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620,636 tools. Updated 2026-09-29 03:51

"A server for automatically scanning and loading database table structures into AI agent context" matching MCP tools:

  • Run a read-only SQL query against an app's Postgres database and return up to 200 result rows. SELECT only — writes and DDL (INSERT/UPDATE/DELETE/ALTER/DROP/…) are rejected server-side; use vibekit_chat or vibekit_submit_task to have the agent make data or schema changes. Call vibekit_db_schema first to learn the tables. SQL string, max 5000 chars.
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  • Analyze text for writing style issues: weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs, and research-cited AI tells. Read-only and stateless — text is analyzed in memory on the hosted server and never stored. Returns a plain-text report with each issue's line and column, the matched text, surrounding context, and the reason for AI tells; texts over 100,000 characters return an error message. This hosted server has no filesystem access — the wsc-mcp npm package adds a check_file tool for local files. It only reports issues — to auto-remove duplicate words, follow up with fix_duplicates.
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  • Get Lenny Zeltser's expert CTI writing guidelines. Topics include tone, words, structure, executive_summary, voice, articles, summary, brief (one-page brief section guidance), handoffs (cross-server routing), methodology (the three subsections), fields (per-field guidance), and CTI-specific topics: attribution (full Six Signals prose), confidence (ICD-203 ladder), pyramid_of_pain, six_signals (signals table only), and anti_patterns. The general writing topics (tone/words/structure/executive_summary) now defer to `get_security_writing_guidelines` for the canonical Five Elements rules; CTI-specific content lives in the other topics. Pair the 'fields' topic with field_id for single-field guidance. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.
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  • Get Lenny Zeltser's CTI cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `cti_load_context`. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.
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  • Fetches a domain's homepage and checks for content patterns that could constitute prompt injection attacks against AI agents that visit and ingest the page. Signals include hidden text, invisible divs, `<!-- AI: ignore -->` style comments, and known injection patterns. Use this tool when: - You are vetting a domain before feeding its content into an LLM context. - You want to assess the prompt injection risk of a URL before browsing it with an agent. - You are auditing a set of domains for adversarial AI content. Do NOT use this tool when: - You want tracker surveillance data — use `get_domain` instead. - You want AI training opt-out signals — use `intel_optout` instead. - You want the agent surface (MCP/OpenAPI) — use `intel_agent` instead. Inputs: - `domain` (query, required): Domain to scan. Returns: - `injection_signals`: list of signal types detected (e.g., `hidden_text`, `ai_instruction_comment`, `invisible_div`). - `risk_level`: `none`, `low`, `medium`, or `high` based on signal count and type. Cost: - Free. No API key required. Latency: - Typical: 2-4s (HTML fetch), p99: 7s.
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  • Connectivity check that confirms the Nordic MCP server process is responding. Use this at the start of a session to verify the server is reachable before making other calls. Do not use as a proxy for database health — the server can respond while the Qdrant vector database is temporarily unavailable. To confirm data availability, call search_filings directly. Returns: A greeting string: "Hello {name}! Nordic MCP server is running."
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Matching MCP Servers

  • F
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    C
    maintenance
    Converts Excel table definitions to structured JSON and exposes them to LLMs via MCP tools like list_tables and get_table_schema, enabling accurate SQL generation and data modeling assistance.
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  • A
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    Not graded
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    A
    maintenance
    A lightweight MCP server for semantic search over markdown knowledge bases, enabling AI coding agents to index, search, and answer questions from local markdown documents.
    MIT

Matching MCP Connectors

  • Agent-work MCP: free context preflight and page-read previews, paid x402 HTTP upgrades.

  • MobilityData's open catalogue of public-transport feeds — ~4,500 GTFS schedule feeds and ~1,900…

  • Analyze text for writing style issues: weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs, and research-cited AI tells. Read-only and stateless — text is analyzed in memory on the hosted server and never stored. Returns a plain-text report with each issue's line and column, the matched text, surrounding context, and the reason for AI tells; texts over 100,000 characters return an error message. This hosted server has no filesystem access — the wsc-mcp npm package adds a check_file tool for local files. It only reports issues — to auto-remove duplicate words, follow up with fix_duplicates.
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  • Translate a plain-language question into a candidate SQL query using pattern-matching against the live schema (no AI model — simple questions only: counts, averages, filtered selects on a named table). Returns the SQL without executing it, with a confidence score; low confidence means the table was guessed. Review the statement and tables_used, then run it with scalix_db_query. For complex questions, read scalix_db_schema and write the SQL directly.
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  • First step of setting up a new data integration: creates a data spec. By default (sourceType "file") this returns presigned upload URL(s) for the sample file (and optional format/target-schema file) — upload the file(s) per the returned instructions, then call finish_data_source_onboarding with the returned specId to kick off AI analysis and wait for it to complete. Use sourceType "tables" instead when the request is to derive/aggregate data that is ALREADY loaded into workspace tables — e.g. "build me a daily summary of the customers table", or "set up a job that reads from the orders table and maintains a running total" — rather than loading a new file. It generates a SQL query (INSERT or MERGE, per `merge`) via AI instead of a Python parser, run through the query engine instead of a Glue job. There are never sample/format files, but targetOption still works the same three ways as sourceType "file" (see targetOption below) — so this call returns files: [] and you can call finish_data_source_onboarding immediately UNLESS targetOption is "target-schema-file", in which case it returns one upload URL for that file, same as the file-source path. The generated SQL automatically windows itself to rows added since the spec's last successful run. sourceType "tables" ALSO requires autoRefresh — how this spec stays up to date is not optional to decide, and must not be inferred from other jobs/triggers that happen to already exist in the workspace: ask the user whether it should re-run automatically whenever a specific upstream spec finishes loading ("spec_success" — the natural choice when the request is "run this after X finishes/loads"), on a plain cron-like cadence ("schedule" — the natural choice when the request is "run this every day/hour" with no mention of depending on another job), or stay manual-only ("none" — re-run later with run_data_job). If the request already states the timing unambiguously, that answers it; otherwise ask before calling this tool. Getting this wrong either way has a real cost: "none" means the summary silently goes stale until someone remembers to re-run it by hand, while an unwanted trigger keeps re-running (and charging credits for) a spec the user only wanted once. See autoRefresh below.
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  • Run a SQL query in the project and return the result. Prefer the `execute_sql_readonly` tool if possible. This tool can execute any query that bigquery supports including: * SQL Queries (`SELECT`, `INSERT`, `UPDATE`, `DELETE`, `CREATE`, etc.) * AI/ML functions like `AI.FORECAST`, `AI.KEY_DRIVERS`, `ML.EVALUATE`, `ML.PREDICT` * Any other query that bigquery supports. Example Queries: ```sql -- Insert data into a table. INSERT INTO `my_project.my_dataset`.my_table (name, age) VALUES ('Alice', 30); -- Create a table. CREATE TABLE `my_project.my_dataset`.my_table ( name STRING, age INT64); -- DELETE data from a table. DELETE FROM `my_project.my_dataset`.my_table WHERE name = 'Alice'; -- Create Dataset CREATE SCHEMA `my_project.my_dataset` OPTIONS (location = 'US'); -- Drop table DROP TABLE `my_project.my_dataset`.my_table; -- Drop dataset DROP SCHEMA `my_project.my_dataset`; -- Create Model CREATE OR REPLACE MODEL `my_project.my_dataset.my_model` OPTIONS ( model_type = 'LINEAR_REG' LS_INIT_LEARN_RATE=0.15, L1_REG=1, MAX_ITERATIONS=5, DATA_SPLIT_METHOD='SEQ', DATA_SPLIT_EVAL_FRACTION=0.3, DATA_SPLIT_COL='timestamp') AS SELECT col1, col2, timestamp, label FROM `my_project.my_dataset.my_table`; ``` Queries executed using the `execute_sql` tool will always have the default job label `goog-mcp-server: true` automatically set in addition to any custom `labels` provided in the request. Queries are charged to the project specified in the `project_id` field. Query Execution Behavior: * If the query completes within the synchronous timeout (default 20 seconds or custom `timeout_ms`), the tool returns `job_complete: true` and the initial result rows directly. For fast queries, `job_id` may be omitted as no persistent background job is created; no further action or polling is needed. * If the query takes longer than `timeout_ms`, the tool returns `job_complete: false` and a `job_id`. In this case, use the `get_query_results` tool with `job_id` to poll until `job_complete: true`, or use `cancel_job` to abort the running query. * You can optionally specify `timeout_ms` to configure the maximum synchronous wait time in milliseconds (defaults to 20,000 ms), and `job_timeout_ms` to enforce a hard server-side timeout after which BigQuery automatically terminates the job.
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  • Introspect the full database structure for a connection: every table, column, type, primary key, foreign key, and index — the map to build queries against an unfamiliar database. Use analyze_table or data_profile instead when you need column VALUES (stats, nulls, PII) rather than structure. Read-only against your database. Results are cached 1 hour per connection; pass refresh=true to force a live re-introspection and bypass that cache, e.g. right after altering the schema. Does not consume daily quota. Returns tables with their columns, types, keys, and indexes.
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  • Run a WRITE SQL statement against the project's Postgres database — CREATE/ALTER TABLE, INSERT, UPDATE, DELETE, DROP, migrations. Destructive statements are allowed but your MCP client will show the user the SQL and ask them to approve it (they can allow once or for the session). Schema-changing statements (CREATE/ALTER/DROP of tables, types, …) automatically re-pull the typed schema helper and return the updated schema — no separate pull_database_schema call needed. Pass `database` only if the project has more than one. The query runs in a single transaction by default; set no_transaction for statements that cannot run inside a transaction block (VACUUM, CREATE INDEX CONCURRENTLY, …). Queries are killed after 90 seconds either way.
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  • The "always start here" premium call for autonomous agents. Composes multiple public/gov upstream sources into a curated world-state snapshot: Fed funds rate, USD-base forex (EUR/JPY/GBP/CHF), HN front page top 5, significant earthquakes 24h, upcoming space launches, top Polymarket markets, and infrastructure status (GitHub, Cloudflare, OpenAI, Anthropic). Returns BOTH a structured JSON `context` object for parsers AND a pre-formatted `system_prompt` string the agent pastes verbatim into its LLM context. Saves the agent from making many separate calls and writing a formatter. Curation choice (which signals matter, how to compress them) is the moat. Costs 2 credits ($0.04 USDC). 5-min cache. Bearer auth required. Note: crypto (BTC, Fear and Greed) and VIX legs were removed 2026-07-23 for market-data licensing compliance.
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  • Get Lenny Zeltser's IR cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `ir_load_context`. This server never requests your incident notes and instructs your AI to keep them local—guidelines flow to your AI for local analysis.
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  • Get Lenny Zeltser's Malware cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `malware_load_context`. This server never requests your sample, analysis notes, or indicators and instructs your AI to keep them local—guidelines and the report template flow to your AI for local analysis.
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  • On-demand agentic-readiness check for any URL. Runs the NHS 7-signal crawler live (llms.txt, ai-plugin.json, OpenAPI, structured API, MCP server, robots.txt AI rules, Schema.org) and returns a score 0-100 with per-signal breakdown. Use before calling an unfamiliar API to confirm it's agent-usable. Re-runnable without the submissions-table side-effect of submit_site — ideal for verify-before-use workflows.
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  • Return up to `limit` records (default 1000, max 5000) from a collection — for snapshotting into agent context. For larger sets use the CLI: `shiply data export <slug> <collection>`.
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  • Ask the IntoDNS.ai AI service for a plain-language explanation of one specific issue (e.g. `spf_missing`, `no_dnssec`). Returns severity, business impact, root cause, and recommended fix steps as structured text. Read-only POST to /ai/explain — never mutates DNS or domain state. Provide `domain` and `issue` (enum); pass `context` from prior scan output (e.g. scan_domain result) for higher-quality answers. Use after scan_domain when an agent needs to walk a user through *why* a finding matters; use generate_dns_fix for the actual DNS record snippet that resolves it.
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  • L3 Empathy Response Strategy: converts text into a deterministic response strategy (approach, tone temperature, pacing, focus points, avoid-list) for AI companions and conversational agents. Deterministic table lookup, no LLM, ~20ms; privacy-first. Not a medical or therapeutic tool.
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  • Attest the connected DropTrack MCP stage, base URL, non-secret database fingerprint, configured database-target match, Lambda identity, region, and authorization role. Call this before any write. Require databaseTargetMatchesExpected=true, compare stage, base URL, and fingerprint to the canonical environment table, then pass the exact stage and database fingerprint to guarded write tools. Never infer environment from company data alone.
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  • Any question the other tools do not cover, as one read-only SQL statement over the archive database. SELECT or WITH only; a LIMIT is imposed if you omit one. Call `read_first` before computing anything and `list_datasets` to find table names. A query estimated to read more than 250,000 rows is refused — narrow it with a WHERE, or ask for one table at a time.
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