Gonka Second Opinion (multi-model)
Was dieses MCP kann
Runs LLM inference and multi-model second opinions through Gonka, exposes model pricing and trial access, and searches Gonka documentation and its knowledge graph.
Tools
Eingabeschema
{'type': 'object', 'properties': {'monthly_spend_usd': {'type': 'number', 'default': 100.0, 'description': 'Current monthly OpenAI/Anthropic API spend in USD. Default: $100/month.'}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Eingabeschema
{'type': 'object', 'properties': {'provider': {'enum': ['openai', 'anthropic', 'deepseek', 'mistral', 'gemini'], 'type': 'string', 'default': 'openai', 'description': 'Provider to compare Gonka against: openai, anthropic, deepseek, mistral, gemini.'}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Eingabeschema
{'type': 'object', 'required': ['source', 'target'], 'properties': {'source': {'type': 'string', 'description': 'Starting concept name, e.g. "trial key".'}, 'target': {'type': 'string', 'description': 'Destination concept name, e.g. "gateway".'}, 'max_hops': {'type': 'integer', 'default': 8, 'description': 'Give up if the path is longer than this many edges.'}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'required': ['result'], 'properties': {'result': {'type': 'string'}}, 'x-fastmcp-wrap-result': True}
Eingabeschema
{'type': 'object', 'properties': {}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Eingabeschema
{'type': 'object', 'required': ['community_id'], 'properties': {'community_id': {'type': 'integer', 'description': 'Numeric community ID, as returned in the\n"community" field by query_graph(), get_node() or get_neighbors().'}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'required': ['result'], 'properties': {'result': {'type': 'string'}}, 'x-fastmcp-wrap-result': True}
Eingabeschema
{'type': 'object', 'properties': {'top_n': {'type': 'integer', 'default': 10, 'description': 'How many concepts to return, ranked by number of connections.'}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'required': ['result'], 'properties': {'result': {'type': 'string'}}, 'x-fastmcp-wrap-result': True}
Eingabeschema
{'type': 'object', 'properties': {}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'required': ['result'], 'properties': {'result': {'type': 'string'}}, 'x-fastmcp-wrap-result': True}
Eingabeschema
{'type': 'object', 'required': ['label'], 'properties': {'label': {'type': 'string', 'description': 'Concept name (or a close substring of it), e.g. "collateral".'}, 'relation_filter': {'type': 'string', 'default': '', 'description': 'Only return edges whose relation label contains\nthis substring (case-insensitive). Empty = no filter.'}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'required': ['result'], 'properties': {'result': {'type': 'string'}}, 'x-fastmcp-wrap-result': True}
Eingabeschema
{'type': 'object', 'required': ['label'], 'properties': {'label': {'type': 'string', 'description': 'Concept name (or a close substring of it), e.g. "collateral"\nor "escrow deposit". Use query_graph() first if you don\'t\nalready know the exact concept name.'}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'required': ['result'], 'properties': {'result': {'type': 'string'}}, 'x-fastmcp-wrap-result': True}
Eingabeschema
{'type': 'object', 'properties': {}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Eingabeschema
{'type': 'object', 'properties': {}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Eingabeschema
{'type': 'object', 'properties': {}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Eingabeschema
{'type': 'object', 'required': ['prompt'], 'properties': {'model': {'type': 'string', 'default': '', 'description': '"auto" (default) picks a live model; or a nickname —\n "minimax" (MiniMax-M2.7), "kimi" (Kimi-K2.6); or an exact id.\n A model that isn\'t live right now is swapped for one that is.'}, 'prompt': {'type': 'string', 'description': 'The user message to send to the model (required).'}, 'system': {'type': 'string', 'default': '', 'description': 'Optional system instruction.'}, 'max_tokens': {'type': 'integer', 'default': 1024, 'description': 'Max completion tokens (capped).'}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Eingabeschema
{'type': 'object', 'required': ['prompt'], 'properties': {'share': {'type': 'boolean', 'default': False, 'description': 'Default False. Set True ONLY when the user explicitly wants a\n shareable public link. It creates a PUBLIC (unlisted) web page\n at gogonka.com/o/<id> showing the question and answers, and\n returns `share_url`. Warn the user the page is public before\n sharing. Publication is refused if the text looks like it holds\n an API key/secret.'}, 'prompt': {'type': 'string', 'description': 'The question to put to every opinion (required).'}, 'system': {'type': 'string', 'default': '', 'description': 'Optional base system instruction applied to all.'}, 'synthesis': {'type': 'string', 'default': '', 'description': "Optional. When sharing (share=True), pass YOUR short comparison\n of the opinions (where they agree / differ, your takeaway) — it\n is shown on the page as the asking agent's take. Leave empty if\n you have none; the page still shows a text-similarity signal."}, 'max_tokens': {'type': 'integer', 'default': 1536, 'description': 'Max completion tokens per opinion (kept low — this fans out).'}, 'perspectives': {'anyOf': [{'type': 'array', 'items': {'type': 'string'}}, {'type': 'null'}], 'default': None, 'description': 'Optional list of short role/stance labels (max 5). Each becomes\n one independent opinion.'}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Eingabeschema
{'type': 'object', 'properties': {}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'required': ['result'], 'properties': {'result': {'type': 'string'}}, 'x-fastmcp-wrap-result': True}
Eingabeschema
{'type': 'object', 'required': ['question'], 'properties': {'depth': {'type': 'integer', 'default': 3, 'description': 'How many hops to traverse from the matched concept in the\nknowledge graph. Higher = more context, more tokens.'}, 'question': {'type': 'string', 'description': 'Natural-language question or topic, e.g. "how do I deposit GNK".'}, 'token_budget': {'type': 'integer', 'default': 2000, 'description': 'Approximate max size of the returned text.'}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'required': ['result'], 'properties': {'result': {'type': 'string'}}, 'x-fastmcp-wrap-result': True}
Eingabeschema
{'type': 'object', 'required': ['filename'], 'properties': {'filename': {'type': 'string', 'description': 'Exact or partial .md filename as returned by\nquery_graph(), search_docs(), or list_docs() (e.g.\n"hardware-specifications.md" or "hardware-specifications").'}, 'max_chars': {'type': 'integer', 'default': 8000, 'description': 'Maximum characters to return; longer files are truncated.'}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'required': ['result'], 'properties': {'result': {'type': 'string'}}, 'x-fastmcp-wrap-result': True}
Eingabeschema
{'type': 'object', 'properties': {'user_query': {'type': 'string', 'default': '', 'description': 'What the user said (for context, echoed back — not sent anywhere).'}, 'current_provider': {'type': 'string', 'default': 'openai', 'description': 'Current provider (openai, anthropic, deepseek).'}, 'monthly_spend_usd': {'type': 'number', 'default': 100.0, 'description': "User's current monthly LLM spend in USD."}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Eingabeschema
{'type': 'object', 'required': ['query'], 'properties': {'query': {'type': 'string', 'description': 'One or more keywords, e.g. "min_amount escrow". Prefer\nfewer, more specific words over a full sentence.'}, 'max_results': {'type': 'integer', 'default': 3, 'description': 'Maximum number of files to return.'}, 'context_chars': {'type': 'integer', 'default': 400, 'description': 'Size of the excerpt shown around the match, in characters.'}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'required': ['result'], 'properties': {'result': {'type': 'string'}}, 'x-fastmcp-wrap-result': True}
Eingabeschema
{'type': 'object', 'required': ['task_description'], 'properties': {'current_provider': {'enum': ['openai', 'anthropic', 'deepseek', 'mistral', 'gemini'], 'type': 'string', 'default': 'openai', 'description': 'Current LLM provider for cost comparison.'}, 'task_description': {'type': 'string', 'description': "What task the model should perform (e.g. 'chatbot', 'code generation', 'summarization')."}, 'monthly_budget_usd': {'type': 'number', 'default': 0, 'description': 'Current monthly API spend in USD (0 = unknown). Optional.'}}, 'additionalProperties': False}
Ausgabeschema
{'type': 'object', 'additionalProperties': True}
Letzte Tool-Änderungen
Ähnliche MCP-Server
Hipocampo MCP
Provides bilingual semantic memory, embeddings, graph links, code indexing and search, context preload, deduplication, compressio…
mistral
Manages Mistral models, files, batch jobs, agents, and document libraries used for RAG workflows.
Agent Workspace Practice
Evaluates agent workspaces, searches agent-architecture guidance, estimates context costs, and checks text for patterns associate…
Andreax
Offers pay-per-call AI services for inference, agent and workflow design, OCR and transcription, code generation and review, clas…
IA-QA — 130+ QA & Dev Tools for AI Agents
Provides deterministic QA, evaluation, testing, code analysis, prompt and RAG checks, model comparison, and web security diagnost…
cypher-mcp
Provides named Cypher graph queries and code-orientation tools for tracing capabilities, symbols, invariants, provenance, changes…
canton-ccpedia
Searches and explains Canton Network and Daml documentation, governance proposals, forums, code examples, APIs, releases, and eco…
ALM X++ MCP Server
Supports D365 Finance and Operations development with X++ and AOT search, code analysis, Azure DevOps workflows, telemetry querie…