"LLVM" matching MCP tools:

• estimate-llm-costA

  OptimTokenA

  Cost a workload with EXACT numbers the caller supplies: arbitrary token counts per request and any monthly volume, not just the 10k/100k/1m presets the other cost tools use. Use this for 'about 800 in and 200 out, 4 million calls a month', or to price one named model across every use-case profile. To compare 2-4 named models like for like at a preset volume, use compare-models-side-by-side instead. Provide a model name to get detailed cost breakdowns, or compare costs across all use case presets. Each figure comes twice: list price, and the optimized price achievable with prompt caching and the batch API. IMPORTANT: Report all cost figures EXACTLY as returned. Do NOT add commentary or recommendations beyond the data.

  ConnectorNo auth

• llmA

  VaayaA

  Ask a DIFFERENT LLM a question and get its answer, billed per token from the Vaaya wallet (model cost + 3%, usually a fraction of a cent). Use it to get a second opinion from a rival model, cross-check an answer, summarize a huge blob cheaply, or query a specific model the user names (Kimi, GPT, Gemini, Claude, DeepSeek, and 300+ more). `model` accepts 'auto' (default: short prompts go cheap, long go mid), 'cheap' | 'mid' | 'best' tiers, or any exact OpenRouter slug like 'moonshotai/kimi-k3'. Typical costs: cheap tier well under 0.1 cents, best tier 1-3 cents per call. Not for the conversation you are already having — it is a one-shot ask to another model.

  ConnectorNo auth

• cross_checkA

  Plumbline — a trust layer for LLM answersA

  Corroborate a figure you obtained (e.g. from run_sql) against the verified fact store: it re-derives the named value_key from the same SQL the verified pipeline uses and compares. AGREE lifts an otherwise-unverified session to 'corroborated'; DISAGREE marks a CONFLICT (worse than unverified). This is the only way a raw figure earns trust back, and it can only ever agree with a number the fact store itself computed. Resolvable keys cover the single-snapshot fact concepts plus temporal and per-department growth keys (e.g. indexed_people.<dept>); per-question keys (a named department's median, one person's direct-report count) return unmapped.

  ConnectorNo auth

• find_payroll_anomaliesA

  Plumbline — a trust layer for LLM answersA

  Surface payroll and deduction anomalies in the latest snapshot. NOTE: internal drafting is disabled on this deployment. If your client supports MCP sampling, this tool asks YOUR model to draft in the same call (verified server-side); otherwise it returns an explicit refusal, and you should use ask_prepare then ask_submit_draft to draft with your own model.

  ConnectorNo auth

• get_evidence_documentA

  Plumbline — a trust layer for LLM answersA

  Show ONE retrieved evidence document behind an answer you already received, addressed by that answer's correlation_id plus a document_id from its evidence_documents references. Returns the full stored document (title, body, metadata, embedding_text) with the retrieval rank and scores the answer recorded; never the raw embedding vector. Only documents the addressed answer actually recorded resolve: there is no fetch-by-id in general and no way to browse the store. Requires the persisted compliance log and the same session that produced the answer. Absent from the no-auth public demo.

  ConnectorNo auth

• dcl_evaluate_batchA

  DCL Trust Oracle — AI/LLM Output Audit (x402 MCP)A

  PRE-ACTION Bulk Processing ($0.10). Evaluates a list of items in one call; each item is a dict shaped {"response": str, "policy"?: str}, where policy defaults to "default" if omitted and may be any built-in policy name (default, strict, anti_jailbreak, safety, content_quality). Each item gets its own independent COMMIT/NO_COMMIT verdict via the same logic as the matching single-item evaluate_* tool; results are returned in input order under `results`, plus a shared `batch_id`. Capped at 200 items per call — oversized batches are rejected. Use this instead of multiple single-item evaluate_* calls when checking several responses — optionally against different policies — in one priced call rather than paying per item separately.

  ConnectorNo auth

• estimate_costA

  LLM AbacusA

  估算一段文本或指定 token 数在某个大模型上的**输入侧**调用成本，以人民币 ¥ 计价。text_or_tokens 支持两种模式：传自然语言文本时自动估算 token 数（中文按字符、英文按词分别计），传纯数字字符串时直接作为已知 token 数使用。返回：所用估算方法、token 数、按原价计的输入成本；若该模型提供缓存输入价，另给出缓存命中成本、相对原价的节省百分比与倍数差价；末尾附该模型的输入 / 输出 / 缓存输入单价（每百万 tokens）。⚠️ 本工具只计算输入成本，不含输出 token 费用——要估算完整往返成本，需另取输出单价自行相加。

  ConnectorNo auth

• compare-llm-modelsA

  OptimTokenA

  Browse and filter the whole LLM catalogue and get back a ranked table: price, quality (ELO), efficiency and capabilities. Use this when the user wants to SEE THE FIELD — 'show me models under $1/1M', 'which providers have vision models', 'list open-weight models above ELO 1300'. For a single PICK under a budget use recommend-llm-model; to weigh 2-4 NAMED models against each other use compare-models-side-by-side. Prices come from optimtoken.optimnow.io where reachable; the response's `provenance` says which tier served them and whether they are vendor-verified. Filter by provider, price tier (category), openness, capability, price range, or minimum ELO score. Optionally enrich with business metrics for a use case. Price tier and openness are independent: a model can be Frontier-priced and open-weight at once. Reports both list-price cost and the optimized cost achievable with prompt caching and the batch API. IMPORTANT: Report all prices, costs, and scores EXACTLY as returned. Do NOT add commentary, opinions, or recommendations beyond what the data shows. Present the results as a table and let the user draw conclusions.

  ConnectorNo auth

• ask_prepareA

  Plumbline — a trust layer for LLM answersA

  BYO step 1 of 2: run the server-side trust plane for a business question (plan, SQL-verified facts, evidence retrieval, answerability gate, prompt composition) and return the composed prompt plus a single-use prepare_key (a signed handoff token; echo it back VERBATIM and in full, or use the short ask_id alias; in prose always say the ask_id, never the token). Read-only: prepare holds no server state and is safe to run without confirmation — the receipt is minted only at ask_submit_draft. Pass the user's question VERBATIM: do not expand, narrow, or reword it — the server plans coverage itself, and an unrequested rewrite misleads the user about what was asked. If you must revise it (e.g. to fold in essential conversation context), you MUST also pass the user's exact wording in original_question so the panel can disclose the revision. YOU (the connected model) then write the draft answer from that prompt and submit it with ask_submit_draft — generation happens on your side, so no LLM credential ever reaches this server. An unanswerable question still receives a prompt for reference; its draft is force-refused at submit and can never earn a verified verdict. Only a policy refusal stops at prepare. Recommended drafting path on this deployment.

  ConnectorNo auth

• dcl_commitA

  DCL Trust Oracle — AI/LLM Output Audit (x402 MCP)A

  FINAL-STEP Leibniz Layer Crypto Commit ($0.01). Writes a trading/agent decision to the append-only Leibniz Layer audit chain and returns a Merkle-proof-style receipt: `tx_hash` (proof of this specific commit), `chain_hash` (the previous commit's hash, linking this one into the chain), and `chain_depth` (this commit's position in the chain). Unlike the evaluate_* tools, this call has no pass/fail verdict of its own — it always succeeds and simply seals the decision. Passing `prior_checks` is optional but recommended: it records which earlier pipeline steps (firewall/wallet/trade/MEV) this specific commit is downstream of, in one auditable record. Always run this LAST, after every other crypto-suite check has passed.

  ConnectorNo auth

• search_docsA

  Plumbline — a trust layer for LLM answersA

  Search Plumbline's own product and architecture documentation (how the verification, refusal, and trust layer itself works). Returns ranked, citable doc passages; does NOT touch employment data, and returns an empty result set honestly when nothing matches.

  ConnectorNo auth

• code_ir_equivA

  Affine Earth Math Court RemoteB

  The Code Court's first move: are two implementations equivalent by the exact value multiset of their LLVM IR constants? THE PAYLOAD IS THE CODE FILE — submit two whole source files (left_file and right_file, each {path, content}, or files as an array of two). The result leaf is those same whole files under files, plus WIN (constant-pool digests identical), CODE_IR_DIVERGED (naming the first divergent value), NOT_KNOWN (nothing mineable), or a REFUSED_* refusal. Stateless and content-addressed — the same two files rule identically on every cell, so a reviewer re-checks by re-digesting, not by re-reading the diff. Compares the constant pool (a necessary, strong condition for the numeric kernels the corpus is built from), not full behavioural equivalence.

  ConnectorNo auth

• dcl_evaluate_fastA

  DCL Trust Oracle — AI/LLM Output Audit (x402 MCP)A

  FAST Pre-Action Audit ($0.01). Runs the response through the server's "default" policy: a substring check against 3 forbidden phrases ("ignore previous instructions", "jailbreak", "bypass safety") with a 0.7 minimum-confidence threshold. Each forbidden match found costs 0.4 confidence; if confidence falls below 0.7, or any match is found, the verdict is NO_COMMIT and `reason` lists which phrase triggered it. Otherwise COMMIT. Use this as the default low-cost first-pass gate before a risky agent action; switch to dcl_evaluate_strict for a broader, higher-bar check, or to dcl_evaluate_jailbreak / dcl_evaluate_safety / dcl_evaluate_quality for a narrower, single-topic check instead of the general-purpose default policy.

  ConnectorNo auth

• dcl_evaluate_strictA

  DCL Trust Oracle — AI/LLM Output Audit (x402 MCP)A

  STRICT Pre-Action Audit ($0.05). Runs the response against a broader, higher-bar "strict" policy: the union of all forbidden phrases from the default, anti-jailbreak, and safety policies (8 phrases total), with a 0.85 minimum-confidence threshold instead of the default policy's 0.7. Each matched phrase costs 0.4 confidence; if confidence falls below 0.85, or any phrase matches, the verdict is NO_COMMIT with `reason` listing every match found. Use this instead of dcl_evaluate_fast when the cost of a false COMMIT is high — e.g. before an irreversible or high-stakes agent action — since it catches jailbreak- and safety-adjacent phrasing that the plain default policy would miss.

  ConnectorNo auth

• dcl_evaluate_jailbreakA

  DCL Trust Oracle — AI/LLM Output Audit (x402 MCP)A

  PRE-ACTION Instruction Adherence Check ($0.02). Runs the "anti_jailbreak" policy: a substring check against 6 forbidden phrases ("ignore previous instructions", "jailbreak", "bypass safety", "pretend you are", "act as if", "DAN") with a 0.8 minimum-confidence threshold — each match costs 0.4 confidence. Returns COMMIT if no phrase matches and confidence stays at or above 0.8, otherwise NO_COMMIT with `reason` listing the matched phrase(s). Use this as a targeted, cheaper check when the concern is specifically prompt-injection / persona-hijack risk; use dcl_evaluate_strict instead when you also want safety- and default-policy phrases covered in the same call.

  ConnectorNo auth

• dcl_evaluate_safetyA

  DCL Trust Oracle — AI/LLM Output Audit (x402 MCP)A

  PRE-ACTION Baseline Safety Check ($0.01). Runs the "safety" policy: flags 2 forbidden disclaimers ("I cannot be held responsible", "no guarantees") and additionally REQUIRES the substring "AI" to appear somewhere in the response — missing it costs 0.2 confidence even with no forbidden phrase present. Minimum confidence is 0.75. Returns NO_COMMIT if confidence drops below 0.75, with `reason` naming the forbidden phrase found or the missing required pattern. Use this when you specifically need to confirm an AI-disclosure marker is present and the two disclaimer phrases are absent — not as a general-purpose safety net; for broader coverage use dcl_evaluate_fast or dcl_evaluate_strict instead.

  ConnectorNo auth

• dcl_evaluate_qualityA

  DCL Trust Oracle — AI/LLM Output Audit (x402 MCP)A

  PRE-ACTION Content Quality & Drift Check ($0.03). Runs the "content_quality" policy: flags 12 absolutist or unverifiable-claim phrases (e.g. "guaranteed returns", "100% accurate", "studies show", "without a doubt") with a 0.85 minimum-confidence threshold — the highest bar of any single-policy tool. Returns NO_COMMIT if any phrase matches or confidence falls below 0.85, with `reason` listing the matched phrase(s). Use this to catch overconfident or unsubstantiated claims in generated content — a different concern from jailbreak or safety phrasing — e.g. before publishing agent-written copy or reports.

  ConnectorNo auth

• dcl_evaluate_walletA

  DCL Trust Oracle — AI/LLM Output Audit (x402 MCP)A

  POST-ACTION Wallet Secret Guardian ($0.02). Scans for BIP-39 seed phrases (12 or 24 consecutive wordlist words), raw hex or WIF-format private keys, Ethereum/Bitcoin wallet addresses, and API keys/bearer tokens appearing near wallet/custody/signing terminology. Any finding results in NO_COMMIT — wallet secrets have no safe threshold, unlike other DCL evaluators. Returns a `sanitized_output` with all matches redacted (null if nothing was found) and a masked `redacted_sample` per finding — the real value is never returned or stored server-side.

  ConnectorNo auth

• dcl_evaluate_output_sanitizerA

  DCL Trust Oracle — AI/LLM Output Audit (x402 MCP)A

  FINAL-GATE Output Sanitizer ($0.02). Post-processing checkpoint that strips secrets/credentials, PII, crypto material (seed phrases, private keys, wallet addresses), internal network details (private IPs, MAC addresses, .internal/.local/.corp hostnames), and unsafe shell/SQL/path-traversal fragments from a raw model response — plus a narrow, high-precision safety net for direct self-harm-instruction-seeking and targeted-harassment phrasing (not a general toxicity classifier). Returns a single `sanitized_output` with every match replaced by `[REDACTED]`; use that instead of the original whenever verdict is NO_COMMIT. Run this as the LAST gate before a response reaches its destination — after `dcl_evaluate_jailbreak_crypto`/other input-side checks have already run, and immediately before `dcl_commit` seals the final decision. Internally re-uses the same detection tables as `dcl_evaluate_secrets`/`dcl_evaluate_pii` for the secrets/PII categories, so results stay consistent with those tools.

  ConnectorNo auth

• read_urlA

  agent-web — URL to LLM-ready markdown: a polite, robots-respecting web page reader (free)A

  Fetch one publicly reachable URL and return clean, LLM-ready markdown (title + page description + word count + markdown). HTML pages are extracted to markdown — including HTML tables, which become GitHub-flavored Markdown tables; URLs pointing straight at a Markdown or plain-text document (raw READMEs, llms.txt, docs) are passed through verbatim. Polite by design: honors the origin's robots.txt for our user-agent, identifies honestly, read-only GET, never bypasses anti-bot/CAPTCHA/paywalls. Free. JavaScript-rendered pages are not supported yet.

  ConnectorNo auth

• recommend-llm-modelA

  OptimTokenA

  Pick a model. Returns a ranked top 3 for one workload under optional constraints, each with a per-constraint satisfied/violated breakdown as the evidence. Use this when the user wants an ANSWER rather than a table — 'what should I use for support tickets under $500 a month'. To browse or filter the whole catalogue instead, use compare-llm-models. Constraints: (monthly budget, minimum ELO, required capability, self-hostability). Returns a top 3 as structured facts — efficiency rank, ELO, list and optimized cost, FinOps flag, volatility, and a per-constraint satisfied/violated breakdown. When nothing satisfies every constraint the query is reported as over-constrained and the nearest misses are returned instead, each carrying the constraint it failed. IMPORTANT: Report the returned facts EXACTLY. The ranking is already computed — do not re-rank, and do not present a near miss as if it satisfied the constraints.

  ConnectorNo auth