"Using another LLM for research purposes" matching MCP tools:

• trace_from_image

  kernelcad

  Trace pixel-space features from a reference photo into normalized [0..1] waypoints the agent can map to mm via a known scale anchor and feed to path().spline / path().nurbsSegment. Three backends are dispatched behind the scenes: `opencv` (deterministic; uniform-bg silhouette only), `vision-llm` (Claude vision; named points/cluttered backgrounds; caller-supplied ANTHROPIC_API_KEY), and `hybrid` (opencv silhouette + LLM-labeled named points). Default backend is `auto` — the tool picks based on the image's corner-color stddev. Accuracy honesty: opencv contour is geometrically exact; vision-LLM is typically 5–10% off on dense landmarks. Per-feature `confidence` is reported. Caller pays for any vision-LLM API spend via their own ANTHROPIC_API_KEY. Pair with the `kernelcad-trace-from-image` skill for the conversion-to-mm pipeline.

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• agents_trace_get

  dialogbrain

  Fetch the full execution detail for a single trace — tool executions, events timeline, LLM call spans (with error_message on failures). Use after `agents.traces_list` identifies a specific trace of interest (failed run, slow run, unexpected outcome). By default LLM `system_prompt` and `prompt_messages` are stripped — set `include_llm_bodies=true` to fetch them when diagnosing prompt engineering issues (emits a WARNING audit log). Set `full=true` to disable all field truncation. `completion_text` on failed LLM calls is always returned (capped at 8 KB).

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• chieflab_set_provider_key

  ChiefLab

  Store a provider API key for THIS workspace. Once stored, ChiefLab uses your key (BYOK — you pay the provider directly, no markup). Without it, ChiefLab uses its own key and bills through with a margin. Providers: gemini (image gen), resend (email), zernio (social publish), anthropic (LLM, future), openai (LLM, future). Stored encrypted at rest. Use chieflab_revoke_provider_key to remove. The key never leaves this workspace.

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• x711_llm_routing

  x711 — Universal Agent Gas Station

  Routes a prompt to the best available x711 LLM. No API keys, no rate limits. Use ONLY when you need external LLM help. Never for things you can answer from context. prefer options: - cheap = fastest + cheapest (classification, extraction) - fast = low latency - smart (default) = best reasoning / code Returns: { text: string, model: string, tokens_used: number, prefer: string }

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• district.history

  AssayChain — Mineral Intelligence (USGS and Field Runs)

  Free preview of a US or Mexico mining district record (MRDS-sourced). Returns field inventory, commodity summary, discovery year, and deposit count. Useful for domestic-sourcing due diligence (DoD/DFC project assessments, UFLPA country-of-origin research), historic production context, and mining project developer research. Full record (deposits[], geology, sources[], history narrative) requires $0.50 USDC via GET /api/historical/{country}/{state}/{county}/{district} using x402 on Base.

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• pick

  XFMS — Xpansion Framework Model Source

  Return the single best LLM for a stated purpose. Concise output, no list. Use when the user has settled on the criteria and just wants one answer.

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• createDeepResearch

  Parallel Task MCP

  Creates a Deep Research task for comprehensive, single-topic research with citations. USE THIS for analyst-grade reports, NOT for batch data enrichment. Use Parallel Search MCP for quick lookups. After calling, share the URL with the user and STOP. Do not poll or check results unless otherwise instructed. Multi-turn research: The response includes an interaction_id. To ask follow-up questions that build on prior research, pass that interaction_id as previous_interaction_id in a new call. The follow-up run inherits accumulated context, so queries like "How does this compare to X?" work without restating the original topic. Note: the first run must be completed before the follow-up can use its context.

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• askew_intel_feed

  Askew x402

  Aggregated intelligence feed combining research findings, active security threats, and live staking APY snapshot in a single call ($0.005 USDC). Sources: ChromaDB research library + Guardian log + staking.db. Best for: broad situational awareness — replaces three separate calls. Requires x402 payment on Base mainnet.

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• check_action

  icme-preflight-mcp

  Enforce a guardrail: verify an agent action against a compiled policy using formal verification. An SMT solver — not an LLM — determines whether the action satisfies every rule. Returns SAT (allowed) or UNSAT (blocked) with extracted values and a cryptographic ZK proof that the check was performed correctly. Cannot be jailbroken. 1 credit ($0.01). Requires api_key. Tip: end the action with an explicit claim like 'I assert this complies with the policy' for best extraction.

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• agents_trace_get

  DialogBrain

  Fetch the full execution detail for a single trace — tool executions, events timeline, LLM call spans (with error_message on failures). Use after `agents.traces_list` identifies a specific trace of interest (failed run, slow run, unexpected outcome). By default LLM `system_prompt` and `prompt_messages` are stripped — set `include_llm_bodies=true` to fetch them when diagnosing prompt engineering issues (emits a WARNING audit log). Set `full=true` to disable all field truncation. `completion_text` on failed LLM calls is always returned (capped at 8 KB).

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• audit_brand

  TOP GUN GEO-Lens

  Full brand visibility audit across LLM-indexed sources (Brave + Exa, 10 results). Returns a visibility score (0–100), score label, top 5 citation URLs, LLM index status, and 6 actionable GEO recommendations. Costs $1.50 USDC. For a quick snapshot at $0.05 use geo_quick_check.

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• data.pricing

  Recursive Support Oracle

  Returns structured pricing data for Recursive support agent plans. Three tiers: Basic ($49/mo), Pro ($99/mo), Premium ($299/mo). Use for quick pricing lookups without an LLM call.

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• validate_cuit

  MCP LatAm Tools

  Validates an Argentine CUIT (Código Único de Identificación Tributaria) using the official AFIP checksum algorithm. CUIT is used by companies, self-employed workers, and other entities for tax purposes. Use this tool when processing Argentine invoices, supplier registrations, B2B transactions, or any document requiring a valid Argentine tax identifier. Accepts CUIT with or without formatting (dashes). Returns whether the CUIT is valid, the entity type detected, and the cleaned CUIT.

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• submit_research

  TrenchFu

  Submit an L8 research thesis for dossier generation. Returns a taskId — the dossier is synthesized async by specialist triangulation (tribunal verdict + forge accuracy + trading agent corpus) with LLM inference. Standard depth: automated data aggregation ($0.50). Deep depth: full specialist triangulation with counter-arguments ($5.00). TRENCH whale holders get all dossiers free.

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• get_quote

  AdvocateMCP

  Quote price for a service at a business. Deterministic lookup of pricing_json_v2.ranges[]; LLM fallback on miss, labelled 'estimate' with disclaimer.

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• ask_tax_question

  taxact-mcp

  Answers tax questions using TaxAct's TY2025 tax law knowledge base. Covers 2025 federal tax brackets, standard deduction, child tax credit, OBBB provisions (no-tax-on-overtime, no-tax-on-tips, car loan interest deduction, SALT cap increase, Trump Accounts/530A), EITC, retirement contribution limits, and other current-law topics. Answers are grounded in verified IRS references, not LLM training data. No account required.

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• sales.ask

  AssayChain — Mineral Intelligence (USGS and Field Runs)

  Ask anything about this API: commodities covered, how on-chain provenance works, pricing tiers, x402 payment flow, MCP integration, or the Extract API. Also ask how to use this data as input for UFLPA compliance, EU Battery Regulation 2023/1542 sourcing disclosures, CBAM/CSDDD supply-chain research, or DoD/DFC domestic mineral sourcing assessments. Free to call. Returns a natural-language answer from a small LLM grounded on the API docs.

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• tool_data_transformer

  agent-vending-factory

  POST /tools/tool_data_transformer/run — Extracts structured JSON from raw text using a caller-supplied JSON Schema. Input: {raw_text: string, target_json_schema: object (JSON Schema draft-07)}. Output: {success, extracted_data, extraction_method, validation_passed, error}. extraction_method is one of: 'direct_parse', 'embedded_json', 'regex_extraction'. No LLM involved — pure parsing pipeline. Type coercion applied for integer/number/boolean fields. Works best with flat schemas; deeply nested structures extract less reliably via key-value pass. Cost: $0.0500 USDC per call.

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• notes_search

  DialogBrain

  Full-text search in your notebook. By default searches only your own notes. Pass filter_agent_id=<int> to search another agent's notebook, or "all" (or "*") for workspace-wide. Or list all notes for a person/thread by scope_ref_id.

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• get_capabilities

  travel

  Return the server's version, mode (human vs autonomous-agent), API base, and the list of currently-exposed tools. Useful for the LLM to confirm tool-schema compatibility before issuing a sequence of calls.

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