"A tool for parsing tables and analyzing data" matching MCP tools:

• xer_parserA

  cpp-cpm-engineA

  Parse a Primavera P6 XER file and return a TABLE SUMMARY (not the full row-level data — XER row dumps explode the MCP context window). For each table in the XER, returns the table name, field list, and record count. Per-row data is intentionally omitted — for forensic / DCMA / windows analysis use the dedicated tools (``forensic_windows_analysis``, ``critical_path_validator``, etc.) which consume the parsed XER internally and return analytical summaries, not raw rows. Use this tool to confirm an XER is parseable, list its tables, see the data date / project name from PROJECT, or count activities in TASK before deciding which deeper tool to run. Args: xer_path: server-side filesystem path to the XER file. xer_content: full text of the XER file (alternative for hosted/remote use). Supply EXACTLY ONE of path/content. Returns: { "filepath": absolute path, "encoding_used": "utf-8" | "cp1252" | ..., "ermhdr": file header dict (P6 version, export user, etc.), "tables": [{"name", "fields", "record_count"}, ...], "table_count": int, "total_records": int, "project_summary": { "proj_id", "proj_short_name", "proj_long_name", "data_date", "plan_end_date" } (from first PROJECT row, if any) }

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• link_tablesA

  Georgia Civic DataA

  List the tables link_query can read (curated gold paths only) and the join keys that bridge facts → dimensions → Census geography. Call this BEFORE writing a link_query. Two-tier to stay context-cheap: with NO arguments it returns a LEAN index — every table's name, grain, detail levels, default `read_parquet(...)` snippet, and join keys (enough to pick tables and write a single-detail join). To get every column and a snippet per detail level for the few tables you actually need, call again with `tables=["<name>", ...]` (a `name` from the index, e.g. 'education/gosa/attendance' or 'attendance', or a dimension like 'districts'). Paste the `read_parquet(...)` snippets verbatim into your SQL — they are exactly what the sandbox accepts.

  ConnectorNo auth

• getInstagramPostInteractingUsersA

  Social Media Search API — Twitter, Instagram, Reddit, TikTok (XPOZ)A

  Get USER PROFILES of people who interacted with an Instagram post. Returns full user data (bio, followerCount, followingCount, etc.). RETURNS USER PROFILES: id, username, fullName, biography, followerCount, followingCount, isVerified, profilePicUrl. Use for analyzing WHO engaged with a post. NOT FOR COMMENT TEXT: To read the actual comment content (what people wrote), use getInstagramCommentsByPostId instead. INTERACTION TYPES: "commenters" (users who commented), "likers" (users who liked). WHEN TO USE THIS TOOL: Analyzing commenters/likers demographics, finding influencers who engaged, building audience profiles, network analysis of who interacts with posts. WHEN TO USE getInstagramCommentsByPostId: Reading comment text, sentiment analysis of what was said, analyzing discussion content. FAST (default, omit responseType or responseType="fast"): Returns up to 300 results directly (use limit param to reduce, e.g. limit=5). Auto API fallback for commenters when stale. PAGING (responseType="paging"): Async paginated results (1000 users per page with default fields), returns operationId - IMMEDIATELY call checkOperationStatus to get results. CSV export included via dataDumpExportOperationId. Supports pageNumber/tableName for subsequent pages. Optional fields (default: ["id", "username", "fullName"]). Available: biography, isPrivate, isVerified, followerCount, followingCount, mediaCount, profilePicUrl. This is a safe, read-only tool for analyzing searchable information.

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• execute_queryA

  BorealHostB

  Run ONE bounded read against a site's database. One statement, beginning with SELECT, SHOW, DESCRIBE or EXPLAIN, with no second statement and no data-modifying clause anywhere in it. The container proves the statement is a read before running it and caps the result: 200 rows by default, 1000 maximum, 2 MB of cell data. `truncated` in the response says whether a cap was reached. ⚠ THIS TOOL CANNOT CHANGE ANYTHING, AND RETRYING WITH DIFFERENT WORDING WILL NOT MAKE IT. Anything that is not a single bounded read is refused with READ_ONLY and nothing runs. To change data or schema, use the tool for the job: optimize_database, database_search_replace, manage_db_user, list_databases, list_tables — and for creating, altering, dropping, importing or exporting tables, the database manager in the control panel, which has no tool here. Requires: API key with write scope (unchanged — the scope is the customer's published permission for this operation, not a claim about what it does). Args: slug: Site identifier database: Database name query: One read statement Returns: {"columns": ["id", "user_email"], "rows": [[1, "a@example.com"], ...], "row_count": 1, "truncated": false, "execution_time_ms": 12.0} Errors: READ_ONLY: Not a single bounded read. The error text carries the explanation and names the operation to use instead.

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• get_job_statusA

  CatchAll (by NewsCatcher)B

  Check the status of a submitted job. Call this after submit_query to see if your job is ready. Status progression: submitted -> analyzing -> fetching -> clustering -> enriching -> completed/failed IMPORTANT: Jobs take several minutes to process. First check after ~1-2 minutes, then poll every 30-60 seconds. Broad searches can take 10-30+ minutes; for long jobs, poll every 60-120 seconds. Do NOT call this tool in a tight loop. Stop polling when status is `completed` or `failed`. Treat `submitted`, `analyzing`, `fetching`, `clustering`, and `enriching` as active states and continue polling. You don't need to wait for completion to pull results. Partial results are available during `enriching` — call pull_results after ~2 minutes, then poll status every 30-60 seconds and pull again for fresher results. Do not stop pulling just because an intermediate pull is empty/unchanged. Use `progress_validated` vs `candidate_records` to track whether more results may still appear (`progress_validated < candidate_records`). If transport/session fails, resume using the same `job_id`.

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• get_job_statusA

  CatchAllB

  Check the status of a submitted job. Call this after submit_query to see if your job is ready. Status progression: submitted -> analyzing -> fetching -> clustering -> enriching -> completed/failed IMPORTANT: Jobs take several minutes to process. First check after ~1-2 minutes, then poll every 30-60 seconds. Broad searches can take 10-30+ minutes; for long jobs, poll every 60-120 seconds. Do NOT call this tool in a tight loop. Stop polling when status is `completed` or `failed`. Treat `submitted`, `analyzing`, `fetching`, `clustering`, and `enriching` as active states and continue polling. You don't need to wait for completion to pull results. Partial results are available during `enriching` — call pull_results after ~2 minutes, then poll status every 30-60 seconds and pull again for fresher results. Do not stop pulling just because an intermediate pull is empty/unchanged. Use `progress_validated` vs `candidate_records` to track whether more results may still appear (`progress_validated < candidate_records`). If transport/session fails, resume using the same `job_id`.

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• sample_sizeA

  Moltline Research DeskA

  Calculate the survey sample size needed for a confidence level and margin. FREE. Uses maximum variance (p=0.5) with a finite-population correction when population is given. Typical input {"population": 5000, "confidence_pct": 95, "margin_pct": 5} returns {"required_sample": 357, "assumptions": "p=0.5 (max variance), random sampling"}. Use before collecting data, to size a survey. Not for analyzing data already collected (stats_describe, confidence_interval). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "confidence_pct must be 90, 95, or 99"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

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• sample_sizeA

  Moltline Research DeskA

  Calculate the survey sample size needed for a confidence level and margin. FREE. Uses maximum variance (p=0.5) with a finite-population correction when population is given. Typical input {"population": 5000, "confidence_pct": 95, "margin_pct": 5} returns {"required_sample": 357, "assumptions": "p=0.5 (max variance), random sampling"}. Use before collecting data, to size a survey. Not for analyzing data already collected (stats_describe, confidence_interval). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "confidence_pct must be 90, 95, or 99"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

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• database_tools_execute_queryA

  Point Topic — UK Broadband Market ReferenceA

  Query Point Topic's public broadband ontology (ClickHouse) — read-only. Exposes the public reference tables (the visitor view): the entity graph of ISPs, network operators, networks, links, link standards and their relationships. Discover tables with SHOW TABLES FROM ontology; inspect columns with DESCRIBE TABLE <name>. Licensed measurement data (footprints, premises, speeds, tariffs, forecasts, take-up, subscribers) is not queryable here and requires a Point Topic licence — denied queries return contact details. Only SELECT/WITH/SHOW/DESCRIBE/EXPLAIN allowed; returns CSV (large results are truncated at ~50k tokens with a leading notice — add LIMIT to keep results small).

  ConnectorNo auth

• runtime_sandbox_interpreter_runA

  Runtime CloudA

  Run code in a stateful interpreter inside a running sandbox: variables and imports persist per context, like notebook cells (Go keeps declarations, not values). Returns stdout, stderr, results (text, HTML, PNG charts, tables for DataFrames) and any error with its traceback; values over 48 KiB come back as refs to files. For data analysis, charts and quick computations; runtime_sandbox_exec runs shell commands. timeoutMs defaults to 300000.

  ConnectorAPI key

• ag_table_markdown_extractA

  agent-guildB

  Extract structured tables from markdown text. Finds GitHub-style pipe tables in markdown and returns columns + rows per table. Use on model output or docs before downstream structured processing. Deterministic, fixture-verified, free for guests (rate-limited; pass your Guild api_key to use your member budget). Returns the result plus a Guild-signed provenance envelope. `payload` MUST match this JSON Schema: {"type": "object", "properties": {"markdown": {"type": "string", "maxLength": 60000}}, "required": ["markdown"], "additionalProperties": false} Output schema: {"type": "object", "properties": {"tables": {"type": "array"}, "count": {"type": "integer"}}, "required": ["tables", "count"], "additionalProperties": false}

  ConnectorNo auth

• getInstagramCommentsByPostIdA

  Social Media Search API — Twitter, Instagram, Reddit, TikTok (XPOZ)A

  Get COMMENT CONTENT (text, likes) for an Instagram post. Returns the actual comment objects with text and metadata. RETURNS COMMENT DATA: id, text, username, createdAtDate, likeCount, childCommentCount. Use for reading what people said. NOT FOR USER PROFILES: To get detailed user profiles (bio, followerCount, followingCount) of commenters, use getInstagramPostInteractingUsers with interactionType="commenters" instead. IMPORTANT: postId must be in strong_id format (e.g., "3606450040306139062_4836333238") - use the full "id" value from other Instagram tools, NOT just the media_id. FAST (default, omit responseType or responseType="fast"): Returns up to 300 results directly (use limit param to reduce, e.g. limit=5). Auto API fallback for fresh data. PAGING (responseType="paging"): Async paginated results (100/page), returns operationId for polling via checkOperationStatus. Supports pageNumber/tableName for subsequent pages. CSV (responseType="csv"): Async single CSV download, returns operationId, poll for S3 link. CODE EXECUTION: For csv mode, download CSV and use code execution to analyze full dataset. Ideal for: sentiment analysis, reading discussions, analyzing comment content, engagement patterns. Date filters: OMIT startDate/endDate parameters by default. ONLY pass these if user explicitly requests specific date range (YYYY-MM-DD format). IMPORTANT!!!!!: THE CURRENT YEAR IS 2026. When user requests relative dates (last week, last month), verify the current date from your system context and double-check the calculated dates - models often get the year wrong, searching one year earlier than intended. Optional fields: ["id", "text", "username", "createdAtDate", "likeCount"]. This is a safe, read-only tool for analyzing searchable information.

  ConnectorOAuth

• getTiktokCommentsByPostIdA

  Social Media Search API — Twitter, Instagram, Reddit, TikTok (XPOZ)A

  Get COMMENT CONTENT (text, likes) for a Tiktok post. Returns the actual comment objects with text and metadata. RETURNS COMMENT DATA: id, text, username, createdAtDate, likeCount. Use for reading what people said. FAST (default, omit responseType or responseType="fast"): Returns up to 300 results directly (use limit param to reduce, e.g. limit=5). Auto API fallback for fresh data. PAGING (responseType="paging"): Async paginated results (100/page), returns operationId for polling via checkOperationStatus. Supports pageNumber/tableName for subsequent pages. CSV (responseType="csv"): Async single CSV download, returns operationId, poll for S3 link. CODE EXECUTION: For csv mode, download CSV and use code execution to analyze full dataset. Ideal for: sentiment analysis, reading discussions, analyzing comment content, engagement patterns. Date filters: OMIT startDate/endDate by default. ONLY pass if user explicitly requests date range. IMPORTANT!!!!!: THE CURRENT YEAR IS 2026. When user requests relative dates (last week, last month), verify the current date from your system context and double-check the calculated dates - models often get the year wrong, searching one year earlier than intended. Optional fields: ["id", "text", "username", "createdAtDate", "likeCount"]. This is a safe, read-only tool for analyzing searchable information.

  ConnectorOAuth

• terminology_diffA

  Medical Terminologies MCPA

  Report what diff data is available between two versions of a terminology. For most terminologies this is **guidance only** — the server doesn't ship historical snapshots, so the tool points at the publisher's official changelog and explains the cadence. `bundled_versions` lists the version(s) this server actually has on hand. For **ICD-10 vs ICD-11** specifically, the tool surfaces a real cross-revision summary from the bundled WHO transition tables (the ICD-10 → ICD-11 case is a structural diff between two WHO revisions). Use `terminology: "icd10"` with no `to_version` to get the cross-revision summary: total mapped ICD-10 categories, how many are 1:1 vs split into multiple ICD-11 codes, and the average number of alternatives when split. Inputs: - `terminology` (required): which terminology to report on. - `from_version` (optional): the version you have data from. If omitted, the tool reports against the currently-bundled version. - `to_version` (optional): the version you want to compare to. If omitted, the tool reports against the publisher's latest known release. This tool is intentionally a metadata + guidance layer, not a diff engine — for terminologies that change frequently (SNOMED, LOINC, RxNorm, MeSH), the publisher's official changelog is the authoritative source.

  ConnectorNo auth

• get_db_schemaA

  FinBridgeA

  Inspect the schema of the local finbridge database (SQLite with ingested KR/US company fundamentals, filings, and daily prices): tables, views, columns, per-table row counts (counted in the background and refreshed every 30 minutes; null with rows_note "counting…" right after a server start), and ready-to-run example queries for query_db. Read this before writing a query_db statement. It returns no company data itself — get_db_schema describes the tables, query_db runs the SELECT. Args: (none) Returns: {tables: [{name, columns: [{name, type}], rows}], views: [{name, columns: [{name, type}]}], examples: [sql_string]} Key objects: - companies: KR companies have source='dart' + stock_code (6-digit), US companies source='edgar' + ticker - financials: one row per company x fiscal_year x quarter (quarter=0 = annual); raw unscaled KRW/USD amounts - prices_daily: daily OHLCV per company_id - views v_financials (financials joined with company name/ticker/stock_code) and v_latest_annual (latest annual row per company) — prefer these in query_db Examples: - Call before writing SQL for query_db, to learn table/column names. - Check row counts to see how much data the nightly ingest has loaded. Use when: preparing a query_db, or checking ingest coverage. Don't use for market data itself (get_stock_prices / get_valuation read the same tables with the right joins already done). FinBridge has no real-time equity quote tool — equity prices here are end-of-day closes from the nightly ingest; the only live data is regulator filings (get_dart_filings / get_edgar_filings). Errors: 'database has not been built yet' — the ingest pipeline has not run on the server.

  ConnectorAPI key

• list_test_data_setsA

  RoutebaseA

  List the data sets of a test suite — the tables that drive data-driven runs. The list gives names and row counts; pass dataSetId to get one set including its columns and rows. Use this to browse data sets; to create, change or import one use manage_test_data_set. Requires project context.

  ConnectorAPI key

• cricket_minor_league_infoA

  mcp-cricketA

  Player records and league history for Minor League Cricket (the US domestic T20 league), from data this server holds: CricClubs scorecards for 2026 and results with grounds for 2023-2026, CricClubs' 2025 and 2026 batting tables, Wikipedia's 2021-2024 squads and leader tables (CC BY-SA 4.0) and a research record that sources every fact. Every argument is optional and they combine: player for a season-by-season card (add team when two players share a name); team for where they are based and play, their finals and a season's captain, wicketkeeper, players and results; team with opponent, date or both for one fixture's ground, result and 2026 top performers; ground; leaders with season and team; season alone for its champion, final and awards; topic for league facts. With no arguments it lists what it can answer. Each answer gives exact figures, its source and how current it is, and says what the data lacks. Names are matched whole: a surname several players share gets a question back, and a name the data lacks is said to be missing, never answered with another player's figures. Live scores and win chances are cricket_minor_league's.

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• epss_timeseriesA

  ZEN SecDBA

  Get the historical EPSS time series for a specific CVE. ## What this tool does Returns the historical EPSS score, percentile, and model version available for a CVE across time, ordered by date. Useful for analyzing how exploitability likelihood has evolved over time. ## When to use this tool Use this tool when the user asks about: - EPSS trend over time - how exploitability probability changed - whether EPSS spiked or dropped - historical comparison of risk If the user only wants the current EPSS score, use `vulnerability_score` instead. ## Inputs - **cve_id**: valid CVE identifier (`CVE-YYYY-NNNNN`). ## Outputs - **series**: array of objects, each containing: - `date`: measurement date in ISO format - `score`: EPSS score - `percentile`: EPSS percentile - `model`: EPSS model version ## LLM usage guidelines - Never guess EPSS values-use this tool for all EPSS time-series questions. - If `cve_id` is malformed or incomplete, ask the user to correct it before calling. - If the user mentions multiple CVEs, call the tool once per CVE as needed. - If no historical data is available, return an empty series and state that no EPSS history was found.

  ConnectorNo auth

• getRedditPostWithCommentsByIdA

  Social Media Search API — Twitter, Instagram, Reddit, TikTok (XPOZ)A

  Get Reddit post by ID with its comments. Returns both the post data and comments in a single response. FAST (default, omit responseType or responseType="fast"): Returns post and up to 300 comments directly (use limit param to reduce, e.g. limit=5). Auto API fallback for fresh data. Results include guidance for full mode. PAGING (responseType="paging"): Async paginated results (100 comments/page), returns operationId for polling via checkOperationStatus. Supports pageNumber/tableName for subsequent pages. RESPONSE STRUCTURE: Returns { results: { post: {...}, comments: [...] }, count, guidance }. PAGING MODE DETAILS: FIRST CALL: Omit pageNumber and tableName. Creates cached table for comments, returns page 1 with post data and pagination metadata. SUBSEQUENT PAGES: Use tableName from first response with pageNumber (2, 3, etc.) to fetch additional comment pages. Post data is NOT returned on subsequent pages. FIELD SELECTION: Use postFields for post data optimization, commentFields for comment data optimization. First searches database for both post and comments, then external API if data is stale or missing. This is a safe, read-only tool for analyzing searchable information.

  ConnectorOAuth

• faostat_dataframe_describeA

  faostat-mcp-serverA

  List the canvas tables (faostat_xxxxxxxx) staged by faostat_query_observations and faostat_commodity_profile, each with its source tool, the query parameters that produced it, creation/expiry timestamps, row count, and column schema. Call this before faostat_dataframe_query to discover the exact table and column names to reference in SQL. Tables are listed newest-first and paged: pass `name` to describe one table outright, or page with `offset` + `limit` — when the response reports `truncated`, pass the returned `nextOffset` to fetch the rest.

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• get_sample_scoreA

  x402 Counterparty ScoreA

  Returns a clearly-marked stale sample counterparty score in the exact response schema of score_x402_counterparty (verdict, wash_trading_ratio, real_buyer_count, repeat_buyer_rate, source, updated_at, confidence). Free, no payment required, no input required. Sample data is fixed and expired by design — use it to validate response parsing, then call score_x402_counterparty ($0.02/call) for fresh decision-grade scores.

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