"KLM" matching MCP tools:

• 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

• 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

• 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

• start_visibility_checkA

  LLM AuditA

  Measure whether a brand shows up when buyers ask AI assistants for a recommendation. Asks real buyer questions to OpenAI, Anthropic and Gemini and reports what they answered, not what they claim they would answer. Free, no signup, one measurement per domain every 30 days. Returns a runId: call get_visibility_check with it after about a minute. If the domain was already measured this month the response says quota_reached and includes when it ran.

  ConnectorNo auth

• search_employeeA

  Plumbline — a trust layer for LLM answersA

  Search the employment fact store for a person and return a grounded, verified answer. 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

• summarize_benefitsA

  Plumbline — a trust layer for LLM answersA

  Summarize benefit enrollments, optionally scoped (e.g. a department or plan). 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

• ask_resultA

  Plumbline — a trust layer for LLM answersA

  Fetch the published verdict for an ask_prepare session (status: pending, published, or unknown). This is the app panel's polling surface, so the verified answer renders inside the panel that prepared the question. As the connected model you normally never need it: write your draft and call ask_submit_draft. Exception: if you are asked to submit for a session whose composed prompt is no longer in your context, call this with include_prompt true to receive that session's exact prompt again, then draft and submit.

  ConnectorNo auth

• compare_departmentsA

  Plumbline — a trust layer for LLM answersA

  Compare two departments by headcount and compensation. 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

• describe_fact_capabilitiesA

  Plumbline — a trust layer for LLM answersA

  List safe metadata for the registered fact queries available on the server-pinned dataset: IDs, labels, parameters, and result shapes, never SQL. Use this when a question appears relevant but a selected route produced no facts; a route miss is not evidence that the underlying records are absent.

  ConnectorNo auth

• get_answer_queriesA

  Plumbline — a trust layer for LLM answersA

  Show the SQL behind one answer you already received, addressed by its correlation_id. Returns only the recorded fact queries (id, version, label, SQL, parameters, result schema, dataset version) for that answer: no rows, no evidence text, no answer text, no envelope, and no way to list the catalog. Recorded SQL has its arguments inlined as literals, so a query that filters on a specific value discloses that value; the built-in workforce queries inline only snapshot ids. Requires the persisted compliance log and the same session that produced the answer. Absent from the no-auth public demo.

  ConnectorNo auth

• get_visibility_checkA

  LLM AuditA

  Collect the result of a measurement started with start_visibility_check. Returns status running while the providers are still answering, so poll every 20 to 30 seconds. When ready it reports a verdict, how many of the buyer questions the brand won, which competitors were named instead, and which providers actually answered.

  ConnectorNo auth

• a2awire_guideA

  New LLM Release Tracker — buy per-query in-session (llmwatch)B

  ✅ No API key needed — call this now. Navigator for the full A2AWire tool surface. Call with no topic for the categorized catalog of every callable tool (name + one-liner). Pass topic=escrow|negotiate|hire|pay|board|onboard|owner|foundry|wallet|discovery|sell|buy|benchmark for a recommended call sequence. Every listed tool is callable via tools/call by name — tools/list shows only always-on essentials.

  ConnectorNo auth

• get_xlm_sentimentA

  Echo Sentiment — XLM Market SentimentA

  Composite XLM market sentiment in [-1, 1] (positive = bullish), with per-component breakdown, live XLM price, and bid/ask spread. Output is a JSON string with keys: sentiment, sentiment_source, components, price_usd, mid, spread_pct, ts.

  ConnectorNo auth

• get_manager_chainA

  Plumbline — a trust layer for LLM answersA

  Return the management/reporting chain for an employee. 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

• check_earningsA

  New LLM Release Tracker — buy per-query in-session (llmwatch)B

  Check how much I have earned and what is pending. Returns lifetime USDC earned as seller (released escrows plus claimed rewards), in-flight pending amounts, unclaimed claim-later rewards such as the admission mission's, payout-address balance, buyer spend summary, and first-agent reputation. Read-only; earnings settle non-custodially to your withdrawal address on release.

  ConnectorNo auth

• briefA

  Pyfile LLM & Data APIB

  Composite analyst brief: gathers live market facts (crypto prices, DeFi yields, Base block height) and returns an LLM-written brief with headline, key points, and a take. Requires a Bearer key.

  ConnectorNo auth

• get_purchase_flowA

  LLM Red-Team ScannerA

  Machine-readable autonomous purchase procedure: how an agent pays 3 USDT and receives the PRO download with no human in the loop (callback push + poll fallback).

  ConnectorNo auth

• get_ai_act_transparencyA

  Plumbline — a trust layer for LLM answersA

  Return the Art. 13 transparency descriptor: intended purpose, capabilities, limitations, human oversight, logging mechanism, and article mapping.

  ConnectorNo auth