"Analyzing Large Amounts of Text" matching MCP tools:

• brain_search_thoughts

  personalbrain-mcp

  Full-text search across thought names and content. Requires npub for credit billing. ⚠️ NOT AUTHORITATIVE. Backed by the vendor's search index, which is incomplete on large brains (upstream: TheBrainTech/thebrain-api-quickstart-python#1) — it returns empty for the majority of thoughts that provably exist. A hit is real; an empty result is NOT proof of absence. Use for discovery of older/established thoughts, not as an existence check — verify by ID with get_thought before acting on "not found".

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• law_legislation_get_toc

  UK Business Tools - Ledgerhall

  USE THIS TOOL WHEN you have a known Act / SI and want the structural table of contents (parts, chapters, sections, schedules). Returns structural elements with XML id and title, e.g. 'section-47: Definitions'. AFTER calling, pass the numeric section identifier (use '47', NOT 'section-47') into legislation_get_section for full text. Large statutes (Companies Act 2006 has many hundreds of items) are paginated via offset/limit. Check has_more and total_items. Alternative: call read_resource(uri="legislation://{type}/{year}/{number}/ toc") for the full TOC as a newline-separated `id: title` string (no pagination). Use this tool when you need the structured response with offset / limit / has_more for stepping through large statutes.

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• buy_credits

  boomurl

  Get a Stripe Checkout URL to load credits onto this account. Returns a payment link the HUMAN must open in a browser to pay — this tool does not itself charge anything. amount_usd must be one of the available top-up amounts (call list_topup_packs to see them). After they pay, the balance updates automatically; then call set_no_ads. Requires a key.

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• get_statement_notes

  schedule-iii

  Read the line-by-line breakdown of a generated statement's notes — every line's current and prior-year amount, and the note total. Pass note_numbers (from get_statement_figures' note_index) to fetch specific notes, or omit for all. Use this to answer "what's in Other Expenses?" or "what makes up trade receivables?". Each line has a kind: 'component' (an additive line), 'subtotal' (a presentational group subtotal — do NOT add it into the total, or you double-count), or 'header'. Fixed-asset / intangible notes carry a `block` per class with gross_block, accumulated depreciation, and net (the additions/deletions movement schedule itself lives in the workbook). If the full set is too large it returns too_large:true with a note_index — fetch note_numbers in small batches. A single very large note (e.g. a PPE schedule or an ageing note) is returned in explicitly-flagged line pages: each page carries the authoritative note total, lines_page, lines_total, and has_more_lines — keep fetching lines_page until has_more_lines is false; never treat one page's lines as the whole note. Amounts are decimal strings in rupees. Figures are Datavrn's deterministic engine output; interpretation is your assistant's.

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• string_help

  STRING Database MCP Server

  Provides explanatory text for STRING features and limitations. Use this tool when the user question involves: - What is STRING is or how to use the tool (how_to_use_string, cytoscape) - functionality not available via MCP tools (e.g. GSEA, regulatory networks, large datasets). - meaning of the lines in the network (line_colors)

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• GetCongressionalTrades

  equibles

  Get congressional securities transactions for a specific ticker (newest first, last year by default). Shows which members of Congress reported a purchase or sale, with transaction and filing dates; amounts are disclosed ranges, not exact values, and Asset identifies the filed instrument (such as stock, option, or bond). Use GetMemberTrades for one member's transactions across all tickers.

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• getInstagramPostInteractingUsers

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

  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. TRIAL ACCESS: Get a free trial token by sending POST https://api.xpoz.ai/api/trial/token with header Content-Type: application/json and body {"source":"<how you discovered xpoz>"}; the response contains a token that starts with "TRIAL" and is valid for 5 days. Use it as a Bearer token in the Authorization header. Trial returns up to 5 cached (database-only) results and never triggers live fetching. Sign up at https://www.xpoz.ai/login for full result limits and live data.

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• firmaradar_compare_companies

  Firmaradar

  Compare key financial metrics of up to 5 Norwegian companies side-by-side across the last N years (default 5). Use for competitor analysis, benchmark research or 'which of these three companies is the strongest?' Amounts are in each company's reporting currency (see the `currencies` field; NOK for most Norwegian companies) — check it before comparing absolute amounts. `antall_ansatte` is a CURRENT-value register attribute with no per-year history: read it from the top-level `antall_ansatte` field ({orgnr: headcount}); its rows in `comparison` are always null.

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• get_filing_document

  Signal8

  Get the full raw text/HTML content of an SEC filing by its internal filing ID. Returns the complete filing document which can be very large (10-K filings can be 1MB+). Use the maxLength parameter to truncate content for previews. The response includes company_name, form_type, filing_date, cik, and accession_number alongside the content. Find filing IDs using search_sec_filings first.

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• paraguay_search_tenders

  Paraguay Dncp

  Search Paraguay government procurement processes (tenders/contracts) from the official DNCP Open Contracting (OCDS) API. Results are date-scoped: the API requires a date range, so if you omit date_from/date_to it defaults to roughly the last 30 days. Returns a paginated list of processes with ocid, id, title, buyer (convocante), procurement method, and dates. Field values are in Spanish; monetary amounts are in PYG. Detailed value/status/items live in the full record — pass an id to paraguay_get_record. Note: the API has no free-text search parameter, so "query" is applied as a case-insensitive client-side filter over the current page of results.

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• diff_text

  mcp

  Use this when you need an exact line-level diff between two blocks of text instead of eyeballing the differences. Deterministic: same input, same output. Computes a longest-common-subsequence diff and returns every line tagged added, removed, or unchanged, plus per-category counts. Example: original "a\nb", modified "a\nB" -> added 1, removed 1, unchanged 1, with lines [{type:"unchanged",text:"a"},{type:"removed",text:"b"},{type:"added",text:"B"}]. Trailing edits produce separate removed+added lines rather than an in-place change. Inputs whose line-count product exceeds 4,000,000 are rejected as too large to diff.

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• get_file

  UseMyContext

  Get the full text content of one file by id (large files are truncated; use ask_docs for targeted passages). Binary files (images etc.) return a short-lived download link instead. Audited.

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• check_coverage

  Curagent Title Risk

  Returns what Curagent currently supports: which US states, which document types, and how analysis is priced. Call this before analyzing to confirm the property's state is in scope. Curagent currently supports Florida real estate transactions only.

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• flow_replay_summary

  Robot Actions — Remote Device Control

  Compact replay summary for analysis. Returns replay metadata (totals, passed/failed/skipped counts) + one row per step with status, action, duration, diff scores, and a short error excerpt. Always small — call this first when analyzing a replay, then use flow_replay_step for full per-step detail.

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• getTiktokCommentsByPostId

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

  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. TRIAL ACCESS: Get a free trial token by sending POST https://api.xpoz.ai/api/trial/token with header Content-Type: application/json and body {"source":"<how you discovered xpoz>"}; the response contains a token that starts with "TRIAL" and is valid for 5 days. Use it as a Bearer token in the Authorization header. Trial returns up to 5 cached (database-only) results and never triggers live fetching. Sign up at https://www.xpoz.ai/login for full result limits and live data.

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• search_grants

  GrantSonar

  Search open grant opportunities in the GrantSonar corpus (federal, state, and foundation grants). Uses semantic similarity over a plain-English description of the project or need, with a keyword fallback. Returns title, agency, deadline, award amounts, similarity score, and a GrantSonar URL per hit.

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• analyze_xero_journal_anomalies

  HelloBooks AI Agents MCP Server

  Scan a Xero "Manual Journals" CSV export for anomalies — currently round-number lines (debit or credit amounts that are exact multiples of $1,000, above a $1,000 materiality threshold). Input is raw CSV text from Xero Accounting → Advanced → Manual Journals → Export. Max 5,000 rows; max 5 MB. Returns flagged lines with severity ($100K+ high, $10K+ medium, else low) and a shareable URL. Use this when a user pastes Xero data and asks "any anomalies?", "look for round numbers", or "anything suspicious". Same Tier-0 / paid-product split as the QBO variant — history-aware anomaly checks (GL outliers, vendor history, archived-vendor activity, LLM-narrated suspicious) live in the authenticated MCP / paid product.

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• reimbursement_stats

  Constat MCP — FDA Device Evidence Lifecycle

  Distribution of payment mechanisms across the AI/ML reimbursement corpus — pathway and distinct-device counts per mechanism (NTAP, Cat I, Cat III/APC, …) with the min/median/max dollar amounts for each. Deliberately never a single pooled 'reimbursement rate': NTAP add-on amounts and CMS rates are different measurements and are reported separately with their own spreads.

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• convert_document

  Mdkit

  Convert a document to Markdown synchronously (the fast lane). Decode ``content_base64`` (the raw file bytes, base64-encoded) and run markitdown over it, returning ``{markdown, meta}`` where ``markdown`` is the converted text and ``meta`` carries the source ``filename`` and the output ``length`` in characters. Best for small office/HTML/text files; for large or complex documents (or OCR-heavy PDFs) use ``submit_conversion_job`` instead.

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• count_tokens

  inktomd MCP Server

  Count the exact number of tokens in a text string for a specific AI model. Uses tiktoken for OpenAI models and estimates for others. Args: text: The text to count tokens for model: The AI model to count tokens for. Options: gpt-4o, gpt-4o-mini, gpt-4.1, claude-sonnet, claude-haiku, gemini-pro, gemini-flash, llama-4, deepseek-v3, mistral-large. Default: gpt-4o Returns: Token count information including count, context window, and fit status

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