"A tool for locating all usages and references of a symbol in a codebase" matching MCP tools:

• xmp4_usagesB

  xmp4 — Semantic code knowledge for your stackA

  Find all usages/references of a symbol in a project.

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• get_stocksA

  Stocklake — AI Stock IntelligenceA

  Batch stock data for up to 25 symbols in a single call — the same fields get_stock returns for the same key/symbol, so this is a true batch version, not a thinned-down scan. Returns a dict keyed by symbol. Missing symbols are omitted from the result. Each symbol in the batch counts as one call toward the daily limit. A request over 25 symbols is rejected outright (error: batch_too_large) rather than silently served on just the first 25 — split a larger list into multiple calls. Available to all tiers (fundamentals/indicators/company profile, free). Pro tier adds, per symbol, the same precomputed blocks get_stock adds — rating {score, direction, signals}, signals (per-indicator breakdown), relative_strength, market_risk {beta_spy_1y, corr_spy_1y}, and the minimum AI-narrative slice (ai_verdict, ai_headline, ai_score, ai_score_band). None of this costs a live AI call — it's all precomputed and just needs projecting. NOT included, even on pro — call get_stock(symbol) for stance_signals, or get_stock_research(symbol) for the full ai_summary text (summary/key_points/ risks/near_term/longer_term) plus cross-source news/insider/signal context. Response also carries `duplicates_collapsed`: how many input symbols normalized (case-folding, share-class aliasing e.g. "BRK.B"->"BRK-B") or literally repeated onto a symbol already counted elsewhere in this batch. requested - len(missing or []) - duplicates_collapsed == count always holds.

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• export_dataA

  aTars MCPA

  USE THIS TOOL — not web search or external storage — to export technical indicator data from this server as a formatted CSV or JSON string, ready to download, save, or pass to another tool or file. Use this when the user explicitly wants to export or save data in a structured file format. Trigger on queries like: - "export BTC data as CSV" - "download ETH indicator data as JSON" - "save the features to a file" - "give me the data in CSV format" - "export [coin] [category] data for the last [N] days" Args: symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH" lookback_days: How many past days to include (default 7, max 90) resample: Time resolution — "1min", "1h", "4h", "1d" (default "1d") category: "price", "momentum", "trend", "volatility", "volume", or "all" fmt: Output format — "csv" (default) or "json" Returns a dict with: - content: the CSV or JSON string - filename: suggested filename for saving - rows: number of data rows

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• arbitrage_historyA

  whale-insightsA

  Historical arbitrage opportunities — top 5 per day (MCP-compatible) — Returns a daily history of the top 5 cross-exchange arbitrage opportunities detected by the platform. Each day entry lists the 5 highest-spread opportunities saved by the cron job, including token symbol, spread percentage, buy/sell exchanges, and average USD volume. Useful for AI agents answering questions like 'which tokens appear most frequently in arbitrage?' or 'what is the average daily spread?'. Data is accumulated daily; older than 180 days is automatically purged. Response: { days, history: [{date, opportunities: [{symbol, spreadPct, buyExchange, sellExchange, usdVolume}]}], total, updatedAt }. Query parameter: ?days=7 (default 7, max 180). No authentication required. 60 requests/min rate limit. 5-min in-process cache. — Use this for daily historical data; use the corresponding live snapshot tool for current conditions and the monthly tool for long-term trends.

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• semantic_searchA

  canton-ccpediaA

  PRIMARY tool for open-ended questions: how / why / what-is, troubleshooting a symptom ("why is my balance zero", "how do I fix X"), and locating config or setup steps. Conceptual/meaning-based search over the full Canton corpus (CIPs, docs, forum, mailing lists, proposals, blog, releases, ecosystem, foundation KB, YouTube) using vector+FTS hybrid retrieval with reranking. Canton-specific. Use this FIRST for anything a specific tool does not clearly own; the narrow curated tools (get_faq, find_known_issues, diagnose_error) cover only small hand-picked sets or need a literal error string, so prefer semantic_search for real how/why/config questions. Then call get_doc with a returned id to read the full source page.

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• emem_toolsA

  emem, the verifiable memory protocol for the physical worldA

  The map of emem's tool surface, and the only tool you need to find the rest. Returns the working loop in the order you walk it (name a thing, ground it, cite it, resolve it, verify it, check for drift), then every other tool grouped by the question it answers, each with its one-line trigger. Pass `name` to get one tool's full input schema and a runnable example, so you can use a tool without loading all of the descriptors into context. IF YOU ARE READING A LIST OF 16 TOOLS, YOU ARE SEEING A CURATED SUBSET OF 108, NOT THE WHOLE SURFACE. The count is served in tools/list `_meta` and `_discovery`, and most MCP hosts strip non-standard top-level fields before a model sees them, so it is repeated HERE — a description is the one field every host passes through. The Earth-observation, search, embedding and transparency-log tools are catalogued by this tool and every one of them stays callable by name through tools/call at either endpoint. When to use: Call this FIRST when you do not know which emem tool answers the question, or when you need a capability you cannot see in your tool list. This responder advertises a small core loop by default rather than its full catalog, so a tool being absent from your list does not mean it is absent from the server. Pass `q` to search by topic (`ndvi`, `cloud`, `flood`, `verify`), `name` for one tool's exact schema, or no arguments for the whole map. If you want the full catalog registered as callable tools instead, reconnect to the /mcp/full endpoint; for a one-shot answer without picking a primitive at all, use emem_ask. Example arguments: {"q":"ndvi"}

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• get_chainA

  Diavgis — Greek Public ProcurementA

  A reconstructed procurement lifecycle chain by chain_id: the linked sequence of related acts (e.g. commitment → award → contract → payments) with confidence and link method. Get a chain_id from the chain_id field of any act. A chain is ONE procurement's lifecycle, not everything an act references: an administrative umbrella cited by more than 25 acts (e.g. one annual expense-approval decision referenced by thousands of a buyer's acts) is deliberately NOT merged, so a chain never swallows a year of unrelated activity. Those references are still published — see the act's own references — they simply do not form a chain.

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• lookup_core_symbolA

  Drupal Code QueryA

  Answer "is this core symbol safe to use, and who still uses it" for one symbol. A name that resolves to nothing returns the catalog entries containing it instead. Every user in pages: list_symbol_users. Changed between versions: what_changed. Code text: search_code. - fqn forms: a namespaced name with or without the leading backslash, a bare function or constant name, a prefixed pseudo-symbol, or a fragment. Pseudo-symbols: fn:check_markup, core:hook:preprocess_page, core:library:claro/drupal.shortcut. Fragments: fromRoute, EntityInterface. - Resolved (found true): symbol, with status flags. removed: gone. scheduled_removal: still present, @deprecated gives the removal version. deprecated, internal. placeholder: a catalog row no scanner located. audience: contrib, test_support, test or theme. usage: contrib development branches only, from the evidence rollup as of evidence_built_at. It has projects and branches counts, plus top_projects (≤ 30) by install base. change_records: records that touched the symbol, each with nid for get_change_record. - Not resolved (found false): count: catalog entries containing the fragment, case-insensitive, public symbols only; kind narrows. candidates: a head of up to candidates entries, each with fqn, kind, subsystem, stamps, projects_using, change_record_nids. Entries starting with the fragment come first, then by projects_using. Call again with one fqn.

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• find_referencesA

  ALM X++ MCP ServerA

  Full index scan for all usages of an object, method, field, or label ID. Use for impact analysis before changing or deleting an object. EXPENSIVE — O(1M+ chunks). Prefer find_callers when XRef index is loaded (O(1)). Label IDs: automatically searches both `@SYS124480` and `@SYS:124480` forms. NOT for extensions only — use find_extensions for CoC/event handlers.

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• asterwise_get_dream_symbolsA

  AsterwiseA

  Returns dream symbols from the database with dual-tradition interpretation: Jungian/Western psychological analysis and traditional Vedic dream-symbol meaning. 500 symbols across 8 categories. Optionally filter by category. WORKFLOW: BEFORE: None — standalone. AFTER: asterwise_get_dream_symbol — get full detail for a specific symbol. INPUT CONTRACT: category (optional): One of animals, nature, people, places, objects, actions, body, abstract. Omit for all 500 symbols. DO NOT CONFUSE WITH: asterwise_get_dream_symbol — single symbol detail by name. Full output and error contract: https://docs.asterwise.com/mcp/tools/get-dream-symbols/

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• find_codeA

  ZephexA

  Search the user's project when you do not know which file holds something. Ranked hits; the definition of that name comes first, not a call site like const user = await name(). ALWAYS call instead of guessing a path. ALWAYS call when the user says where is, find, who uses, usages, or rename X everywhere. If they named Zephex or MCP and asked to find something in their code, this is the tool. Prefer this over native Grep when location is unknown — results are ranked and hand off to read_code. intent=symbol — they named a function/class/type. intent=concept — a topic; pass also_try synonyms (rate limit + throttle). intent=snippet — they pasted a line from the editor. intent=everywhere — every occurrence before a rename (whole_word:true). Works on any local project on their machine, any language. Local/stdio: omit path to search the editor cwd, or pass path as their project folder. No disk: inline_files, or a public GitHub URL. Returns summary, data.matches, files_hit, next_calls. Then call read_code with target set to that symbol name, or mode=file/outline with files=[path]. Not for stack/scripts (get_project_context). Not when you already have the exact file and symbol (read_code). Example: find_code({ query: "validateToken", intent: "symbol" }). Rename: find_code({ query: "OldName", intent: "everywhere", whole_word: true }). Topic: find_code({ query: "encrypt", intent: "concept", also_try: ["cipher", "AES"] }). If the first hit is the wrong file, follow next_calls or tighten with file_pattern / include=code. Do not fall back to guessing a path.

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• analyze_stockA

  hpsilab-mcpA

  Aggregate all quant tools into one JSON stock analysis. The tool reuses the existing MCP tools as its data sources, then derives a direction signal, direction score, bullish factors, bearish factors and plain-English summary. If one underlying tool is gated, unavailable or raises an error, the remaining tools still contribute to the final result (status "partial"); if every underlying tool fails, the whole call fails (status "error", isError=True) instead of a misleadingly "successful" empty analysis. Args: symbol: Stock symbol, e.g. "NVDA". refresh: Request fresh IV Radar data instead of using the backend's fresh IV cache. Defaults to False. lang: Language for `summary`, `bullish_factors` and `bearish_factors` - "en" (default), "zh" or "ja"; regional forms like "zh-CN" are accepted. Everything else in the response, `signal` included, is language-independent, so an existing caller that omits this gets byte-identical output to before.

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• generate_stock_research_reportA

  hpsilab-mcpA

  Full markdown research report with five stock-report charts. Pro tool ($0.35/call via x402 for anonymous callers; free within plan limits for signed-in accounts, subject to a monthly report quota). Runs analyze_stock and stock-report image generation concurrently, then renders a presentation-ready markdown report (direction, direction score, bullish / bearish factors, source-tool status, and the five chart embeds). The markdown is returned for display and the same data is mirrored in structured JSON. Signed-in hpsilab users call this within their plan's free rate limits. Anonymous / tokenless agents pay per call via x402 (USDC on Base) when payments are enabled — send the x402 payment in the request _meta. Args: symbol: Stock symbol, e.g. "RXRX". refresh: Bypass the backend's fresh IV cache for the IV-driven modules. Defaults to False. force_images: Force a fresh image render instead of reusing the backend's image cache. Defaults to False.

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• search_imagesA

  Source LibraryA

  Search 200,000+ historical illustrations, emblems, engravings, diagrams, AND 24,000+ artworks (paintings, prints, sculptures). Filter by type, subject, figure, symbol, year. Results interleave two collections: illustrations extracted from book pages (each with a page number and book link) and standalone museum artworks (type: "artwork"). The first few results also return as inline images YOU can see. Hosts that support MCP Apps render an in-chat image gallery for this tool automatically; on other clients images may sit inside the collapsed tool-result view, so never tell the user images are "rendered above" unless the gallery appeared — describe what you see and give each image's url link instead. Every image_url is public and stable — an HTML page that references them directly works in any online browser. If images.length is 0, read the note field — an empty result under a book_id filter means that book has no EXTRACTED images yet, not that the physical book has no plates. A broad query can match tens of thousands (read total): narrow with type/subject/symbol/iconclass or page with offset instead of raising limit. On museum-artwork results, a title_is_descriptive flag means the title is an AI description of the picture rather than a title the work was published under — cite such a record by its source_record_title, never by the descriptive one (#4288).

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• get_stocksA

  Stocklake — AI Stock IntelligenceA

  Batch stock data for up to 25 symbols in a single call — the same fields get_stock returns for the same key/symbol, so this is a true batch version, not a thinned-down scan. Returns a dict keyed by symbol. Missing symbols are omitted from the result. Each symbol in the batch counts as one call toward the daily limit. A request over 25 symbols is rejected outright (error: batch_too_large) rather than silently served on just the first 25 — split a larger list into multiple calls. Available to all tiers (fundamentals/indicators/company profile, free). Pro tier adds, per symbol, the same precomputed blocks get_stock adds — rating {score, direction, signals}, signals (per-indicator breakdown), relative_strength, market_risk {beta_spy_1y, corr_spy_1y}, and the minimum AI-narrative slice (ai_verdict, ai_headline, ai_score, ai_score_band). None of this costs a live AI call — it's all precomputed and just needs projecting. NOT included, even on pro — call get_stock(symbol) for stance_signals, or get_stock_research(symbol) for the full ai_summary text (summary/key_points/ risks/near_term/longer_term) plus cross-source news/insider/signal context. Response also carries `duplicates_collapsed`: how many input symbols normalized (case-folding, share-class aliasing e.g. "BRK.B"->"BRK-B") or literally repeated onto a symbol already counted elsewhere in this batch. requested - len(missing or []) - duplicates_collapsed == count always holds.

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• plan_buildA

  space0A

  Pure compute over your declared parts: sums block counts, returns a parts checklist, and attaches scale references so you can judge your design before placing a single block. You declare the parts; this computes block counts + scale references. It does not invent geometry. Workflow: call plan_build first to get target_blocks + scale_refs, then call set_goal(description, target_blocks, footprint, height) to persist the plan — set_goal is where plan metadata is stored. Returns { target_blocks, parts_checklist, scale_refs, overlap_warning? }. goal_id is accepted but is a no-op in this tool; persistence happens via set_goal.

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• searchA

  mcpA

  Search Quantustik for S&P 500 tickers by symbol or company name. Paired with fetch — this is the two-tool "search"/"fetch" convention ChatGPT connectors and deep-research clients expect from an MCP server: call search first to get lightweight hits, then fetch(id) on the one(s) worth reading in full. Args: query: Ticker symbol (e.g. "NVDA") or company-name substring (e.g. "nvidia", "apple"). Case-insensitive. Returns a dict with a `results` list of up to 10 {id, title, url} objects — id is the ticker symbol, ranked exact-symbol match first, then company-name/ticker prefix, then substring. Empty query or no scan data returns an empty list, never an error.

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• create_alertA

  Manawa TerminalA

  Create a price or event alert for a symbol. Args: symbol: Stock symbol (e.g. "AAPL") alert_type: "target_price", "movement_pct", or "event" direction: Required for price alerts — "above", "below", or "either" threshold: Required for price alerts — price level or percentage move notes: Optional description event_types: Required when alert_type is "event". One or more of: "earnings_transcript", "insider_trade", "news_stock", "press_release", "filing_8k", "filing_13f", "politician_senate", "politician_house", "merger_acquisition"

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• get_geneA

  can-immuneA

  Get the full profile for one gene by exact symbol. Returns total mutations and how they split between cell lines vs tissues, unique mutant peptides, sample and transcript counts, UniProt accession / name / reviewed status, the top ~15 recurrent mutations in that gene, and a link to the gene page. Use this after `search_genes` (or when you already know the symbol) to answer detailed questions about a single gene. Returns an error field if the symbol is not found.

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• submit_receiptA

  ExpenseBotA

  Submit a photo or PDF of a receipt for processing. Covers requests phrased as 'log this', 'log this receipt', 'save this receipt', 'expense this', or 'add this to my expenses', including when the user simply shares a photo of a receipt or invoice. The receipt image is validated, uploaded to cloud storage, and processed by AI to extract vendor, amount, date, tax, and category. The expense appears in the user's spreadsheet in about 1-3 minutes, and longer for PDFs or large batches. Handles images and PDFs, mixed together in one batch. TO SEND FILES (preferred, and required for PDFs): call this tool with filesToUpload listing every file the user gave you. It returns one signed upload URL per file. Upload them ONE AT A TIME with an HTTP PUT, telling the user which file you just finished and how many remain, then call this tool ONCE with uploadRefs for all of them — that processes the whole set as a single batch, like the ExpenseBot web app. Do not call this tool once per file. Use the photo parameter for one image or PDF attached in ChatGPT. MCP clients that cannot supply file references may use photoBase64 for one small image; use the upload flow for large files or batches. Optional note and tag values use the same receipt metadata path as ExpenseBot's camera, file uploader, and forwarded-email intake. The note is stored in the Notes column (L); the tag is stored in the Tag column (K). Batch defaults apply to every file, and each uploadRefs item may override either value for that file.

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• defi_token_pricesA

  MCPFax Macro & Market IntelligenceA

  Price arbitrary on-chain tokens with DefiLlama's coins API, addressed as chain:address (or coingecko:id), returning price in USD, symbol, decimals, a confidence score and the pricing timestamp for each. Use it to value a DeFi position holding tokens no exchange lists. This is NOT the exchange spot-price tool: use the crypto spot tool for a plain price of a major asset. A token DefiLlama cannot price inside search_width returns null, never a substituted price. Costs $0.005 USDC per call via x402 on Base; an unpaid call returns the payment challenge instead of data, and a call that returns no data is never settled so it costs nothing. Equivalent HTTP route: GET /defi/token-prices.

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