"An MCP for extracting and analyzing documentation using AI" matching MCP tools:

• neuron_loginA

  neuronB

  Authenticate with Neuron. Three modes: 1. No args: Opens a browser URL for secure authorization (recommended — no credentials shared with AI) 2. token: Paste an MCP token from the Neuron dashboard 3. email+password: Legacy login (credentials visible to AI)

  ConnectorNo auth

• get_adoption_stageA

  Stratalize GovernanceB

  Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.

  ConnectorNo auth

• cti_get_cross_server_routesA

  website-searchA

  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.

  ConnectorNo auth

• 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`.

  ConnectorNo auth

• 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`.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize HealthcareB

  Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize Real EstateB

  Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize Crypto & DeFiB

  Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize IntelligenceB

  Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize FinanceB

  Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize OracleB

  Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework. $0.02 USDC per call.

  ConnectorNo auth

• check_appointment_askA

  Pinpoint dealership sales toolsA

  Deterministic check of one dealership appointment ask using six fixed text-pattern rules: day, clock time, holding the time, confirmation question, vehicle mention or readiness phrase, and follow-up. Returns a structured score, per-part flags and fixes, and a fixed template example that reuses a detected day and time or supplies sample values when absent. Runs without an AI model. Use this for repeatable rule-based scoring; use review_appointment_ask for contextual AI feedback and a tailored rewrite.

  ConnectorNo auth

• check_appointment_askA

  Pinpoint dealership sales toolsA

  Deterministic check of one dealership appointment ask using six fixed text-pattern rules: day, clock time, holding the time, confirmation question, vehicle mention or readiness phrase, and follow-up. Returns a structured score, per-part flags and fixes, and a fixed template example that reuses a detected day and time or supplies sample values when absent. Runs without an AI model. Use this for repeatable rule-based scoring; use review_appointment_ask for contextual AI feedback and a tailored rewrite.

  ConnectorNo auth

• list_issuesA

  FlowseryA

  List issues the AI found while analyzing session recordings: bugs, broken flows, and UX problems, deduplicated across sessions and ranked by severity (or by last seen with sort recency). Each row has title, severity, status, sessions affected, and first/last seen; the response also carries site-wide open, in_progress, and resolved counts plus pagination.total. Start here for 'what is broken', then call get_issue with an id for occurrences, steps to replicate, and comments. Suspended issues are hidden unless status is suspended, so an issue that vanished was probably suspended, not deleted. Limit defaults to 100 (max 1000). Requires websiteId or domain with a workspace token.

  ConnectorOAuth

• ir_get_cross_server_routesA

  website-searchA

  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.

  ConnectorNo auth

• malware_get_cross_server_routesA

  website-searchA

  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.

  ConnectorNo auth

• check_urlA

  Not Human SearchA

  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.

  ConnectorNo auth

• searchA

  developer-toolkit-mcpA

  Search the AI Developer Toolkit documentation: 950+ guides on Cursor, Claude Code and OpenAI Codex, covering setup, agent workflows, hooks, MCP, testing, CI and deployment, in English and Polish. Returns at most 10 ranked results, each with a short snippet rather than the article text; an empty list means the corpus has nothing on the topic. Pass a result id to `fetch` for the full text.

  ConnectorNo auth

• droptrack_generate_artist_bioA

  DropTrackB

  Queue an AI artist bio draft for the authenticated company. Poll with droptrack_get_ai_job using jobType=artist_bio.

  ConnectorOAuth

• search_anthid_siteA

  AnthidA

  Search anthid.com — the product, pricing, and company pages — and return matching excerpts with their URLs. Use this for questions about what Anthid is, what it costs, which brokers it connects, or how to get in touch. For endpoints, parameters, and payload schemas, use this server's API tools — search_anthid_api and describe_anthid_endpoint. For narrative guides and streaming client code, which neither corpus here carries, use the documentation MCP server at https://docs.anthid.com/mcp.

  ConnectorNo auth

• generate_imageA

  Claude ImagineA

  Generate an AI image from a text prompt using Claude Imagine credits. Returns the image inline plus its full-resolution URL. To change an existing image, use edit_image. For a YouTube thumbnail, book cover, album or podcast cover, poster, blog cover or product photo, call list_styles first and pass one of its ids as style.

  Connector

  Destructive

  OAuth