"Fetching Kubernetes logs for a given pod or deployment" matching MCP tools:

• get_graph_project_info

  RationalBloks

  Get detailed graph project information including Kubernetes deployment status, Neo4j database health, pod status, and resource usage. Use this after deployment to verify the graph project is running correctly.

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• get_k8s_logs

  mcp

  Gets logs from a Kubernetes container in a pod. This is similar to running `kubectl logs`.

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• get_k8s_logs

  Google GKE

  Gets logs from a Kubernetes container in a pod. This is similar to running `kubectl logs`.

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• convoinspect

  InsideOut (Riley)

  INSPECTION: View a session's conversation transcript and metadata Returns the full message history (user / assistant / tool turns) plus the session's meta — workflow step, cloud, deployment status, drift state. This is the transcript-reader companion to the other read tools — combine it with: • `convostatus` for the live stack / config / pricing • `tfruns` for deployment history (apply / destroy / plan / drift) • `stackversions` for the stack-version ladder Use it when a user asks 'what did I say earlier?' or you need to retrace why the session ended up where it did. Read-only; never mutates session state. REQUIRES: session_id (format: sess_v2_...).

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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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• search_episodes_by_topic

  podcasts

  Semantic topic search across the podcast catalog. Unlike `search_episodes` (which does lexical/keyword matching), this tool understands meaning: a query for 'AI safety' will match episodes about 'AI alignment', 'AGI risk', or 'frontier model evaluation' even if they don't contain the exact phrase. Returns ranked episodes with the matched topic phrases so you can explain *why* each result is relevant before fetching the transcript. Best for conceptual or thematic queries — use `search_episodes` instead when the user is looking for a specific person, product, or verbatim phrase.

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• tfstatus

  InsideOut (Riley)

  MONITORING: Quick status check for Terraform deployments Check the current status of a Terraform deployment job. Use this tool to quickly check if a deployment is running, completed, or failed. Returns job status, job_id, and other metadata without streaming logs. Use tflogs to stream the actual deployment logs. REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id to target a specific deployment (use tfruns to discover IDs). **LIVENESS**: The response carries two distinct timestamps: - `updated_at` — last semantic change (only bumped when status / drift / version actually differ). Useful for sorting deployments; NOT a per-poll heartbeat. - `last_refresh_at` — last successful Oracle decode (stamped on every poll where reliable reached Oracle, even if nothing in the row changed). Use this to confirm reliable is still actively talking to Oracle for a long-running RUNNING job. Absent on rows that haven't been refreshed since the column was added. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.

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• get-logs

  mcp

  Get build and runtime logs for a deployment. If no deployment_id is provided, returns logs for the latest deployment. Use this after calling deploy to monitor build progress and diagnose failures. Logs include: framework detection output, dependency installation, build steps, container startup, and health check results. If a deployment fails, check the logs for error details — common issues include missing dependencies, build errors, or the app not listening on the correct PORT (check the PORT env var — 8080 for auto-detected frameworks, or the EXPOSE value from Dockerfile).

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• mu_quote

  mu

  Ask the MU manufacturing router how a thing could be MADE before you create a product: which supplier(s) can make it, the est. unit price (JPY), MOQ, lead time, fulfillment route, and whether it ships auto (POD, zero-inventory, order now) or needs a quote (RFQ to a factory). Pass `kind` (a known POD kind like tee/hoodie/rashguard_ls, OR a non-POD kind like `gi`/`loopwheel_sweat`/`seamless_knit`/`rashguard_premium`) OR a free-text `description` (e.g. "道着 for a dojo", "a seamless knit sweater") and the router infers the kind. Optional `qty`, `region` (e.g. jp/us), `budget` (JPY/unit). Read-only — creates nothing. No API key required. Options are ranked: buyable-now (auto + in budget) first. If an option's mode is `auto`, follow up with mu_create_product; if `quote`, it needs a human RFQ.

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• deploy_staging

  RationalBloks

  Deploy a project to the staging environment. This triggers: (1) Schema validation, (2) Docker image build, (3) GitHub commit, (4) Kubernetes deployment, (5) Database migrations. The operation is ASYNCHRONOUS - it returns immediately with a job_id. Use get_job_status with the job_id to monitor progress. Deployment typically takes 2-5 minutes depending on schema complexity. If deployment fails, check: (1) Schema format is FLAT (no 'fields' nesting), (2) Every field has a 'type' property, (3) Foreign keys reference existing tables, (4) No PostgreSQL reserved words in table/field names. Use get_project_info to see if the deployment succeeded.

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• tfoutputs

  InsideOut (Riley)

  INSPECTION: Retrieve Terraform outputs from a completed deployment Returns structured output values (VPC IDs, endpoints, cluster names, etc.) after a successful deploy. Sensitive outputs are redacted (shown as '(sensitive)'). By default returns outputs for the latest successful deploy. Optionally specify job_id to get outputs for a specific deployment. REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id (specific deployment), lifecycle (filter by step e.g. 'cloud-provision').

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• tfstatus

  InsideOut (Riley)

  MONITORING: Quick status check for Terraform deployments Check the current status of a Terraform deployment job. Use this tool to quickly check if a deployment is running, completed, or failed. Returns job status, job_id, and other metadata without streaming logs. Use tflogs to stream the actual deployment logs. REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id to target a specific deployment (use tfruns to discover IDs). **LIVENESS**: The response carries two distinct timestamps: - `updated_at` — last semantic change (only bumped when status / drift / version actually differ). Useful for sorting deployments; NOT a per-poll heartbeat. - `last_refresh_at` — last successful Oracle decode (stamped on every poll where reliable reached Oracle, even if nothing in the row changed). Use this to confirm reliable is still actively talking to Oracle for a long-running RUNNING job. Absent on rows that haven't been refreshed since the column was added. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.

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• find_tracks_by_key

  FreqBlog Music Metadata

  Find catalog tracks in a given musical key — for harmonic mixing and key-locked playlists. `key` accepts Camelot ("8A"), Open Key ("1m"), or a key name ("A-Minor", "F#-Major"). Returns tracks ordered by popularity, each with full audio features. To discover which keys mix well with a given key first, use find_compatible_keys.

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• delete_deployment

  mcp

  Deletes a deployment and its underlying app VM. Pass the numeric id from list_deployments. IMPORTANT: if the deployment used database:'managed', the managed Postgres VM is NOT deleted (data safety) — this tool returns its id so you can delete_database it when you're done with the data. Cannot be undone.

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• mark_thread_read

  shiply

  Zero the unread counter for a thread. Useful after the agent has read but not acted. read_thread already calls this implicitly; use this explicitly when you want to clear unread without re-fetching the thread body.

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• portal_evm_query_logs

  SQD Portal

  Query raw EVM logs with address/topic filters, common event aliases, earliest/latest scanning, and optional inline decoding. COMMON USER ASKS: - Recent USDC Transfer logs - First recent USDC Transfer log - Latest ERC721/pass mint ID and tx hash FIRST CHOICE FOR: - NFT or ERC721 mint lookups such as latest pass minted, token ID, and mint transaction hash - contract event questions where the user needs exact event evidence rather than wallet or transaction summaries WHEN TO USE: - You need event logs filtered by contract or topic signature. - You want decoded log hints while still keeping the raw log shape available. - You want the first or last matching event in a bounded block/time window. - You want common event names such as transfer, approval, swap, mint, or burn instead of remembering topic0 hashes. - You need the latest ERC721/pass mint in a bounded deployment/recent window: filter Transfer events with topic1 as the zero address, use scan_order=latest, limit=1, and decode=true to expose decoded_log.decoded.token_id plus transaction_hash. DON'T USE: - You only want token transfers, which are easier with the token-transfer tool. EXAMPLES: - Recent USDC Transfer logs: {"network":"base-mainnet","timeframe":"1h","token_symbols":["USDC"],"event":"transfer","limit":20} - First recent USDC Transfer log: {"network":"base-mainnet","timeframe":"1h","token_symbols":["USDC"],"event":"transfer","scan_order":"earliest","limit":1} - Latest ERC721/pass mint ID and tx hash: {"network":"base-mainnet","from_block":46020000,"to_block":46100000,"addresses":["0xE4E70FdF2Fc1147a7f35c4c5de88E6BeA63eeAfA"],"event":"transfer","topic1":["0x0000000000000000000000000000000000000000000000000000000000000000"],"scan_order":"latest","decode":true,"include_transaction":true,"limit":1} - Decode logs inline: {"network":"ethereum-mainnet","timeframe":"1h","topic0":["0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f55a4df523b3ef"],"decode":true,"limit":10}

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• get_stock_price_change

  Signal8

  Get percentage price changes for a stock across multiple timeframes: 1D, 5D, 1M, 3M, 6M, YTD, 1Y, 3Y, 5Y, 10Y, and MAX. Use this for quick "how much is it up/down" answers without fetching full candle data.

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• queryDataset

  mcp

  # Instructions 1. Query Axiom datasets using Axiom Processing Language (APL). The query must be a valid APL query string. **Only use this for `events`, `otel.traces`, and similar datasets. Do NOT use for `otel-metrics-v1` datasets — use `queryMetrics()` instead.** 2. ALWAYS understand schema before substantive queries—do not guess column names or types. Prefer `getDatasetFields()` or APL `| where _time > ago(5m) | getschema` on a narrow window (use dataset names from `listDatasets`); use `take 1` or project specific columns for sample values. Before you `where` or `summarize` by a field, estimate cardinality on recent data: `| where _time > ago(5m) | summarize count() by <field> | top 10 by count_`. Avoid `project *` or projecting all fields on very wide datasets unless deliberately mapping shape (see item 5). Skipping probes causes wrong field names, bad types, and expensive re-runs. 3. Keep in mind that there's a maximum row limit of 65000 rows per query. 4. Prefer aggregations over non aggregating queries when possible to reduce the amount of data returned. 5. Be selective in what you project in each query (unless otherwise needed, like for discovering the schema). It's expensive to project all fields. 6. ALWAYS restrict `startTime`/`endTime` to the narrowest window that answers the question—every query scans data and consumes resources. Prefer the smallest APL per step; widen time or complexity only after probing (item 2). 7. When filtering for a specific term or value, put it on the right field—use `has`/`has_cs`/`contains` there after item 2. See **Avoid `search`** under Query performance rules. 8. **`map[string]` columns (e.g. `attributes`, `attributes.custom`, `resource` in OTel-style data)** — `getDatasetFields` and in-query `getschema` show the type but **not** the keys inside the map. You must **sample** (`take`, `project` the map column, or `mv-expand` + `summarize` to list keys) to learn the structure, then use bracket access (e.g. `['attributes']['http.method']`, `['attributes.custom']['http.response.status_code']`). Do not assume key names across services or SDK versions. ### Query performance rules 1. **Narrow `startTime`/`endTime`** — These bound how much data is scanned. Do not rely on in-query `_time` filters alone; keep the API window as tight as your question allows. 2. **`_time` first in APL** — When you filter on `_time` in the query text, put `where _time between (...)` before other filters. This keeps extra in-query narrowing fast. 3. **Most selective `where` first** — Axiom does not reorder predicates; put the filter that removes the most rows earliest. 4. **`project` early and narrowly** — Avoid pulling all columns from very wide datasets (expensive payloads; risk of failures on huge rows). 5. **Prefer fast string ops** — Use `_cs` (case-sensitive) variants when possible; prefer `startswith`/`endswith` over `contains` when applicable; `matches regex` only as a last resort. 6. **Use `has`/`has_cs` for unique-looking strings** — IDs, UUIDs, trace IDs, error codes, session tokens. `has` leverages full-text indexes when available and is much faster than `contains` for high-entropy terms. Use `contains` only when you need true substring matching (e.g., partial paths). 7. **Duration literals** — e.g. `duration > 10s`, not manual conversion. 8. **Avoid search** — scans ALL fields. Use `has`/`has_cs`/`contains` on specific fields. 9. **Avoid heavy `parse_json()` in hot paths** — Filter/narrow first when possible. 10. **Avoid pack(*)** — creates dict of ALL fields per row. Use pack with named fields only. 11. Limit results—use take 10 or top 20 instead of default 1000 when exploring. 12. **Field quoting**—quote identifiers with dots/dashes/spaces: ['geo.country']. For map field keys, use index notation: ['attributes.custom']['http.protocol']. # Examples Basic: - Filter: ['logs'] | where ['severity'] == "error" or ['duration'] > 500ms - Time range: ['logs'] | where ['_time'] > ago(2h) and ['_time'] < now() - Project rename: ['logs'] | project-rename responseTime=['duration'], path=['url'] Aggregations: - Count by: ['logs'] | summarize count() by bin(['_time'], 5m), ['status'] - Multiple aggs: ['logs'] | summarize count(), avg(['duration']), max(['duration']), p95=percentile(['duration'], 95) by ['endpoint'] - Dimensional: ['logs'] | summarize dimensional_analysis(['isError'], pack_array(['endpoint'], ['status'])) - Histograms: ['logs'] | summarize histogram(['responseTime'], 100) by ['endpoint'] - Distinct: ['logs'] | summarize dcount(['userId']) by bin_auto(['_time']) Text matching & Parse: - Match on known fields (avoid full-row `search`): ['logs'] | where ['message'] has_cs "error" or ['message'] has_cs "exception" - Parse logs: ['logs'] | parse-kv ['message'] as (duration:long, error:string) with (pair_delimiter=",") - Regex extract: ['logs'] | extend errorCode = extract("error code ([0-9]+)", 1, ['message']) - Contains ops: ['logs'] | where ['message'] contains_cs "ERROR" or ['message'] startswith "FATAL" Data Shaping: - Extend & Calculate: ['logs'] | extend duration_s = ['duration']/1000, success = ['status'] < 400 - Dynamic: ['logs'] | extend props = parse_json(['properties']) | where ['props.level'] == "error" - Pack/Unpack: ['logs'] | extend fields = pack("status", ['status'], "duration", ['duration']) - Arrays: ['logs'] | where ['url'] in ("login", "logout", "home") | where array_length(['tags']) > 0 Advanced: - Union: union ['logs-app*'] | where ['severity'] == "error" - Case: ['logs'] | extend level = case(['status'] >= 500, "error", ['status'] >= 400, "warn", "info") Time Operations: - Bin & Range: ['logs'] | where ['_time'] between(datetime(2024-01-01)..now()) - Multiple time bins: ['logs'] | summarize count() by bin(['_time'], 1h), bin(['_time'], 1d) - Time shifts: ['logs'] | extend prev_hour = ['_time'] - 1h String Operations: - String funcs: ['logs'] | extend domain = tolower(extract("://([^/]+)", 1, ['url'])) - Concat: ['logs'] | extend full_msg = strcat(['level'], ": ", ['message']) - Replace: ['logs'] | extend clean_msg = replace_regex("(password=)[^&]*", "\1***", ['message']) Common Patterns: - Error analysis: ['logs'] | where ['severity'] == "error" | summarize error_count=count() by ['error_code'], ['service'] - Status codes: ['logs'] | summarize requests=count() by ['status'], bin_auto(['_time']) | where ['status'] >= 500 - Latency tracking: ['logs'] | summarize p50=percentile(['duration'], 50), p90=percentile(['duration'], 90) by ['endpoint'] - User activity: ['logs'] | summarize user_actions=count() by ['userId'], ['action'], bin(['_time'], 1h)

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• perspective_get_preview_link

  Perspective AI

  Returns a shareable URL that opens the perspective in preview mode, so you (or a teammate) can have a sample conversation with it before deploying. Behavior: - Read-only. The same stable preview URL every time for a given perspective; the link does not expire. - Conversations started from this URL are preview conversations and do NOT count toward the workspace's quota. - Anyone with the link can start a preview conversation, even unauthenticated — treat as semi-public. When to use this tool: - After perspective_create or perspective_update, to manually verify the perspective's behavior before going live. - To share a preview link with a teammate for review. When NOT to use this tool: - For production deployment — use perspective_get_embed_options, which returns embed snippets and a public share link whose conversations count toward quota. - To inspect existing real conversations — use perspective_list_conversations / perspective_get_conversation. Typical flow: 1. perspective_create → design 2. perspective_get_preview_link → test 3. perspective_update → refine 4. perspective_get_embed_options → deploy

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• convoinspect

  InsideOut (Riley)

  INSPECTION: View a session's conversation transcript and metadata Returns the full message history (user / assistant / tool turns) plus the session's meta — workflow step, cloud, deployment status, drift state. This is the transcript-reader companion to the other read tools — combine it with: • `convostatus` for the live stack / config / pricing • `tfruns` for deployment history (apply / destroy / plan / drift) • `stackversions` for the stack-version ladder Use it when a user asks 'what did I say earlier?' or you need to retrace why the session ended up where it did. Read-only; never mutates session state. REQUIRES: session_id (format: sess_v2_...).

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