Blockworks

List the tabular datasets this server serves, or describe one in full. Tabular models are row-and-column datasets — assets, exchanges, funding rounds, funds, ETF holdings — as opposed to metrics over time, which timeseries_catalog covers. Call this before tabular_data. It returns the exact model slug, the exact column field names, which columns are sortable, and which filter operators each column accepts. tabular_data rejects any field or operator that does not match the catalog exactly. With no arguments it returns one compact entry per model. Pass a model slug for that model's full column list, or search to narrow the list. This catalog lists only the models Messari publishes to the public API; any model it does not publish is omitted here and cannot be discovered through this tool. Publishing is separate from data access: a listed model still carries an access tier, so reading a paid, unpaid or permissive model needs an API key with the matching entitlement, while a public model needs none.

tabular_catalog

List the time-series datasets this server serves, or describe one in full. Timeseries models are metrics observed over time — prices, supply, revenue, volumes, mindshare — split into named series, usually one per asset, protocol, exchange or sector, and available at fixed granularities. Call this before timeseries_data. It returns the exact model slug, the granularities the model supports, the exact metric field names, and whether the model can return every series at once (allowAllSeries true) or requires you to name the series keys. With no arguments it returns one compact entry per model. Pass a model slug for that model's full metric list, or search to narrow the list. Series keys are the model's own identifiers, usually opaque ids rather than tickers or names. To turn a name or symbol into a series key, read the matching tabular model with tabular_data and take the key from the matching row.

timeseries_catalog

Read points from one time-series dataset over a time window. Call timeseries_catalog with this model's slug first: the slug, the valid granularities, the metric field names, and whether every series can be read at once all come from there. Series keys are the model's own identifiers — if you have only a name or a ticker, resolve it through the matching tabular model with tabular_data. Points are positional arrays, not objects: position 0 is a unix timestamp in seconds and the remaining positions follow point_schema in order. Read point_schema before interpreting any point. Choose a window with either timeframe (relative: 30d, 12h, 4w, or ytd, qtd, mtd, dtd; or calendar: 2024, 2024-06, 2024-06-01) or start and end — never both. With none of them the window defaults to the last 90 days. Set bounds true to get only the first and last point of the window for each series, which is the cheap way to compute a change over a period. One call returns at most 5000 points across all series. If a call is rejected for exceeding that, shorten the timeframe, use a coarser granularity, name fewer series, select fewer metrics, or set bounds true.

timeseries_data

Read rows from one tabular dataset, filtered, sorted and paginated. Call tabular_catalog with this model's slug first. Every string passed in model, selections, filters and sortBy must match the catalog exactly. Filters are objects of the form {field, operator, values}, combined with AND. A column only accepts the operators listed in its catalog entry. Values are written as strings and coerced to the column's type; dates accept YYYY-MM-DD, RFC3339 or unix seconds. Includes and IncludesAnyOf apply to columns holding arrays. Returns at most 200 rows, 25 by default, plus totalRows and totalPages for the whole filtered set — check totalRows before concluding you have seen everything. To answer a question about a large model, filter and sort on the server rather than paging through it.

tabular_data

Retrieve source documents from Messari's research and news corpus. This tool runs Messari's Search agent on its own. It returns the documents it found. It does NOT write an answer: read the documents and write the answer yourself. USE THIS TOOL FOR: - Explanations and background: "What is EigenLayer's restaking model?" - News and events: "What happened to Balancer in November?" - Governance proposals, diligence findings, project narrative, conference transcripts - Any question that needs text evidence you can cite USE tabular_data OR timeseries_data INSTEAD FOR: - Numbers, rankings, aggregates, and time series: price, volume, TVL, fees, revenue, supply, funding amounts, unlock schedules - Any question a query over a dataset answers better than a document INPUT: query, one natural language question. Ask one thing per call. Several focused calls beat one loaded query. The corpus spans years and the agent reads the time window out of your wording, so name concrete dates whenever the question is time-sensitive: "in August 2026" or "since 2026-08-01", not "latest" or "recently". A query with no date returns the best matches from the whole corpus, most of which will be old. OUTPUT: - sources: the retrieved documents. Each has domain (research, news, external_news, diligence, governance, twitter, video_transcript, and others), title, url, published_at (absent when the corpus carries no date), and content. - error: set only when the run finds no usable document. It explains why. Cite the title and url of every document you use. State no fact the documents do not support.

search_documents