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208,911 tools. Last updated 2026-06-18 11:22

"Providing Mathematical and Scientific Calculation Functions for a Single-Modal LLM" matching MCP tools:

  • Get the full schema for one petal_components component: attrs, slots, defaults, allowed values, and a working HEEx usage example. Call this every time you are about to write a tag like <.button>, <.modal>, <.table>, or <.field> so the attrs and slots match the real library instead of training-data guesses.
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  • Run a single-statement SELECT against the canvas dataframes registered by bls_get_series. Read-only: writes, DDL, DROP, COPY, PRAGMA, ATTACH, and external-file table functions are rejected. System catalogs (information_schema, pg_catalog, sqlite_master, duckdb_*) are denied at the bridge layer — use bls_dataframe_describe to list available dataframes. Supports JOINs, aggregates, window functions, and CTEs. Optional register_as persists the result as a new dataframe with a fresh TTL for chained analysis. Canvas SQL operations consume zero BLS API quota. Requires CANVAS_PROVIDER_TYPE=duckdb.
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  • 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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  • Returns the structured-data JSON artefacts the pipeline extracted from a filing's source PDFs. Use this when the question is about **rating mechanics, data tables, risk curves, calculation steps, or coverage / form definitions** — anything where the narrative summary isn't enough and the LLM needs the actual structured rows. Whitelist (and what each contains): - `calculations.json` — step-by-step rate calculation walk-through (base rate, factor application, final premium). One entry per documented example calculation. - `coverages.json` — coverage definitions: which perils / lines / risk types the filing addresses, with limits and applicability. - `deductibles.json` — deductible options offered, dollar amounts, and any peril-specific rules. - `discounts.json` — available discounts / surcharges, eligibility criteria, and the corresponding multiplicative factors. - `endorsements.json` — optional endorsements / riders attached to the filing. - `examples.json` — worked policyholder examples (sample insureds with calculated premiums). - `exclusions.json` — coverage exclusions and conditions under which they apply. - `extraction_summary.json` — structured machine-readable form of the same content `get_filing_summary` returns as Markdown; useful when you want filing-type / what-this-filing-does fields as JSON rather than prose. - `final_rating_calculation.json` — the canonical rating expression / equation the filing prescribes (base × factor1 × factor2 …). - `forms.json` — policy form numbers, edition dates, and the form types associated with the filing. - `rates_data.json` — the rate tables themselves: rows of (segment / cell / factor) values. **The biggest file by far** — can be hundreds of thousands of rows for territory-detailed filings. See truncation below. - `underwriting_guidelines.json` — eligibility and underwriting rules (e.g. credit-tier bands, prior-loss caps). **Truncation**: any returned file whose JSON contains an array longer than 100 rows is truncated to the first 100 rows. The truncated file gets a `_truncated` envelope describing the original total. For a lighter table-of-contents view (counts, item names, source pages — no payloads) call `get_filing_extract_meta` instead; it's the right surface for "what's in this filing" questions. `rates_data.json` is the common case where truncation fires. Args: `serff` (required), `files` (optional array — narrows the response to a subset of the whitelist; pass empty / omit for everything). Returns: `{ serff, files: { "<name>": <parsed json> | { content, _truncated } }, count, skipped, truncated }`.
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  • Recent species occurrence records from GBIF (Global Biodiversity Information Facility) — individual field observations and museum specimens of where/when a species has been recorded. Each record: species + scientific name, lat/lon, date, country, source dataset (+ GBIF citation), recorder, and basis-of-record (human observation, preserved specimen, etc.). `query` = a common name ("koala", "great white shark", "platypus") or scientific name ("Phascolarctos cinereus"); resolved against the GBIF backbone taxonomy. `near` = "lat,lon" to restrict to records within ~100 km of that point (e.g. "-27.47,153.02" for Brisbane). Keyless GBIF.org API. COMMERCIALLY CLEARED: filtered server-side to CC0 + CC BY 4.0 records only; CC BY-NC records are excluded. OCCURRENCE data only — not a population census or range map; coverage is sampling-biased and absence of records does not mean absence of the species. Source: GBIF.org API v1 — CC0 1.0 + CC BY 4.0 (per record).
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  • Retrieves the current trading price for a publicly listed stock by ticker symbol. Returns the current price as a single numeric value. This is a lightweight variant of stock_quote — it omits intraday high/low, percentage change, previous close, company name, sector, and exchange metadata. Use stock_price_lite when only the raw current price is needed for a quick lookup or calculation. Prefer stock_quote when the agent also needs price change, intraday range, company information, or a fully structured response suitable for portfolio reporting. Does not support cryptocurrency prices — use crypto_price for full market data (price, volume, market cap) or crypto_price_lite for a lightweight spot price lookup.
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  • List recent execution traces for an agent — the same data as /admin/requests, scoped to one agent and readable by an LLM. Use this when an agent call timed out, drafted the wrong response, or you want to know which tool/LLM call burned the latency. Pair with `agents.trace_get` for full detail on a specific trace. Filters: `status`, `success`, `source` (single value or comma-separated: `agent,voice`), `date_from`/`date_to` (ISO-8601), pagination via `limit`/`offset`. Returns `returned_count`, `dropped_on_page` (should be 0 — positive means the backend agent_id predicate let something through), and `has_more`. Edge case: a raw page of all-dedup-dropped rows yields `returned_count=0, has_more=true`; re-call with `offset += limit`.
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  • Run a single-statement SELECT against the canvas tables staged by faostat_query_observations and faostat_commodity_profile (table names look like faostat_xxxxxxxx). Use this for cross-country and cross-item aggregation, GROUP BY rankings, joins, and time-series analysis over the full result set the inline preview only sampled. Standard DuckDB SQL — joins, aggregates, window functions, CTEs all work. Read-only: writes, DDL, DROP, COPY, PRAGMA, ATTACH, and external-file table functions are rejected; system catalogs (information_schema, sqlite_master, duckdb_*) are denied — list staged tables via faostat_dataframe_describe. Every row carries its data-quality `flag` (A=Official, E=Estimated, I=Imputed, …) — keep it in projections and honor it in interpretation.
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  • Retrieves the current trading price for a publicly listed stock by ticker symbol. Returns the current price as a single numeric value. This is a lightweight variant of stock_quote — it omits intraday high/low, percentage change, previous close, company name, sector, and exchange metadata. Use stock_price_lite when only the raw current price is needed for a quick lookup or calculation. Prefer stock_quote when the agent also needs price change, intraday range, company information, or a fully structured response suitable for portfolio reporting. Does not support cryptocurrency prices — use crypto_price for full market data (price, volume, market cap) or crypto_price_lite for a lightweight spot price lookup.
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  • Run a read-only SQL SELECT against a DataCanvas table staged by an openFDA search tool (canvas_id + canvas_table in its response when spilled=true). Enables GROUP BY, COUNT/SUM/AVG, time-series, and joins across the full result set without re-paging the API. Call openfda_dataframe_describe first to get the exact table and column names. Scalar fields are stored as text (CAST for numeric math); nested objects/arrays are JSON columns — read them with DuckDB json functions, e.g. json_extract_string(openfda, '$.brand_name[0]'). Only SELECT is allowed — DDL, DML, COPY, and file-reading functions are blocked.
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  • 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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  • "Is [Panthera leo] a valid scientific name" / "exact match for [Latin binomial]" / "disambiguate homonyms" — exact scientific-name match returning 0 or 1 hit plus close alternatives. Use when you have a precise Latin name and want to confirm acceptance or distinguish homonyms (same name used for different organisms — pass authorship to disambiguate).
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  • 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 a single container by ID, including notes_count, children_count, description, negative_space (what does NOT belong in the folder), and LLM instructions if set. Optional: include_tree (boolean) to also get ancestor chain and children. Required: id (integer).
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  • Run a single-statement SELECT against the canvas dataframes registered by secedgar_fetch_frames, secedgar_search_filings, and secedgar_get_financials. Read-only: writes, DDL, DROP, COPY, PRAGMA, ATTACH, and external-file table functions are rejected. System catalogs (information_schema, pg_catalog, sqlite_master, duckdb_*) are denied — list dataframes via secedgar_dataframe_describe. Optional register_as chains the result as a new dataframe with a fresh TTL.
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  • Estimate credit cost for a conversion BEFORE running it. Returns word count, page calculation (300 words/page), and a credit breakdown by format and template type. Use this when the user asks 'how much will this cost?' or when you suspect a conversion might exceed their balance — convert_document refuses to run if credits are insufficient, so estimating first is friendlier.
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  • Match a scientific name against the GBIF backbone taxonomy. Returns the best-matching taxon with full classification and a confidence score (0–100). This is the mandatory first step for any GBIF workflow — it resolves synonyms and returns the backbone taxonKey required by gbif_search_occurrences, gbif_count_occurrences, and gbif_occurrence_facets. Below confidence 80, the match should be reviewed. matchType NONE means no usable match was found — try removing the strict flag or broadening the name.
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  • Performs precise financial calculations across six calculation types entirely locally with no external API dependency. compound_interest computes the final value and total interest earned on a principal over time at a given annual rate. loan_repayment calculates the monthly payment, total repayable amount, and total interest for a mortgage or loan given the principal, annual rate, and term in months. roi returns return on investment as a percentage and absolute profit or loss, with optional annualised ROI when a holding period is provided. present_value discounts a future cash amount back to its current value using a discount rate. future_value projects a present amount forward at a compounding annual rate. break_even finds the unit volume and revenue at which fixed and variable costs are fully covered by sales. Use this tool when an agent needs to perform any structured financial calculation — loan affordability, investment return, discounted cash flow, or cost analysis. Prefer financial_calculator_lite when only the single headline result is needed rather than a full structured breakdown. Do not use this tool to fetch live market prices or exchange rates — use stock_quote for stock prices, crypto_price for cryptocurrency prices, or currency_convert for FX rates.
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  • Extract text from PDFs and images as clean Markdown. Uses Mistral OCR — handles complex layouts, tables, handwriting, multi-column documents, and mathematical notation. Preserves document hierarchy in structured Markdown. 10 sats/page. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='extract_document' and quantity=pageCount for multi-page PDFs.
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  • Use this read-only tool when agents need the complete ATLAS-7 calculation bundle for an issuer and source_date. It assembles one calculation-history row from the existing ATLAS-7 precomputed surfaces: covenant stress, company fundamentals, peer ranking, alpha signals, alpha score breakdown, market regime context, SPECTRA inputs, quality flags, provenance, source fields, and hashes. Parameters: source_date replays one YYYY-MM-DD ATLAS-7 slice; source_date_from/source_date_to can page recent slices; ticker or CIK narrows to one issuer; mode=compact by default and full includes source_fields_json. Behavior: read-only and idempotent; it has no destructive side effects and performs no wallet, settlement, or trading actions. Use this before historical report rendering so agents do not mix latest-only fields into a historical answer.
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