Search recorded Binance USDT-M market microstructure with reproducible MCP research tools and evidence-linked results.
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💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Inspect callable tools, capabilities, and parameters exposed to AI agents by EdgeDepth Research.
list_featuresReturns the closed grammar registry: feature ids, types, ranges, operators, windows, sequence rules, limits, and error codes. `search`, `feature_ids` and `compact` narrow it.
list_instrumentsReturns the research universe and coverage. The default is a compact summary; use `symbols: [...]` for selected full records or `full: true` for the verbatim canonical universe.
prepare_studyFree deterministic structured preparation, provenance and allowance estimate. Requires the web `/prepare` release first.
interpret_proseTurns prose into a proposed query document. It does not execute the query. Optional `time_zone` accepts an IANA time zone for calendar planning.
run_scanExecutes a `research_query.v2` document and returns result bytes with counts, denominators, outcomes, the unconditional same-scope reference, and the reproducibility key. Projected by default (`rows`, `full_rows`, `full_counts`).
next_pageContinues a prior scan with its opaque cursor. Never construct cursors manually.
The EdgeDepth Research MCP server gives MCP clients access to EdgeDepth's recorded-market research system for Binance USDT-M crypto perpetuals. It is designed for studies that need an exact condition, a defined eligible population, forward outcomes, and an auditable result rather than a single illustrative chart or example.
The server can describe the available feature grammar and research universe, turn a natural-language request into a proposed query document, execute a research_query.v2 document, and continue paginated scans. It also supports event-focused analysis, including screenshot grounding, move investigation, point-in-time feature snapshots, base rates, commonality, cohort comparisons, and stratified outcomes.
EdgeDepth Research MCP server uses one tool core through hosted Streamable HTTP or local stdio. The hosted endpoint is https://mcp.edgedepth.com/mcp and uses browser authorization. Local execution runs the npm package with npx and an API key.
A typical workflow starts by checking the grammar or interpreting the research question. prepare_study can validate a structured scope, predicates, and outcome and return an unrun definition with an allowance estimate. interpret_prose produces a proposed query but does not execute it. After approval, run_scan executes the unchanged document. next_page uses the cursor returned by a previous scan; cursors should not be constructed manually.
Scan-family responses are projected by default to reduce client context usage. Options such as full_rows, full_outcomes, and full_counts recover additional detail where supported. Counts, denominators, exclusions, outcome coverage, and the reproducibility key remain part of the result contract.
For hosted use, add the MCP URL to a compatible client and complete the EdgeDepth browser authorization flow. Cursor uses an MCP entry with a url, while Codex uses an mcp_servers configuration and codex mcp login edgedepth.
For local stdio, install nothing permanently: run npx -y @edgedepth/research-mcp with EDGEDEPTH_API_KEY set to a key created on the EdgeDepth Developer page. Node.js 20 or newer is required. The research:read scope covers recorded-data tools; research:interpret is additionally needed for interpret_prose.
list_features.list_instruments.run_scan, next_page, and run_cohort.ground_screenshots, investigate_move, and snapshot_at.base_rate, commonality, run_stratified, and outcome_first.get_report.The EdgeDepth Research MCP server is research-only. Its tools do not trade, change alerts, or publish reports. Fresh scans, cohort calculations, and stratified computations may consume research allowance units, and the response identifies that side effect. prepare_study, interpret_prose, and screenshot grounding are described as free where applicable.
Outcome fields cannot be used as scan filters, which prevents lookahead selection in the supported workflow. Missing data is represented as absent rather than silently converted to zero. Page rows are examples; population counts and outcome denominators should be used for conclusions. The optional private Radar pilot can save account-owned hypotheses only with the separate research:hypotheses permission.
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