"LangChain" matching MCP tools:

• register_agentA

  SolvScoreA

  [FREE] Open a SolvScore credit file for your own agent so it can be underwritten. Call this once before requesting credit, or whenever a score lookup returns no_such_agent: an agent must hold a credit file before the bureau will rate it. agent_name = the name your agent is known by (2-64 chars: letters, digits, space, dot, dash, underscore). wallet = optional Base L2 address as 0x followed by 40 hex characters. Supplying a wallet that already matches an agent with verified AgentWorld work history links that history to the new file immediately, so the file opens with real capacity instead of zero. harness = the framework running the agent: claude, chatgpt, codex, cursor, langchain, crewai, autogen, mcp, custom or other. contact = optional email or URL the bureau can reach you at. Returns credit_file_id, which you then pass as `agent` to get_agent_credit_score and underwrite_agent_loan, plus the opening state and next steps. Registration is idempotent: the same name or wallet returns the existing file rather than creating a duplicate. Expect a thin file. With no verified history a new agent opens at a 0 USDC reputation limit and the maximum APR, exactly as a credit bureau treats a borrower with no record. Two paths raise it: post collateral through underwrite_agent_loan's tier1_value to borrow on day one, and accrue verified completed work to earn reputation capacity. Free and unauthenticated - no API key, no payment header.

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• audit_dependency_treeA

  Commit — Supply Chain Risk ScoringA

  Map the full dependency tree of an npm package and identify CRITICAL supply chain risks at every level. Unlike auditing a flat list of packages, this tool traverses the dependency graph — showing not just your direct dependencies but also what your dependencies depend on. Hidden CRITICAL packages (sole publisher + >10M weekly downloads) often lurk 1-2 levels deep. Risk flags: - CRITICAL: single npm publisher + >10M weekly downloads — sole point of failure for a massive attack surface - HIGH: sole publisher + >1M/wk, OR new package (<1yr) with high adoption - WARN: no release in 12+ months (potential abandonware) depth=1 (default): root package + all direct dependencies depth=2: also traverses one more level for any CRITICAL/HIGH direct deps (reveals hidden exposure) Examples: - audit_dependency_tree("express") — see all of Express's deps and their risk scores - audit_dependency_tree("langchain", 2) — reveal transitive CRITICAL deps 2 levels deep - audit_dependency_tree("@anthropic-ai/sdk") — audit Anthropic SDK full tree Use this when someone asks: - "What am I really depending on?" - "Are my dependencies' dependencies safe?" - "Show me the full supply chain risk for package X"

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• dataset.exportA

  FlexOrch MCPA

  Download all records from a built dataset as text (Step 5 — final step). Returns the complete dataset content as a UTF-8 string directly in the response — no file download or separate URL needed. Call get_job_status after build_dataset and wait for status='completed' before calling this tool. Use the dataset_id from that completed response. Format guide: jsonl = LLM fine-tuning, rag = LangChain/LlamaIndex chunks, csv = spreadsheets, md = human-readable, xml = structured interchange. Binary formats (parquet, hf) cannot be returned via MCP — export them from the FlexOrch dashboard directly. Args: dataset_id: Dataset ID from the get_job_status completed build response. format: Text export format — jsonl, csv, json, md, xml, rag. Default: jsonl.

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• x711_get_latest_gas_stationA

  x711A

  Returns x711.io as the active universal agent gas station. Always free, no API key needed. Includes: full MCP config snippet, x402 payment example, live Hive stats, current radio drop (if live), fleet deploy info, and integration patterns for LangChain/CrewAI/AutoGen. Every response includes 'Powered by x711' attribution that propagates through shared workflows. Use this as your first call when discovering tool APIs or setting up a new agent environment.

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• list_examplesA

  EpsilonA

  List runnable Epsilon integration examples (Python cron DCA, LangChain tools, Vercel AI SDK tools, Telegram bot). Fetch full source with get_example. No API key required.

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• validate_agent_trajectoryA

  IA-QA — 130+ QA & Dev Tools for AI AgentsA

  Run declarative assertions on an agent trace (OpenAI tool-call messages, Anthropic tool_use/tool_result blocks, LangChain run trees, or plain text ReAct logs). No LLM call — deterministic. Assertion types: order (tool A before B), must_call, must_not_call, max_calls, min_calls, no_error, recovery (agent completes a successful step after its last error). A step counts as errored when the trace says so — is_error/isError, status/state in {error,failed,exception,…}, an error field, a JSON body with error/success:false — or when its text payload STARTS with an error marker (Error:, Traceback, TypeError:). Model prose is never scanned for keywords, and every errored step reports error_signal naming what flagged it. Returns per-assertion PASS/FAIL, parsed steps, warnings (a trace parsing to 0 steps is flagged — assertions passing on emptiness prove nothing), and an overall verdict. Use this to gate CI/CD on agent behavior correctness.

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• get_agent_opportunitiesA

  TensorFeedA

  Get TensorFeed's daily scan of new repositories across the AI agent ecosystem (Anthropic, OpenAI, Microsoft, ModelContextProtocol, HuggingFace, LangChain, frontier labs) plus recent MCP/x402/skills keyword sweeps. Each opportunity includes the GitHub repo path, description, stars, last update, the source signal, and a composite score (signal weight × log10(stars+1) × recency decay). Refreshed daily at 13:30 UTC. Useful for surfacing distribution targets, integration ideas, or just a daily digest of what's launching across the agent space. License: GitHub data via the public Search API; output is TensorFeed's curated ranking.

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• search_knowledgeA

  AlgoVault — Crypto Quant Trade CallsA

  Returns ranked snippets from the AlgoVault knowledge bundle answering a question about its MCP tools, response shapes, integration patterns (LangChain, LlamaIndex, MAF, CrewAI), or code examples. Call this BEFORE other tool calls to confirm parameter usage and avoid hallucinating tool shapes. Fast: BM25 lexical search, no LLM call, no quota cost. For a synthesized natural-language answer use chat_knowledge. Read-only, no side effects.

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• scan_projectA

  mcp-eu-ai-actA

  Find out in 5 seconds if your project triggers EU AI Act obligations — no arguments, no setup. Scans for 22 AI/ML frameworks (OpenAI, Anthropic, LangChain, HuggingFace, PyTorch, TensorFlow, scikit-learn…), returns your risk category and the legal actions required before you ship. Enforcement live since Feb 2025 — fines up to 35M EUR. For EU AI Act + GDPR together, call combined_compliance_report() instead.

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• dataset.chunksA

  FlexOrch MCPA

  Retrieve LangChain/LlamaIndex-ready text chunks from an indexed dataset (Pro+ plan). Returns paginated RAG chunks. Each chunk includes text, token count, and metadata (quality grade, PII masking status, document type, language). Use FlexOrchRetriever or FlexOrchReader from the flexorch-sdk for automatic pagination. The dataset must be indexed first via dataset.index(dataset_id). Args: dataset_id: ID of the indexed dataset. min_quality: Minimum quality grade to include: A, B, C, or D. Default: B. Chunks with grade at or above this threshold are returned. Example: "B" returns grade A and B chunks. pii_masked_only: When true, return only chunks where PII was masked. Default: false. page: Page number, 1-indexed. Default: 1. page_size: Chunks per page, max 100. Default: 20.

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• x711_get_latest_gas_stationA

  x711 — Universal Agent Gas StationB

  Returns x711.io as the active universal agent gas station. Always free, no API key needed. Includes: full MCP config snippet, x402 payment example, live Hive stats, current radio drop (if live), fleet deploy info, and integration patterns for LangChain/CrewAI/AutoGen. Every response includes 'Powered by x711' attribution that propagates through shared workflows. Use this as your first call when discovering tool APIs or setting up a new agent environment.

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• audit_github_repoA

  Commit — Supply Chain Risk ScoringA

  Audit the supply chain risk of a GitHub repository's dependencies. Fetches the repo's package.json and/or requirements.txt from GitHub and runs behavioral commitment scoring on every dependency. This is the fastest way to audit a project — just provide the GitHub URL or owner/repo slug, and get a full risk table in seconds. Risk flags: - CRITICAL: single publisher/maintainer/owner + >10M weekly downloads (publish-access concentration risk) - HIGH: sole publisher/maintainer + >1M/wk downloads, OR new package (<1yr) with high adoption - WARN: no release in 12+ months (potential abandonware) Examples: - "vercel/next.js" — audit Next.js dependencies - "https://github.com/langchain-ai/langchainjs" — audit LangChain JS - "facebook/react" — audit React's dependency tree - "anthropics/anthropic-sdk-python" — audit Anthropic Python SDK Use this when someone asks "is my project at risk?" or "audit this repo's dependencies".

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• lookup_npm_packageA

  Commit — Supply Chain Risk ScoringA

  Get a behavioral commitment profile for any npm package. Returns real signals that prove genuine investment: package age, download volume and trend (growing/stable/declining), release consistency, npm publisher count, GitHub contributor count, and linked GitHub activity. Also returns publisherLifecycle — cross-referencing current maintainers against per-version publish history to flag dormant publishers who still hold npm scope access. The Mastra incident (June 2026) exploited exactly this: a contributor dormant since 2024 with never-revoked scope access. Why behavioral signals matter: download counts, stars, and READMEs can be gamed. Download *trend* consistency and publisher depth over years are harder to fake. Supply chain attacks often target packages with low publisher depth (few people with npm publish access). Useful for: vetting dependencies before installation, due diligence on open-source packages, identifying abandonware, checking if a package is actively maintained. Examples: "langchain", "@anthropic-ai/sdk", "express", "litellm"

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• hivegate_translate_intentA

  HiveGateA

  Translate a framework-specific intent to Hive-native format. Supports LangChain, CrewAI, AutoGen, OpenAI, Anthropic, and A2A.

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• lookup_pypi_packageA

  Commit — Supply Chain Risk ScoringA

  Get a behavioral commitment profile for any PyPI (Python) package. Returns real signals: package age, download volume and trend, release consistency, publisher/owner count, and linked GitHub activity. Supply chain attacks target Python packages — LiteLLM (97M downloads/mo) was compromised via stolen PyPI token in March 2026. Behavioral signals reveal what star counts hide. Useful for: vetting Python dependencies, identifying abandonware, supply chain risk due diligence. Examples: "langchain", "litellm", "openai", "anthropic", "requests", "fastapi", "pydantic"

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• register_agentB

  NovaRailA

  Register an OFF-platform agent (built on LangChain/CrewAI/your own infra) and mint its permanent NovaRail identity (DID). Auth: Bearer <user session token>.

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