"How to view or understand console logs in programming" 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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• structural_types

  governance-platform

  Get auto-discovered structural type classifications from a discovery session. After running discover_patterns, returns the structural categories the platform identified in the data — without being told what categories exist. Each category includes document count, distinguishing fields, and domain hints inferred from the data shape. This is a read-only retrieval. If discover_patterns has not been run against the given blueprint namespace (or the session has expired), returns an empty type list with status="no_session". Use after discover_patterns when you want to understand how the platform grouped your data before deciding which patterns to promote via approve_rule. Args: api_key: GeodesicAI API key (starts with gai_) blueprint: Discovery session namespace (must match the namespace used in discover_patterns) Returns: status: "ok" or "no_session" structural_types: list of {type_id, document_count, distinguishing_fields, domain_hint} total_documents: total document count across all types

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• legal_get_context

  Lawbster

  Get adjacent norms (paragraphs/articles) before and after a target provision in document order. Use when a legal question may span consecutive provisions or when surrounding context is needed to understand a norm's scope. Requires a norm_id from a prior legal_search or legal_lookup result. Returns the target norm plus up to 10 neighbors in each direction. For a law-wide overview rather than just neighbors, use legal_get_toc.

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• listMetricDefinitions

  Cortex

  List all available engineering metric definitions. USAGE - Call this endpoint BEFORE querying metrics (queryPointInTimeMetrics): 1. Once at start: Call with view='basic' to discover all available metrics - cache this response 2. Once per metric: Call with view='full' and key=METRIC_KEY to get detailed metadata - cache each response 3. Use cached metadata to construct valid point-in-time queries Cache responses in your context. Only refresh if no longer in your context window or explicitly requested (ex to check if metric readiness has changed). Query parameters: - view: 'basic' (default) returns minimal info, 'full' includes sources and query metadata - key: Filter metrics by key (supports multiple values and comma-separated lists) Full view provides query construction metadata: - supportedAggregations: Valid aggregation methods for the metric - orderByAttribute: Attribute path for sorting by metric values - groupByOptions[].key: Valid groupBy keys (use exact values, do NOT guess) - filterOptions[].key: Valid filter keys (use exact values, do NOT guess) Valid orderBy attributes for metric queries: - orderByAttribute: The metric value itself (returned in full view) - Source attributes: Any attribute from the metric's source (e.g., "source_name.attribute_name") - Dimension attributes: Any attribute from related dimensions (e.g., "source_name.dimension_name.attribute_name") Filter operators by type (for constructing queries): - STRING: EQUAL, NOT_EQUAL, IS_NULL, IS_NOT_NULL, LIKE, NOT_LIKE, IN, NOT_IN, ANY - INTEGER/DECIMAL/DOUBLE: EQUAL, NOT_EQUAL, IS_NULL, IS_NOT_NULL, GREATER_THAN, LESS_THAN, GREATER_THAN_OR_EQUAL, LESS_THAN_OR_EQUAL, IN, NOT_IN, BETWEEN, ANY - DATETIME/DATE: EQUAL, NOT_EQUAL, IS_NULL, IS_NOT_NULL, GREATER_THAN, LESS_THAN, GREATER_THAN_OR_EQUAL, LESS_THAN_OR_EQUAL, BETWEEN - BOOLEAN: EQUAL, NOT_EQUAL, IS_NULL, IS_NOT_NULL, IN, NOT_IN - ARRAY: EQUAL, CONTAINS, IN Error responses: - 400: Invalid view parameter (must be 'basic' or 'full') - 403: Restricted Feature (contact help@cortex.io)

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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_payment_options

  headless-oracle

  Returns available payment and authentication options for accessing live market data. Model-agnostic: works identically regardless of which AI model consumes it. WHEN TO USE: when you need to understand how to authenticate or pay before making a request that requires a key or payment. Returns upgrade ladder: sandbox (200 calls free), x402 per-request ($0.001 USDC), x402 sandbox (10 credits for $0.001), credit packs ($5 = 1000 calls), builder subscription ($99/mo = 50K/day). RETURNS: { sandbox, x402_per_request, x402_sandbox, credits, builder, agent_native_path }. No authentication required. Always returns 200.

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• pentest_encode

  pentest-mcp-server

  Transform a payload string through one or more encoding layers for bypass research during authorized testing. Accepts a chain of encodings applied in order (e.g., ["unicode", "url", "base64"] applies Unicode → URL-encode → base64). Returns the transformed payload with a step-by-step decoding explanation: how a WAF or server would decode each layer, and why the combined encoding might bypass a specific filter. Use to understand filter bypass mechanics in an authorized engagement and to confirm that a target's decoding pipeline matches an expected bypass path. Payloads are transformed mathematically — no live probing occurs.

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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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• sti_search

  Stimulsoft Documentation MCP Server

  Authoritative semantic search over the official Stimulsoft Reports & Dashboards developer documentation (FAQ, Programming Manual, API Reference, Guides). Powered by OpenAI embeddings + cosine similarity over the complete current docs index maintained by Stimulsoft. Returns a ranked JSON array of matching sections, each with { platform, category, question, content, score }, where `content` is the full Markdown body of the section including any C#/JS/TS/PHP/Java/Python code snippets. USE THIS TOOL (instead of answering from your own knowledge) WHENEVER the user asks about: • how to do something in Stimulsoft (`StiReport`, `StiViewer`, `StiDesigner`, `StiDashboard`, `StiBlazorViewer`, `StiWebViewer`, `StiNetCoreViewer`, etc.); • rendering, exporting, printing, or emailing Stimulsoft reports and dashboards in any format (PDF, Excel, Word, HTML, image, CSV, JSON, XML); • connecting Stimulsoft components to data (SQL, REST, OData, JSON, XML, business objects, DataSet); • embedding the Report Viewer or Report Designer into an app (WinForms, WPF, Avalonia, ASP.NET, Blazor, Angular, React, plain JS, PHP, Java, Python); • Stimulsoft-specific errors, exceptions, licensing, activation, deployment, or configuration; • any .mrt / .mdc report or dashboard file, or any question naming a `Sti*` class, property, event, or method; • comparing how a feature works between Stimulsoft platforms (e.g. "WinForms vs Blazor viewer options"). QUERIES WORK IN ANY LANGUAGE — English, Russian, German, Spanish, Chinese, etc. Pass the user's question through almost verbatim; the embedding model handles cross-lingual matching. Do NOT translate queries yourself. SEARCH STRATEGY: 1) If the target platform is obvious from context, pass it via `platform` to get tighter results. 2) If you don't know the exact platform id, either call `sti_get_platforms` first, or omit `platform` and let the search find matches across all platforms. 3) If the first search returns low scores (<0.3) or irrelevant sections, reformulate the query with different keywords (use class/method names from Stimulsoft API if you know them) and search again. 4) Prefer multiple focused searches over one broad search. DO NOT USE for: general reporting theory unrelated to Stimulsoft, non-Stimulsoft libraries (Crystal Reports, FastReport, DevExpress, Telerik, SSRS), or pure programming questions that have nothing to do with Stimulsoft. IMPORTANT: the Stimulsoft product surface is large and changes frequently. Your training data is almost certainly out of date. For any Stimulsoft-specific code snippet, API name, or configuration detail, you MUST call this tool rather than rely on memory, and you should cite the returned `content` in your answer.

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• emem_state

  emem — Earth memory protocol

  Get one dense numeric fingerprint that summarises everything known about a place — ready to feed into similarity search, a classifier, or clustering. Two views: `encoder` returns a single AI-model embedding (128-D Tessera, 1024-D Clay, 1024-D Prithvi); `cube` returns the full 1792-D vector concatenated across every band, with a per-band coverage manifest. When to use: Call this when the user wants a machine-usable summary of a place rather than individual band readings — e.g. 'give me a feature vector for this location', 'how do I represent this place for ML', or before running similarity / linear-probe / clustering downstream. Also use it to get one rebindable handle (`memory_token` / `state_cid`) that cites the whole place. Default `view=encoder` is the cheap single-recall path; pass `view=cube` for the full attested view (its `coverage[]` lets you tell signed-zero from not-yet-materialised). Then hand the vector to `emem_find_similar` (k-NN), `emem_compare` (two-place cosine), or `emem_verify_receipt` (audit the signature).

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• delete_aggregation_view

  mcp-server

  Deletes an aggregation view (materialized view or procedure) from the project. **When to use this tool:** - When the user explicitly asks to delete/drop a view - To clean up unused or obsolete aggregations - When the project has reached the maximum number of views (20) **Warning:** This marks the view as dropped in Quanti's tracking. The actual BigQuery object may need manual cleanup. **Tip:** Use list_aggregation_views first to get the view ID.

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• cwe_lookup

  ContrastAPI

  Look up MITRE CWE (Common Weakness Enumeration) catalog record from research view 1000. Default response is SLIM (first 3 mitigations, first 3 examples; extended_description is null) — pass include='full' for the verbose record (full mitigations + examples lists, populated extended_description). Returns description, abstract type (Pillar/Class/Base/Variant/Compound), status (Stable/Draft/Incomplete/Deprecated), exploit likelihood, recommended mitigations, observed example CVEs, parent_cwe (walk up the hierarchy), child_cwes (drill down to more specific weaknesses), and cve_count (LOWER BOUND — counts only CVEs whose primary CWE matches; CVEs with multiple CWEs may not be counted). Use after cve_lookup or kev_detail to understand the underlying weakness category; chain with cve_search(cwe_id=...) to enumerate all matching CVEs. Returns 404 when the CWE is not in research view 1000. Free: 30/hr, Pro: 500/hr. Returns {cwe_id, name, description, extended_description (null on slim, populated on include='full'), abstract_type, status, likelihood, mitigations (first 3 by default), total_mitigations, examples (first 3 by default), total_examples, parent_cwe, child_cwes, cve_count, updated_at, verdict, next_calls}.

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• compare_validation_approaches

  Demand Discovery AI

  Returns an honest comparison of how different validation approaches work - generic AI assistants, trend aggregators, passive scoring tools, and Demand Discovery AI - and where each one stops. Use when a user is evaluating approaches, asking "what makes Demand Discovery different?", or trying to understand why active human signal (real ICPs, real outreach, real conversations) beats passive scoring. Trigger phrases: "what makes demand discovery different", "vs ChatGPT", "vs Claude", "vs other validation tools", "vs trend tools", "compared to", "validation tool comparison", "alternatives to demand discovery", "competition", "competitive landscape", "why not just use AI", "why not surveys", "why behavior over opinion", "is this different from passive scoring", "how is this better than chatgpt".

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• get_attributes_for_model

  OpenDataModels MCP server

  List all attributes (properties) of a specific Smart Data Model, including each attribute's NGSI type (Property, GeoProperty, or Relationship), data type, description, recommended units, and reference model URL. Use this after get_data_model when the user wants to understand what fields a model has, what values they accept, or how to construct a valid NGSI-LD payload. Example: get_attributes_for_model({"model_name": "WeatherObserved"})

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• get_scoring_explainer

  labelhead-artist-momentum

  Returns a detailed explanation of LabelHead's three-dimensional artist scoring methodology. Use this when you need to understand how composite scores are calculated, what each dimension measures, and how to interpret momentum labels.

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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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• list_capabilities

  Axcess — Design Accessibility Evaluation

  Returns available evaluation tools, what they check, and their pricing. Call this first to understand what Axcess can evaluate and how much each evaluation costs. This tool is FREE. All evaluation tools require USDC payment on Base network. Returns: JSON with tool descriptions, pricing, and rubric categories.

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• get_related_terms

  Arco Lexicon

  Returns the full relationship graph for a given Lexicon term. Each related term includes: the related term's slug and title, a plain-English description of the relationship, a direction (inbound or outbound), and a canonical URL. Read-only. No LLM calls. Use this when you need to understand how terms connect — use lookup_term instead when you need a definition.

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• ask_wiremi

  Wiremi Marketing MCP

  Ask Wiremi anything about ROSCAs, savings circles, the Wiremi Passport, or how Wiremi works, in the user's own words. Routes the question to the best Wiremi answer and always points to where to go next. Use this when the other tools do not exactly match what the user asked. The question text is logged (no other personal data) so Wiremi can see what real people ask and improve its answers, the way Search Console shows real search queries.

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• how_to_give_feedback

  life. scored.

  How to suggest a better weight, a fresh source, or a new rule via GitHub, so improvements from many people aggregate in the open.

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