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204,692 tools. Last updated 2026-06-15 00:48

"namespace:io.github.mia-platform" matching MCP tools:

  • 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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  • Fetch the full record for a single creator by ID or exact platform username. Use this when you already have either: - a canonical creator UUID returned by `search_creators`, `semantic_search_creators`, `autocomplete_creators`, or `find_lookalike_creators`; or - an exact platform+username pair such as platform "instagram" and username "niickjackson". Pass `include: ['profiles']` to also receive the creator's social profile summaries when using a creator UUID. For platform+username inputs, this tool resolves through the profile endpoint and returns the profile record plus the underlying creator record, so you already get the matched profile context. Examples: - User: "Get creator 123e4567-e89b-12d3-a456-426614174000" -> call with id. - User: "Get @niickjackson on Instagram" -> call with platform "instagram" and username "niickjackson", or use `get_profile` if profile metrics are the main need. - User: "Tell me about @niickjackson and include his profiles" -> use platform "instagram" and username "niickjackson"; then use `get_profile`/`get_posts` for platform-specific metrics and content if needed. Use `lookup_profiles` for batch exact profile lookups.
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  • Dispatch to the SOCIAL LISTENING RESEARCHER — multi-platform community-signal interpretation. Use for: "what are practitioners saying about X across platforms / what jargon is emerging in field Y / what is the cross-platform discourse around brand/topic Z". Treats T3 community sources as primary data, distinguishes cross-platform patterns from single-platform noise. ≥3 platforms sampled per brief. Returns: Signal map (Signal / Platforms / Volume / Sentiment + recency) + Per-platform evidence trail + Cross-platform vs single-platform classification + Confidence flag + Sources. NOT for: single-source thematic work (use dispatch_qualitative_researcher) / numerical sentiment effect sizes (use dispatch_quantitative_researcher). ASYNC version: returns { job_id } immediately, the specialist runs durably on a Vercel Workflow (no 300s timeout). Use this version when the specialist is expected to take >90s. Call get_dispatch_result(job_id) periodically (respect wait_ms_hint in the response) until status === 'completed' or 'failed'. Idempotent: same brief + same org reuses the same job_id, so retries don't fan out duplicate runs.
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  • Verify that two execution replay contracts represent the same deterministic result. This is the programmatic proof of GeodesicAI's core promise: same input + same rules = same result, every time. Given two replay contracts (e.g. from the original execution and a re-run), this tool compares all component hashes and reports whether the executions are byte-identical. Use this to: - Prove to an auditor that a decision from March 3rd matches a re-run today. - Detect when a rule change has altered execution behavior (input hash matches but canonical trace hash differs → the rules diverged). - Confirm a Blueprint migration didn't change any observable outcomes. Args: api_key: GeodesicAI API key (starts with gai_) contract_a: A replay contract dict (the `replay_contract` field from a prior validate/execute_task response) contract_b: Another replay contract dict to compare against contract_a Returns: replay_match: bool — True if the top-level replay_hash matches (fully identical) contract_version_match: bool matches: dict of field_name → value, for every field that agreed mismatches: dict of field_name → {expected, actual}, for every field that disagreed summary: plain-English one-liner describing the result Interpretation of mismatches: - input_payload_hash: the two runs were fed different data - template_version: the Blueprint was upgraded between runs - solver_registry_hash: the platform itself changed between runs - canonical_trace_hash: same inputs and rules but different execution path (should never happen under determinism; indicates a platform bug) - graph_hash: DAG topology changed between runs
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  • DC Hub platform health: database backup status (last successful, age, integrity check), data freshness across 49 sources (green/yellow/red), agentic heartbeat score (0-100), MCP call volume (last hour), and DCPI recompute cadence. Useful for trust/uptime signals before relying on the platform in production. Try: get_backup_status. Do NOT use for the freshness of a specific dataset (use get_changes); this is platform/infra health, not content.
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  • The official MCP Server from Mia-Platform to interact with Mia-Platform Console

  • VaultCrux Platform — 60 tools: retrieval, proof, intel, economy, watch, org

  • List every Stimulsoft product/platform that has indexed documentation available through this MCP server. Returns a JSON array of { id, name, description } objects covering the full Stimulsoft Reports & Dashboards product line (Reports.NET, Reports.WPF, Reports.AVALONIA, Reports.WEB for ASP.NET, Reports.BLAZOR, Reports.ANGULAR, Reports.REACT, Reports.JS, Reports.PHP, Reports.JAVA, Reports.PYTHON, Server API, etc.). CALL THIS FIRST when the user's question is ambiguous about which Stimulsoft platform they are using, or when you need to pick a valid `platform` value to pass into `sti_search`. The returned platform `id` values are the exact strings accepted by the `platform` parameter of `sti_search`. This tool is cheap (no OpenAI call, no vector search) — call it freely whenever you are unsure about platform naming.
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  • Validate structured data against a Blueprint's rules. Returns PASS, FAIL, or REVIEW. The platform checks mathematical accuracy (do the numbers add up?), structural consistency (do the fields satisfy all constraints?), and semantic plausibility (do the values make sense in context?). Every result includes a determinism hash — the same input with the same Blueprint always produces the same result. Auditable, replayable, legally defensible. A Blueprint is required for meaningful validation. Without one, use create_blueprint or load_rule_pack to define your governance rules first. Args: api_key: GeodesicAI API key (starts with gai_) structured_data: The data to validate (key-value pairs) blueprint: Name of the Blueprint to validate against. Use list_blueprints to see options.
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  • Run validation and return the detailed execution trace. Shows the exact sequence of validation nodes that ran, whether each was deterministic, and the runtime of each node. Use for debugging, compliance audits, or understanding exactly what the platform checked. Different from validate: validate returns the verdict (PASS / FAIL / REVIEW) and the state vector summary. get_execution_trace returns everything validate does PLUS the per-node trace records. Use validate for normal operation; use get_execution_trace when you need to see inside the pipeline (debugging, audit prep, latency analysis). The trace is the same whether validation passes or fails — every node that ran is recorded with its inputs, outputs, and timing. Args: api_key: GeodesicAI API key (starts with gai_) structured_data: The data to trace validation for blueprint: Blueprint to validate against. Caller must own the Blueprint. Returns: status: "PASS" / "FAIL" / "REVIEW" / "ERROR" determinism_hash: cryptographic hash of inputs + rules trace: ordered list of node records, each with: node_name, node_type, deterministic (bool), runtime_ms, inputs, outputs node_count: number of nodes in the trace deterministic_count: how many nodes were deterministic state_vector: same state_vector validate returns
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  • Fetch the full record for a single creator by ID or exact platform username. Use this when you already have either: - a canonical creator UUID returned by `search_creators`, `semantic_search_creators`, `autocomplete_creators`, or `find_lookalike_creators`; or - an exact platform+username pair such as platform "instagram" and username "niickjackson". Pass `include: ['profiles']` to also receive the creator's social profile summaries when using a creator UUID. For platform+username inputs, this tool resolves through the profile endpoint and returns the profile record plus the underlying creator record, so you already get the matched profile context. Examples: - User: "Get creator 123e4567-e89b-12d3-a456-426614174000" -> call with id. - User: "Get @niickjackson on Instagram" -> call with platform "instagram" and username "niickjackson", or use `get_profile` if profile metrics are the main need. - User: "Tell me about @niickjackson and include his profiles" -> use platform "instagram" and username "niickjackson"; then use `get_profile`/`get_posts` for platform-specific metrics and content if needed. Use `lookup_profiles` for batch exact profile lookups.
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  • Get the list of legal document templates available for generation on the platform (e.g. NDA, employment agreement, stock purchase agreement). For corporate services like 83(b) filing or registered agent, use get_available_corporate_services instead.
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  • Register your agent to start contributing. Call this ONCE on first use. After registering, save the returned api_key to ~/.agents-overflow-key then call authenticate(api_key=...) to start your session. agent_name: A creative, fun display name for your agent. BE CREATIVE — combine your platform/model with something fun and unique! Good examples: 'Gemini-Galaxy', 'Claude-Catalyst', 'Cursor-Commander', 'Jetson-Jedi', 'Antigrav-Ace', 'Copilot-Comet', 'Nova-Navigator' BAD (too generic): 'DevBot', 'CodeHelper', 'Assistant', 'Antigravity', 'Claude' DO NOT just use your platform name or a generic word. Be playful! platform: Your platform — one of: antigravity, claude_code, cursor, windsurf, copilot, other
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  • Generate a visual preview of how content will appear on each platform. USE THIS WHEN: • Before publishing to see how posts will look • To validate content against platform requirements • To check character counts, hashtag limits, and media requirements Returns an HTML preview mockup for each platform with validation results: • Character count vs limit • Hashtag count (Instagram has 30 max) • Media requirement check • Platform-specific warnings and errors
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  • List Blueprints owned by the calling account. Returns each Blueprint's name, workflow identifier, mode, and field/rule/constraint counts. Use the workflow_name as the 'blueprint' parameter when calling validate. Blueprint modes — important for agents that summarize results: - "observe" (Observation mode): the platform validates data against the Blueprint's rules and returns PASS, FAIL, or REVIEW with repair suggestions. Validation is detection-only; the caller decides what to do with the result. - "enforce" (Execution mode): the platform performs the same validation AND authorizes downstream side effects. Side effects are blocked if validation fails. In this mode, rules that compute derived values (like totals) publish the platform's computed result as authoritative, so downstream consumers see the canonical value rather than the agent's. Both modes run the same rule checks. The difference is what happens after validation, not during it. Scope: only Blueprints created by the calling account are returned. Use validate / repair / etc. with the workflow_name to operate on them. Args: api_key: GeodesicAI API key (starts with gai_)
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  • Create a Blueprint — a governance contract that defines validation rules. A Blueprint tells the platform what "correct" means for your data: which fields exist, what math must hold between them, and what value ranges are acceptable. Without a Blueprint, the platform has nothing to validate against. If you don't know what rules to define, use load_rule_pack to start from a prebuilt template, or use discover_patterns to find rules from your data. Use the blueprint_guide prompt for the complete reference of all available rule types, constraint types, and configuration options. Args: api_key: GeodesicAI API key (starts with gai_) customer_name: Organization or project name (used for folder naming) workflow_name: Unique identifier for this Blueprint (used as the 'blueprint' parameter in validate) mode: "observe" (platform checks agent's work) or "enforce" (platform computes derived fields) extracted_fields: Fields the agent extracts from source data (e.g. ["vendor", "qty", "unit_cost"]) derived_fields: Fields computed from other fields (e.g. ["subtotal", "total"]) derivation_rules: Math rules defining field relationships. Available types: "add" (target = a + b), "subtract" (target = a - b), "multiply" (target = a × b), "divide" (target = a ÷ b), "round" (round field to N places), "copy" (copy source to target), "items_multiply" (per-item a × b in a list), "items_sum" (sum a field across list items). Each rule requires "type" and the relevant fields. See blueprint_guide prompt for full schema. formal_constraints: Value bounds and ratio constraints. Available types: "magnitude_anchor" (field within min/max range, requires "field", "min", "max"), "relative_anchor" (ratio a/b within tolerance, requires "a", "b", "expected_ratio", "tolerance"), "max_action_threshold" (trigger action if field exceeds threshold, requires "field", "threshold", "action"). See blueprint_guide prompt for full schema. semantic_checks: Domain-specific validation checks require_math: Validate mathematical relationships (default true) require_consistency: Check internal consistency (default true) require_coherence: Check structural coherence (default true) require_provenance: Require agents to report extraction source locations require_high_assurance: Strictest validation — feasibility, spectral, and global consistency required enable_anomaly_detection: Geometric fingerprinting to detect structural outliers enable_drift_tracking: Monitor pattern stability across batches
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  • List every Stimulsoft product/platform that has indexed documentation available through this MCP server. Returns a JSON array of { id, name, description } objects covering the full Stimulsoft Reports & Dashboards product line (Reports.NET, Reports.WPF, Reports.AVALONIA, Reports.WEB for ASP.NET, Reports.BLAZOR, Reports.ANGULAR, Reports.REACT, Reports.JS, Reports.PHP, Reports.JAVA, Reports.PYTHON, Server API, etc.). CALL THIS FIRST when the user's question is ambiguous about which Stimulsoft platform they are using, or when you need to pick a valid `platform` value to pass into `sti_search`. The returned platform `id` values are the exact strings accepted by the `platform` parameter of `sti_search`. This tool is cheap (no OpenAI call, no vector search) — call it freely whenever you are unsure about platform naming.
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