"Designing a memory system for persona-based agents to improve user experience" matching MCP tools:

• linux_audit

  ZEN SecDB

  Perform a Linux package vulnerability audit using SecDB. ## What this tool does Analyzes the installed packages of a Linux system-identified by OS and OS version-and returns vulnerability information plus a Markdown summary. The audit results are based exclusively on the package list provided by the user. ## When to use this tool Use this tool when the user wants to determine: - whether installed packages contain known vulnerabilities - whether a host, VM, container, or base image is affected by security advisories - which packages require patching or upgrading If the user does not know the valid values for `os` or `version`, first call the `linux_os` tool to retrieve the exact supported combinations. ## Inputs - **os**: Linux distribution identifier supported by SecDB (use `linux_os` to obtain allowed values). - **version**: OS version or codename corresponding to the selected distribution. - **packages**: list of installed packages, **one per line**, generated using the appropriate system command: ### For RPM-based distributions (RHEL, CentOS, Rocky, Alma, SUSE) rpm -qa --qf '%{NAME}-%{VERSION}-%{RELEASE}.%{ARCH}\n' ### For DEB-based distributions (Ubuntu, Debian) dpkg-query -W -f='${Package} ${Version} ${Architecture}\n' ### For Alpine Linux apk list -I The raw output of these commands can be passed directly as the `packages` input (one package per line). ... python3 3.12.3-0ubuntu2.1 amd64 systemd 255.4-1ubuntu8.10 amd64 tmux 3.4-1ubuntu0.1 amd64 ... ## Outputs - **report**: structured objects describing the advisories affecting the audited packages. - **summary**: Markdown summary including total vulnerabilities, severity breakdown, and key findings. ## LLM usage guidelines - Never guess whether a package is vulnerable-always call this tool for Linux audits. - If `os` or `version` is unclear or missing, call `linux_os` and ask the user to choose a valid combination. - Normalize the package list to “one entry per line” if the user provides unstructured output. - The `summary` is already Markdown and can be shown directly. - Use `report` when deeper technical analysis is required.

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• optimize_prompt

  pqs-mcp-server

  Rewrite a prompt to score higher on the PQS rubric, AND show before/after output comparisons so the user can see the impact. Returns the optimized prompt, the original PQS score, the optimized PQS score, and side-by-side sample outputs from a frontier model using both versions. USE WHEN: - The user got a low score from score_prompt and asks how to improve. - The user explicitly asks to "improve" / "rewrite" / "fix" / "optimize" a prompt they pasted. - The user is dissatisfied with output quality from a previous prompt and asks how to get better results. - score_prompt returned a suggestion to invoke this tool. DO NOT USE WHEN: - The user just asked for a score (use score_prompt only — don't double up). - The user wants you to write a new prompt from scratch (write it directly). REQUIRES: A PQS API key from a Pro subscription ($19.99/month, 1,000 calls/mo, includes batch + A/B comparison). If the user has not provided one, the tool returns a clear subscription URL — pass that response to the user verbatim. Do not invent or guess API keys. There is no free trial of this tool; the user must subscribe before the first call. COST: Counted against your Pro subscription's monthly call quota. LATENCY: ~6-8 seconds.

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• optimize_prompt

  PQS - Prompt Quality Score

  Rewrite a prompt to score higher on the PQS rubric, AND show before/after output comparisons so the user can see the impact. Returns the optimized prompt, the original PQS score, the optimized PQS score, and side-by-side sample outputs from a frontier model using both versions. USE WHEN: - The user got a low score from score_prompt and asks how to improve. - The user explicitly asks to "improve" / "rewrite" / "fix" / "optimize" a prompt they pasted. - The user is dissatisfied with output quality from a previous prompt and asks how to get better results. - score_prompt returned a suggestion to invoke this tool. DO NOT USE WHEN: - The user just asked for a score (use score_prompt only — don't double up). - The user wants you to write a new prompt from scratch (write it directly). REQUIRES: A PQS API key from a Pro subscription ($19.99/month, 1,000 calls/mo, includes batch + A/B comparison). If the user has not provided one, the tool returns a clear subscription URL — pass that response to the user verbatim. Do not invent or guess API keys. There is no free trial of this tool; the user must subscribe before the first call. COST: Counted against your Pro subscription's monthly call quota. LATENCY: ~6-8 seconds.

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• nexbid_purchase

  discovery

  <tool_description> Initiate a purchase for a product found via nexbid_search. Returns a checkout link that the user can click to complete the purchase at the retailer. The agent should present this link to the user for confirmation. </tool_description> <when_to_use> ONLY after user has expressed clear purchase intent for a specific product. Requires a product UUID from nexbid_search or nexbid_product. ALWAYS confirm with user before calling this tool. </when_to_use> <combination_hints> nexbid_search (purchase intent) → nexbid_purchase → present checkout link to user. After purchase → nexbid_order_status to check if completed. Use checkout_mode=wallet_pay when the user has a connected wallet with active mandate. </combination_hints> <output_format> For prefill_link (default): Checkout URL that the user clicks to complete purchase at the retailer. For wallet_pay: Intent ID and status for mandate-based authorization. Include product name and price for user confirmation. </output_format>

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• remember

  Arcgis Peoria

  Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

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• remember

  Acled

  Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

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• remember

  Conceptnet

  Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

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• remember

  Dexscreener

  Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

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• remember

  Regrid

  Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

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• remember

  Harvard Art

  Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

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• improve_prompt

  agent4.io

  Rewrite a field's text to work better as an AI system prompt; returns the improved text. field ∈ persona | task | greeting | storyline_task | storyline_opening | storyline_ai_trigger | storyline_ai_criteria | skill_description | skill_instructions | kb_description | kb_instructions — determines the rewrite target (a Task should read like commands, a KB description says "what's inside / when it's relevant", a skill description says "when to use" in one line, …). current = the current text (empty = draft from scratch); hint = the direction you want this time (empty = general polish); context = on-site context (which KB is being edited, values of sibling fields) — the more specific, the better the fit. Returns only the improved text, no explanation, no wrapper — the caller places it next to the original for a human to accept or reject.

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• submitExperienceApplication

  Nomad Stays

  Submit a completed Experience Application for human review. Rejects with a missingFields list if any required field is still empty, or a 409 if the Application Fee hasn't been paid/waived yet (call purchaseProduct with productId 9 and applicationId first — Experience uses product 9, NOT product 8). There is no partial/optimistic submission. On success the application moves to human review. Requires NOMADSTAYS_MCP_AGENT_TOKEN.

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• tailor_resume_tool

  Workopia

  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.

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• initiate_device_auth

  hypathesis

  Start a browser-based sign-in flow to get an API key for full access. Call this when you need detailed analysis results (reasoning, measurements) that require authentication. Returns a verification URL to show to the user. After the user signs in, poll check_device_auth with the returned user_code to get the API key. Returns: { "verification_url": str, "user_code": str, "expires_in": 600, "message": str }

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• submit_content_to_pipeline

  freedom-mcp

  Submit manual content to a pipeline for transformation. Use when user says "add this to my changelog", "create a newsletter from this", "transform this content", or provides content to be processed. Content will be transformed using the pipeline's persona and ICPs. Social pipelines publish to the pipeline's declared destination (x/linkedin/instagram/facebook/threads — set via update_pipeline; undeclared defaults to x) after human approval; instagram items REQUIRE media_artifact_ids. [write-tier — first use may require a manager's approval; a from-now-on approval makes future calls seamless, a just-once approval re-asks next time.]

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• recommend

  sella

  Ranked related listings with per-item reasons. Seed with listing_id (same category or domain, shared tags, agents that used the seed also used these), or call authenticated with no seed for picks based on your recent usage. Not a keyword search: use search_catalog for that.

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• designesy_skill_md

  Designesy

  Get the Designesy SKILL.md — the agent-skill-format export of the design-system contract, written as behavioral rules an AI coding agent can drop into .agents/skills/ or a system prompt. Use this when you want the contract in a form that steers how an agent *builds* UI (tokens, anti-patterns, behavioral rules, verification). When NOT to use: for the raw contract JSON, use designesy_contract; for scoring, use designesy_score. Read-only — no side effects. Returns markdown text (SKILL.md format) — drop into .agents/skills/ or paste into a system prompt. No parameters.

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• ccs_info

  SqueezeOS — Institutional AI Market Intelligence

  Cognitive Credit Swarms discovery endpoint. Returns full system description, how-it-works, verdict definitions, pricing, all endpoint URLs, and MCP tool list. Written for AI agents to parse. Free — this is the doorbell. Use this first to understand the CCS system before calling ccs_validate.

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• get_session_dossiers

  VaultCrux Memory Core

  Retrieve pre-synthesized per-session memory dossiers (typed: experience | fact | preference; with When/Involving/To-purpose metadata). Use for multi-session or preference-style questions where stitching across conversations is the bottleneck — the dossier already summarises each session's key events. Two modes: mode='search' with a query (BM25-ish ranking over summary+purpose, optional type_filter), or mode='list' returns the tenant's most-recent dossiers chronologically. Tenants without FEATURE_SESSION_DOSSIERS enabled return an empty list (no error).

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• analyze_eeat

  mcp

  Analyze a website URL for E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness). Evaluates content quality signals based on Google's Search Quality Rater Guidelines and "Creating helpful content" documentation. Detects EEAT signals including: - Experience: First-person language, case studies, testimonials, years of experience - Expertise: Author credentials, certifications, professional memberships, topic depth - Authoritativeness: Organization schema, awards, trust badges, media mentions - Trustworthiness: HTTPS, contact info, privacy policy, source citations Also detects YMYL (Your Money or Your Life) content for health, financial, and legal topics. Results are cached for fast subsequent lookups. Rate limited to 1 request per minute per domain. Args: url: The website URL to analyze (e.g., "https://example.com"). Returns: EEAT analysis result with: - url: The analyzed URL - score: Overall EEAT score (0-100) - grade: Letter grade (A-F) - scores: Individual category scores (experience, expertise, authoritativeness, trustworthiness) - issues: Categorized issues (critical, warnings, info) - signals: Detected EEAT signals - meta: Extracted meta information - recommendations: Prioritized list of improvements - cached: Whether result was from cache

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