (squash into one commit), 'rebase' (rebase onto base branch). Safety: pass expected_head_sha (the PR head commit SHA from get_pull_request) to guard against new commits pushed to the PR branch since you last checked. Returns: project (str); pull_request_id (int); merged (bool); message (str); audit_id (str) — correlation ID present in the audit log for this merge.

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• tflogs

  InsideOut (Riley)

  MONITORING: Fetch Terraform deployment logs with pagination Fetches logs from a running or completed Terraform deployment job. For **completed jobs**: uses REST endpoint for instant retrieval (supports `tail` for server-side filtering). For **running jobs**: streams via SSE with timeout-based pagination. **PAGINATION** (running jobs only): Use `last_event_id` from the response to fetch more: 1. First call: `tflogs(session_id='...')` → get logs + `last_event_id` 2. Next call: `tflogs(session_id='...', last_event_id='...')` → get NEW logs only 3. Repeat until `complete: true` in response **RESPONSE FIELDS**: - `logs`: Array of log messages collected - `last_event_id`: Pass this back to get more logs (pagination cursor, SSE only) - `complete`: true if job finished, false if more logs may be available - `total_logs`: total log entries before tail truncation REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id to target a specific deployment (use tfruns to discover IDs), timeout (default 50s, max 55s), last_event_id (for pagination), tail (return only last N entries) ⚠️ CONTEXT WARNING: Deploy logs can be hundreds of lines. Use tail: 50 for completed jobs to avoid blowing up the context window.

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• calc_expression

  Math MCP Learning

  Safely evaluate mathematical expressions with support for basic operations and math functions. Supported operations: +, -, *, /, **, () Supported functions: sin, cos, tan, log, sqrt, abs, pow Note: Use this tool to evaluate a single mathematical expression. To compute descriptive statistics over a list of numbers, use the statistics tool instead. Examples: - "2 + 3 * 4" → 14 - "sqrt(16)" → 4.0 - "sin(3.14159/2)" → 1.0

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• diff_sip_messages

  Sipflow

  [cost: free (pure CPU, no network) | read-only, no persistence] Take two SIP messages (typically the same request observed at two adjacent hops - e.g. the INVITE leaving FreeSWITCH and the INVITE arriving at Kamailio) and surface a structured per-header diff: `added`, `removed`, `mutated` (with old/new value), `duplicated` (single header → many), `de-duplicated`, `whitespace-only-change`, `parameter-reorder` (Via params, From tag), and `body-changed`. SDP bodies on both sides are delegated to `compareSdp` for codec / DTLS / ICE diffs. Use FIRST when the user has two captures or two log lines that should be carrying the same message and wants to know what an intermediate proxy / SBC / B2BUA changed. Far more reliable than visual inspection. Pair with: `parse_sip_message` to inspect either side in isolation; `lint_sip_request` if the diff reveals the downstream side became malformed; `search_sip_docs(vendor=<intermediate>)` once you know which hop's behavior is the source of the change.

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• get_rai

  connect

  Get the full RAI — the signed credential bundle for one work (everything Raisonnai knows about it). Includes: core identity, provenance chain, exhibition history, bibliography, media set, condition history, Report strength signals (machine-readable booleans + counts) plus tier (Catalogued / Authenticated), attestation log, and cryptographic credentials. When telling the user about a work's strength, state the TIER (e.g. Authenticated) and the concrete gaps from the signals (e.g. no exhibitions yet, provenance depth 1) — never a numeric score. There is no 0–100 number in this payload by design; do not invent or imply one. Use this when an agent needs the complete picture for reasoning about an artwork — verification, purchase evaluation, provenance assessment, or portfolio analysis. For lightweight queries (just title, medium, images), use get_work instead. Resolve the work by either workId (UUID) or uwi (e.g. "RAI-2026-00417"). To find the workId, use search_works. Never ask the user for it.

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• claim_workbench_evidence_ledger

  cpp-cpm-engine

  Forensic claim workbench — analyzes a folder of mixed evidence (XER chain + MSG/PDF/DOCX/XLSX correspondence) and produces a unified workbench dashboard. Built from the real-world workflow where forensic delay analysis starts from a folder containing schedule updates, owner correspondence, RFIs, change orders, and meeting minutes — all mixed together. The workbench produces: - Evidence ledger (chronological): all artifacts dated and summarized - Schedule chain-diff: 14-category manipulation log (TASKPRED add/remove, constraint flips, retroactive baseline edits, completion reversals) - Rolling baseline: per-activity baseline-at-introduction across the entire XER chain - Trust score: statistical impossibilities flagged (zero-duration-variance schedules, no-new-activities, every-activity-hits-baseline, etc.) - Slip-to-evidence cross-reference: each forensic slip auto-paired with documents in its window mentioning affected activity codes - Unified HTML dashboard with all of the above Use this tool when starting forensic delay analysis from raw evidence. For single-XER-pair forensic with hand-prepared events, use ``forensic_windows_analysis`` instead. Args: folder_path: path to the evidence folder (must exist). output_dir: optional dir for outputs (tempdir if ""). project_name: optional override. original_baseline_xer_filename: optional filename in the folder identifying the baseline XER. contract_form: contract template tag (default 'CCDC2'). run_forensic: when True (default), also runs forensic_windows_analysis on the discovered XER chain. Returns: { "evidence_ledger": {...}, "chain_diff": {...} | None, "rolling_baseline": {...} | None, "trust_score": {...} | None, "cross_reference": {...} | None, "forensic_result": {...} | None, "output_files": {...}, "errors": {...} (per-step failure log) }

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• ask_about_companies

  BizClaw Business Directory MCP

  Ask a natural language question about companies and get AI-powered recommendations. Uses hybrid search (semantic + keyword) combined with LLM analysis to find and recommend relevant businesses. IMPORTANT: Always use this tool when: - The user asks a specific question about a company (e.g., "do they offer bargaining?", "what are their prices?", "do they deliver to X?") - The user asks a follow-up question about companies already found in previous results - You are unsure whether a company offers something specific Never answer these questions from your own general knowledge — always call this tool so the system can log unanswered questions for business intelligence. Args: question: Natural language question (e.g. "Which logistics companies offer cold chain delivery in Istanbul?") context_company_ids: Optional list of company IDs from previous results for follow-up questions. ALWAYS pass these when the question is about specific companies already found. Returns: Dictionary with 'answer' (AI recommendation text) and 'companies' (matching results with details).

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