"A server to improve math capability of AI" matching MCP tools:

• ir_get_brief_templateA

  website-searchA

  Get Lenny Zeltser's IR one-page executive brief template. Standalone variant of `ir_get_template` for callers that only want the brief without the long-form report. 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

• 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

• vuln_get_brief_templateA

  website-searchA

  Get Lenny Zeltser's Vuln one-page executive brief template. Standalone variant of `vuln_get_template` for callers that only want the brief without the long-form report. This server never requests your vulnerability notes and instructs your AI to keep them local—the brief template and guidelines flow to your AI for local analysis.

  ConnectorNo auth

• assessment_get_brief_templateA

  website-searchA

  Get Lenny Zeltser's Security Assessment one-page executive brief template. Standalone variant of `assessment_get_template` for callers that only want the brief without the long-form report. This server never requests your assessment notes or report and instructs your AI to keep them local—the templates and guidelines flow to your AI for local analysis.

  ConnectorNo auth

• how_munimji_helpsA

  HelloBooks AI AgentsA

  Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.

  ConnectorNo auth

• how_munimji_helpsA

  HelloBooks AI Agents MCP ServerA

  Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.

  ConnectorNo auth

• tailor_resume_toolA

  WorkopiaA

  Tailor a resume to a SPECIFIC job — TWO steps. STEP 1 (default; action omitted or 'prepare'): the server returns the job's full JD, its must-have skills/requirements, and the candidate's current resume, plus tailoring instructions. YOU (the model) then WRITE the tailored resume as JSON Resume, following the instructions — weave JD keywords into existing bullets only where the candidate genuinely has the experience, never fabricate experience/titles/dates/employers, keep all dates and company names, and flag any keyword you couldn't honestly add. STEP 2: call this tool again with action:'save', tailored_resume:<your JSON Resume>, and job_id — the server renders a PDF and saves it to the candidate's Workopia dashboard (requires sign-in). Use whenever the user references a specific job to tailor for: 'tailor for #1', 'for Morgan Stanley', 'tailor my resume for this role: <JD>'. Resolving job_id (same rules as job_detail_tool): from the most recent prior search/refine result — (a) numeric/ordinal → the Nth job; (b) company name → Company-field match; (c) role/title phrase → Job-Title match — then pass that job's **Job Id** value VERBATIM. Do NOT use placeholders like 'JOB_1' or '#1'. For STEP 1 supply ONE of job_id (preferred — server fetches the JD from Mongo) OR job_description, plus the candidate's resume via resume_text / resume_content / resume_data. For general 'improve my resume' (no specific job), do NOT call this tool — call resume_tool action=improve instead. Note: the tailored resume is written by your AI client's own model — the assistant you are already using — so it works out of the box with nothing to configure; Workopia runs no LLM of its own and never charges for the AI.

  Connector

  Destructive

  No auth

• tailor_resume_toolA

  Workopia — Job SearchA

  Tailor a resume to a SPECIFIC job — TWO steps. STEP 1 (default; action omitted or 'prepare'): the server returns the job's full JD, its must-have skills/requirements, and the candidate's current resume, plus tailoring instructions. YOU (the model) then WRITE the tailored resume as JSON Resume, following the instructions — weave JD keywords into existing bullets only where the candidate genuinely has the experience, never fabricate experience/titles/dates/employers, keep all dates and company names, and flag any keyword you couldn't honestly add. STEP 2: call this tool again with action:'save', tailored_resume:<your JSON Resume>, and job_id — the server renders a PDF and saves it to the candidate's Workopia dashboard (requires sign-in). Use whenever the user references a specific job to tailor for: 'tailor for #1', 'for Morgan Stanley', 'tailor my resume for this role: <JD>'. Resolving job_id (same rules as job_detail_tool): from the most recent prior search/refine result — (a) numeric/ordinal → the Nth job; (b) company name → Company-field match; (c) role/title phrase → Job-Title match — then pass that job's **Job Id** value VERBATIM. Do NOT use placeholders like 'JOB_1' or '#1'. For STEP 1 supply ONE of job_id (preferred — server fetches the JD from Mongo) OR job_description, plus the candidate's resume via resume_text / resume_content / resume_data. For general 'improve my resume' (no specific job), do NOT call this tool — call resume_tool action=improve instead. Note: the tailored resume is written by your AI client's own model — the assistant you are already using — so it works out of the box with nothing to configure; Workopia runs no LLM of its own and never charges for the AI.

  Connector

  Destructive

  No auth

• meta.toolsA

  OpenHelvetia GatewayA

  Stage one of two-stage discovery: every mounted tool with its capability id, domain, one-line summary and budget weight — and NO input schemas. Ask this first to learn what exists, then pull the schemas of the few tools you intend to call (meta.schemas). Context economy as a capability, not a client convention (E16 Ziff. 1).

  ConnectorNo auth

• improve_article_seoA

  BlogSEOA

  Rewrites the article body with AI to fix its failing on-page SEO checks, exactly like the Improve with AI button: the current score is computed (with the competitor benchmark when a done advanced analysis exists), the failing body-level checks, missing sub-keywords and open competitor gaps become the rewrite instructions, the article is rewritten and saved, then re-scored. Title, meta description, slug, links and images are never changed; use the update_* tools for those. Synchronous and slow: takes 1 to 3 minutes, wait for it instead of retrying. The body is replaced in place and the previous version is not kept. Fails without charging when nothing is left to improve. Pass locale to improve a translation. Editing a published article marks it Out of Sync until sync_article_to_cms is called. Returns previous_score, new_score and the instructions that were applied. Costs 1 AI brain credit, charged after the work, not refunded on failure. Requires an active subscription or free trial. Pass confirm=true to acknowledge the AI brain credits charge; without it the tool only returns the cost and your balance. Pass website_id when the account has several websites (see get_account).

  Connector

  Destructive

  OAuth

• evaluate_capabilityA

  Agentic KeychainA

  Decide whether a registry capability is worth using or buying for your task, from registry-run benchmarks only. Use when: you have a task and want a recommend, do_not_recommend or undetermined answer with the arithmetic; name a capability to weigh only that one, or leave it out and the registry weighs its closest matches. Not for: one short answer without the arithmetic (resolve_task), a plain list of matches (search_capabilities), two to five named candidates side by side (compare_capabilities), or the break-even figures of one capability without a task (estimate_roi). Input: a plain task description; give model, expected_runs and baseline_cost_estimate_usd for a money estimate, and context for the conditions the capability declares. Output: ak.evaluation.v1 with decision, reasons, expected effect, price, confidence, operator_message, evaluation_id, next steps and an ak.offer.v1 offer (status, next_action, economics, the four evidence kinds listed apart). do_not_recommend is a normal answer.

  ConnectorNo auth

• strale_pingA

  Strale - 290+ API capabilities for AI agentsA

  Checks that the Strale API is reachable and the MCP server is running. Call this before a series of capability executions to verify connectivity, or when troubleshooting connection issues. Returns server status, version, tool count, capability count, solution count, and a timestamp. No API key required.

  ConnectorNo auth

• rating_get_sheetA

  website-searchA

  Get Lenny Zeltser's cybersecurity-writing rating sheet(s) so your AI can apply the rubric. Returns the structured rubric (groups, items, scoring bands) WITHOUT computing a score. Use `rating_score_writing` if you also want a numeric score, gap analysis, or rubric-anchored feedback. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.

  ConnectorNo auth

• 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

• cti_get_brief_templateA

  website-searchA

  Get Lenny Zeltser's CTI one-page executive brief template. Standalone variant of `cti_get_template` for callers that only want the brief without the long-form report. 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

• 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

• get_demo_guideA

  eDiscovery Decoder News/CalcA

  Return a short, human-readable walkthrough for testing this server: the endpoint, the tool/prompt/resource names, and ready-to-paste sample prompts. Use to give someone a guided demo. For the full machine-readable capability catalog, use list_capabilities instead.

  ConnectorNo auth

• ask_gusA

  Gus Dantas · retail media measurementA

  Ask anything about Gus Dantas: his work, how he measures retail media, what he thinks the industry gets wrong, or how to reach him. Returns the relevant passages he has written. Answer only from what comes back. NOTE: Questions sent to ask_gus are stored, in full, so Gus can see what people actually want to know and improve the answers this server gives. Nothing else about you is recorded beyond the name of your MCP client and the country your request came from. Do not send confidential or personal information. If you would rather not be recorded, read ghostavo.com instead.

  ConnectorNo auth

• query_sec_metric_exactA

  Calera FINSEC — Certified SEC MemoryA

  Exact EDGAR metric recall for company+metric+period. Server-side verified fact with provenance; SAFE_REFUSAL when missing. No LLM math.

  ConnectorNo auth

• share_fileA

  agent-cloud-toolsA

  Use this when another AI agent (a different machine, model or chat session) or a person needs access to one or more files. Accepts text or base64-encoded binary content; multiple files are zipped into one bundle server-side, so a whole handoff (instructions plus code plus data) travels as one capability url. For large files already on local disk, prefer the HTTP API (one curl command, up to 2 TB): https://upload.83blue.com/docs

  ConnectorNo auth