"gemini 2.5" matching MCP tools:

• pipeworx_feedback

  Gemini Crypto

  Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.

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• get_deploy_result

  quantifyme

  Wait for a `one_shot` deploy to finish and return its final result. `one_shot` returns a job_token immediately and the LIVE CARD already streams progress and renders the interactive backtest chart itself. Call this ONCE with the token to get the final numbers as TEXT so you can summarize them — it does NOT render another card (no need for get_model_chart). It BLOCKS until the deploy finishes (or ~2.5 min); on timeout it returns ok:false + pending:true — call it again with the same token. IMPORTANT: if `source == "community"`, the deploy used a PRE-EXISTING strategy by `@author` — tell the user that, share the `live_url` as the Live dashboard link, and ask whether they'd like to GENERATE A CUSTOM strategy instead. Use the `note` field as your guide. Args: job_token: the token returned by `one_shot`. Returns: dict with: ok, stem, model, live_url, symbol, timeframe, channels (list), stats:{ret, wr, pf, n, mdd} (out-of-sample test-split metrics — SHOW THESE), source ("community" | "generated"), author (community username if any), author_url + strategy_url (render @author and "pre-existing strategy" as those Markdown links), community_id, suggest_custom (bool), and note (a ready instruction — follow it). On failure: {ok:false, error} (or {pending:true}).

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• asterwise_get_western_horoscope

  Asterwise

  Fetches an AI-synthesised Western sun-sign horoscope for a chosen horizon and returns structured guidance fields plus metadata about the model and period. SECTION: WHAT THIS TOOL COVERS Calls the upstream western horoscope service for a tropical sun sign and a period of daily, weekly, monthly, or yearly. Uses the tropical zodiac (not sidereal). Content is grounded in current sky aspects, slow planet positions, and the solar season — not Vedic transit rules. It does not compute a personal natal chart, divisional charts, or dasha — only sign-level tropical transit-flavoured copy tied to the requested horizon. No remedy field — Western tradition has no planetary remedy system. SECTION: WORKFLOW BEFORE: None — this tool is standalone. AFTER: asterwise_get_western_natal — if the user needs a personalised tropical chart beyond sign-general copy. SECTION: INPUT CONTRACT period is constrained to the tool schema enum (daily, weekly, monthly, yearly). sun_sign accepts English zodiac names only (Aries, Taurus, Gemini, Cancer, Leo, Virgo, Libra, Scorpio, Sagittarius, Capricorn, Aquarius, Pisces). No Sanskrit aliases — this is Western astrology. response_format selects JSON vs markdown rendering only. SECTION: OUTPUT CONTRACT data.content: headline (string) narrative (string) love (string) career (string) money (string) body (string) power_window (string) caution_window (string) closing_message (string) phases[] (monthly only — array of phase objects with phase_number, start_date, end_date, title, narrative) year_theme (string — yearly only) chapters[] (yearly only — array of chapter objects with chapter_number, start_date, end_date, title, narrative) auspicious_months[] (yearly only — string array of month names) landmark_dates[] (yearly only — array of {date, event} objects) data.model_used (string — AI model version label) data.generated_at (string — ISO UTC) data.period_key (string — YYYY-MM-DD for daily; YYYY-W## for weekly; YYYY-MM for monthly; YYYY for yearly) data.horizon (string — 'daily', 'weekly', 'monthly', or 'yearly') data.sun_sign (string — lowercase English, e.g. 'aries') data.zodiac_type (string — 'western') SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data — no fields are added, removed, or filtered by either mode. SECTION: COMPUTE CLASS FAST_LOOKUP SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): — Invalid period enum or other Pydantic field violations on the tool schema → MCP INVALID_PARAMS INVALID_PARAMS (upstream): — Unknown or unsupported sun_sign → MCP INTERNAL_ERROR at the tool layer (upstream rejection). INTERNAL_ERROR: — Any upstream API failure or timeout → MCP INTERNAL_ERROR — Horoscope not yet generated for the current period → MCP INTERNAL_ERROR with status not_generated Edge cases: — Sun-sign content only; not a substitute for birth-chart analysis. — If a period's horoscope has not yet been generated by the cron, returns 404 upstream (surfaces as INTERNAL_ERROR). — No remedy field in western horoscopes by design. SECTION: DO NOT CONFUSE WITH asterwise_get_horoscope — Vedic Moon-sign horoscope using sidereal zodiac, not Western tropical sun-sign. asterwise_get_western_natal — full personalised tropical chart from birth data, not sign-general editorial copy.

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• asterwise_get_horoscope

  Asterwise

  Fetches an AI-synthesised Moon-sign horoscope for a chosen horizon and returns structured guidance fields plus metadata about the model and period. SECTION: WHAT THIS TOOL COVERS Calls the upstream horoscope service for a lunar sign (English or Sanskrit input accepted; response normalises moon_sign to lowercase English) and a period of daily, weekly, monthly, or yearly. It returns narrative and checklist-style content for life areas, remedy, and timing flavour text. It does not compute a personal natal chart, divisional charts, or dasha — only sign-level transit-flavoured copy tied to the requested horizon. SECTION: WORKFLOW BEFORE: None — this tool is standalone. AFTER: asterwise_get_natal_chart — if the user needs a personalised chart beyond sign-general copy. SECTION: INPUT CONTRACT period is constrained to the tool schema enum (daily, weekly, monthly, yearly). moon_sign accepts Sanskrit (Tula, Vrischika, Karka, Simha, Kanya, Dhanu, Makara, Kumbha, Meena, Mesha, Vrishabha, Mithuna) or English (Libra, Scorpio, Cancer, Leo, Virgo, Sagittarius, Capricorn, Aquarius, Pisces, Aries, Taurus, Gemini); resolution is upstream. response_format selects JSON vs markdown rendering only. SECTION: OUTPUT CONTRACT data.content: do[] (string array) body (string) love (string) avoid[] (string array) money (string) career (string) remedy (string) headline (string) narrative (string) open_loop (string) data.model_used (string — AI model version label) data.generated_at (string — ISO UTC) data.period_key (string — YYYY-MM-DD for daily; identifier for other horizons) data.horizon (string — 'daily', 'weekly', 'monthly', or 'yearly') data.moon_sign (string — lowercase English, e.g. 'libra') SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data — no fields are added, removed, or filtered by either mode. SECTION: COMPUTE CLASS FAST_LOOKUP SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): — Invalid period enum or other Pydantic field violations on the tool schema → MCP INVALID_PARAMS INVALID_PARAMS (upstream): — Unknown or unsupported moon_sign → MCP INTERNAL_ERROR at the tool layer (upstream rejection). INTERNAL_ERROR: — Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Sign-level content only; not a substitute for birth-chart analysis. SECTION: DO NOT CONFUSE WITH asterwise_get_natal_chart — full personalised sidereal chart from birth data, not Moon-sign editorial copy. asterwise_get_gochar — nine-planet transit snapshot vs natal chart for today, not AI horoscope prose.

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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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• remember

  Gemini Crypto

  Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

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• tag_file

  Gemina FileTag

  Run the FileTag pipeline against a previously uploaded slot. The ``file_id`` comes from a prior ``files_create_upload`` call. The server validates the uploaded blob (size, content-type, optional SHA-256), atomically consumes the slot, runs the FileTag extraction (renaming + metadata embedding), and returns the structured result with the extracted metadata, the suggested filename, the ``enriched_file_url`` (short-lived signed URL to the renamed copy with metadata embedded into document properties), and a ``next_action`` recipe (``http_get_and_save``) telling the agent to download that URL and save it as the suggested filename -- act on it unless the user explicitly asked for metadata only. Each slot is single-use; reserve a new slot with ``files_create_upload`` to retry.

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• search_concept

  Source Library

  Conceptual / semantic passage search across the whole library. Use when the modern term won't literally appear in historical texts — e.g. "distributed cognition" maps to passages about active intellect, art of memory, wax tablet metaphors; "social contract" maps to pre-Hobbesian discussions of consent and authority. Ranks passages by cosine similarity on Gemini embeddings (768d), so paraphrases and conceptually adjacent phrasings match even when no keyword overlaps. ORIENTATION HINT: if the user named a specific author or work, prefer get_book (returns the book's AI summary + chapter outline) — semantic search is expensive and best reserved for cross-corpus discovery. Prefer search_translations for literal phrases or distinctive single terms; use search_concept when the concept matters more than the wording. Similarity calibration: 0.70+ is a strong match, 0.55–0.70 is worth reading but verify, below 0.55 is mostly conceptual drift. Set max_per_book to diversify results across many books rather than cluster on one source. Each passage carries a snippet_type — quote only "translation" snippets, never "summary". Cross-cultural tip: for pre-modern or non-Western topics, also try source-tradition vocabulary — e.g. for seminal economy try "jing preservation" or "bindu yoga" or "istimnāʾ"; for masturbation try "mollities" (Latin) or "hastamaithuna" (Sanskrit) or "shouyin" (Chinese). The corpus is indexed via period translations that use tradition-internal terminology, so adjacent/euphemistic terms often surface material that modern English keywords miss.

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• get_analyst_config

  TunnelMind Data API

  Returns the TunnelMind analyst config bundle. Configures any LLM (Claude, GPT, Gemini, local) to behave as a TunnelMind analyst that knows the data graph, follows the 5-call golden path, and surfaces attestation_tier on every claim. The bundle is signed inline (Ed25519, key_id from /.well-known/receipt-signing-key.json). Add `?receipt=true` to wrap the response in a Receipt v1.0 envelope for end-to-end audit. Use this tool when: - You want to configure a new LLM runtime to act as a TunnelMind analyst - You want to verify the system prompt you're running matches what TunnelMind serves - You're building a BYOM (bring-your-own-model) deployment and need the canonical config Do NOT use this tool when: - You want to call individual TunnelMind data tools — use the tools directly - You want to verify a specific receipt — use check_receipt_revoked or @tunnelmindai/receipt-verify Inputs (all optional): - `surface` (query): "data" (default, full surface), "scry", or "sigil" - `version` (query): pin a specific bundle version (e.g. "1.0.0" or "1" for latest 1.x.y) - `receipt` (query): "true" to wrap the response in a signed Receipt v1.0 envelope Content negotiation (via Accept header): - `application/json` (default) — full bundle JSON - `text/markdown` — system prompt only (Anthropic flavor) - `application/vnd.anthropic.config+json` — Anthropic-shaped subset - `application/vnd.openai.config+json` — OpenAI-shaped subset Returns: - `version`, `schema`, `issuer`, `surface`, `surface_label` - `system_prompts.{anthropic,openai,generic}` — three encodings of the same semantic prompt - `tools.surface_subset` — array of operationIds for this surface (null = all) - `response_format` — JSON Schema the analyst's verdicts must conform to - `attestation_tiers` — the 4-tier vocabulary (self_asserted → silicon_root) - `graph_state` — live corpus counts at serve time - `references` — URLs to the rest of the open-protocol layer - `bundle_signature` — inline Ed25519 signature for offline verification - `pin_recommended` — stable supply-chain identifier (survives hourly graph_state updates) Headers: `X-Bundle-Version`, `X-Pin-Recommended`, `ETag`, `X-RateLimit-*`. Cost: - Free, anonymous-accessible. Rate-limited on a SEPARATE counter from data-API calls (`cfg:ip:<ip>` identity) so a config refetch loop can't burn your data quota. Latency: - Typical <100ms (cached); cold fetch <500ms (live Supabase counts).

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• audit_brand_visibility

  Ninar AI

  Run a System of Record adjudication on an entity surfaced by an AI engine (e.g. is 'Banner Life' a valid PMI competitor to Enact?). Uses dual-model consensus (Haiku 4.5 + Gemini Flash, escalating to Sonnet 4.6 + Gemini Pro on disagreement) against a versioned taxonomy. Returns the Why Drawer headline, audit trail, and per-model judgments. Pro plan or higher required.

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• calc_statistics

  Math MCP Learning

  Perform statistical calculations on a list of numbers. Available operations: mean, median, mode, std_dev, variance Note: Use this tool to compute descriptive statistics over a list of numbers. To evaluate a single mathematical expression, use the calculate tool instead. Examples: statistics([1.0, 2.5, 3.0, 4.5, 5.0], "mean") # Returns 3.2 statistics([1.0, 2.5, 3.0, 4.5, 5.0], "std_dev") # Returns ~1.58

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• get_status_summary

  TensorFeed

  Get the live operational status of every major AI service tracked by TensorFeed (Claude, ChatGPT, Gemini, Perplexity, Cohere, Mistral, HuggingFace, Replicate, Midjourney, etc). Polled every 2 min. Returns operational | degraded | down per service plus the most recent incident.

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• predict_moment_quality

  Trillboards DOOH Advertising

  Predict the VAS (Viewability Attention Score) a specific creative would achieve at a given moment, based on historical data and causal modeling. Uses the CausalPredictionService which: 1. Embeds the moment description to find historically similar moments 2. If >= 5 similar moments exist with the same creative, uses weighted-average prediction 3. If insufficient data, falls back to Gemini generative prediction 4. Always decomposes the prediction into causal factors WHEN TO USE: - Evaluating whether a creative will perform well in a specific context - A/B testing creative placement hypotheses before committing budget - Understanding which causal factors drive VAS for a creative - Comparing expected performance across different moment types RETURNS: - prediction: { predictedVAS (0-1), confidence (0-1), method ('historical'|'model'), sampleSize } - causal_factors: { audienceMatch, contextMatch, attentionState, socialPotential } (each 0-1) - metadata: { creative_id, moment_description } - suggested_next_queries: Follow-up queries EXAMPLE: User: "How would a coffee ad perform at a transit station during morning rush?" predict_moment_quality({ moment_description: "transit venue, morning commute, 12 viewers, high attention, mostly 25-34 age range", creative_id: "coffee-brand-morning-30s" })

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• resolve_entity

  Gemini Crypto

  Resolve a user-spoken name to the canonical/official identifiers other tools require as input. Use FIRST when you have a name but need an ID. SUPPORTED TYPES: "company" (returns ticker + 10-digit CIK + company_name from SEC EDGAR + pipeworx://edgar/company/{cik} citation URI; accepts ticker, CIK, or company name as input — auto-disambiguated), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/{rxcui} citation; accepts brand or generic name). Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.

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• register

  AI Agents Overflow

  Register your agent to start contributing. Call this ONCE on first use. After registering, save the returned api_key to ~/.agents-overflow-key then call authenticate(api_key=...) to start your session. agent_name: A creative, fun display name for your agent. BE CREATIVE — combine your platform/model with something fun and unique! Good examples: 'Gemini-Galaxy', 'Claude-Catalyst', 'Cursor-Commander', 'Jetson-Jedi', 'Antigrav-Ace', 'Copilot-Comet', 'Nova-Navigator' BAD (too generic): 'DevBot', 'CodeHelper', 'Assistant', 'Antigravity', 'Claude' DO NOT just use your platform name or a generic word. Be playful! platform: Your platform — one of: antigravity, claude_code, cursor, windsurf, copilot, other

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• fillin_answer

  Fillin

  Synthesized post-cutoff answer with inline citations. Use this when your model is small / cheap / weaker at tool-result synthesis (Llama, Gemini Flash, Mistral, Nemotron, Qwen). Fillin runs a server-side LLM pass over the retrieved post-cutoff documents and returns a 150-250 word answer with [title](url) citations already embedded — you can quote it directly. Premium models (Opus, Sonnet, GPT-4o) usually get better results from `fillin_query` and synthesizing themselves, but this tool works for any caller. Costs more than fillin_query because of the synthesis pass. Returns: A dict with: - answer: the synthesized paragraph (str | None) - citations: list of {title, url} extracted from the answer - corpus_match: "strong" | "weak" | "none" — quality of retrieval - top_score: float — top reranked similarity score - model: the synthesizer model used (e.g. claude-haiku-4-5) - reason: set when answer is None (e.g. "no_relevant_docs") - results: raw post-cutoff documents (same shape as fillin_query) - cutoff, query, gap_days: echoes for context

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• validate_claim

  Gemini Crypto

  Fact-check, verify, validate, or confirm/refute a natural-language factual claim or statement against authoritative sources. Use when an agent needs to check whether something a user said is true ("Is it true that…?", "Was X really…?", "Verify the claim that…", "Validate this statement…"). v1 supports company-financial claims (revenue, net income, cash position for public US companies) via SEC EDGAR + XBRL. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported), extracted structured form, actual value with pipeworx:// citation, and percent delta. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → numeric comparison).

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• insurelink_charge

  InsureLink

  Lovable-friendly entry point: same x402 quote → pay → call loop as `pay_then_call`, plus vertical-aware pricing. Pass `vertical` (logistics, legal, oracles, banking, fintech, healthcare, insurance, government, defense, …) and the upstream x402 server re-prices each accepts[] entry by tier (standard 1× / regulated 1.5× / sovereign 2.5× / defense 3×). Pass `attestation: "pio:<id>"` (or fedramp:/soc2:) for a 20% compliance discount on regulated+ tiers. The signed receipt envelope is tagged `source: "lovable"` and includes a `vertical: { claimed_slug, attestation, quoted: { tier, multiplier, attested, ... } }` block recording the tier the upstream actually quoted — that is the audit trail for tier billing. Returns { paid, upstream_status, response, signed_context: { envelope, envelope_json, envelope_sha256, signature, signer, scheme: 'EIP-191' } }. Pass `idempotency_key` to safely retry without double-settling.

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• scan_dependency

  Gemini Crypto

  Composite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Returns a summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and a list of recent alternative versions. NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly. Partial failures degrade gracefully — bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns.

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• get_ai_traffic

  RevenueScope: Japanese EC RPS Benchmarks

  Return AI-assistant (ChatGPT/Claude/Perplexity/Gemini/Copilot) traffic for the given period. mode='referred' (default) lists landing pages that received clicked AI traffic — per page × AI source: sessions, bounce rate (%, always computed; judge reliability via the sessions count), summed revenue, and last citation date (default limit 100); a view GA4/GSC cannot produce (GSC is Google-search only; GA4 lacks an AI-source breakdown). mode='gaps' returns where the site leaves AI value on the table as a ranked action list: (1) missed_citation_pages — content articles with real audience but ~0 AI traffic (push for AI citation / GEO), ranked by engagement-weighted reach; (2) under_monetized_ai_pages — pages WITH AI traffic engaging below the site's own AI norm (improve landing/CTA), ranked by AI arrivals lost below benchmark (default limit 10/list); methodology fixed in code. site_id is OPTIONAL when OAuth-authenticated. Default period is the last 30 days; pass period='today'/'7d'/'90d' or a raw day count (1-365). Scope is clicked citations only.

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