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

• linux_auditA

  ZEN SecDBA

  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.

  ConnectorNo auth

• optimize_promptA

  pqs-mcp-serverA

  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.

  ConnectorNo auth

• optimize_promptA

  PQS - Prompt Quality ScoreA

  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.

  ConnectorNo auth

• update_traveler_profileA

  Gondola Award Travel SearchA

  Save a learned travel preference or experience to the user's traveler profile. Use when the user shares preferences, likes, dislikes, or trip experiences that should inform future recommendations. Examples: 'Prefers boutique hotels over chains', 'Always takes a window seat', 'Vegetarian — recommend plant-based restaurants', 'Hated ground floor rooms'. Don't save temporary logistics like 'my flight lands at 3pm'. Entries are appended, so saving one preference never overwrites another.

  ConnectorNo auth

• submitExperienceApplicationA

  Nomad StaysA

  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.

  ConnectorNo auth

• get_full_productA

  Moltline Creator StudioA

  Load one product in full: its persona plus every paid skill. PREMIUM (license). Typical input {"slug": "brand-voice"} returns {"slug": ..., "name": ..., "persona": "<persona text>", "skills": [{"name": ..., "instructions": ...}, ...], "free_skill": {...}}. Returns persona plus every skill for one product. Use when the caller wants the whole product. Not for a single skill (get_full_skill) and not for a free look, which list_products and get_free_skill provide with no plan. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "unknown slug '<value>'"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• get_auditor_personaA

  Moltline Agent Governance & MCP AuditorA

  Load the Governance Auditor persona for consistent fleet audits. PREMIUM (license). The persona is methodical, evidence-driven, and allergic to 'it's probably fine'. Takes no arguments. Returns {"persona": ..., "identity": ..., "rules": ["...", ...], "opening_move": "..."} ready to adopt as a system prompt. Use to keep repeated audits consistent in voice and rigor. Not for running an audit - the audit tools do that. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• get_full_productA

  Moltline Research DeskA

  Load one product in full: its persona plus every paid skill. PREMIUM (license). Typical input {"slug": "thesis-advisor"} returns {"slug": ..., "name": ..., "persona": "<persona text>", "skills": [{"name": ..., "instructions": ...}, ...], "free_skill": {...}}. Returns persona plus every skill for one product. Use when the caller wants the whole product. Not for a single skill (get_full_skill) and not for a free look, which list_products and get_free_skill provide with no plan. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "unknown slug '<value>'"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• get_full_productA

  Moltline Creator StudioA

  Load one product in full: its persona plus every paid skill. PREMIUM (license). Typical input {"slug": "brand-voice"} returns {"slug": ..., "name": ..., "persona": "<persona text>", "skills": [{"name": ..., "instructions": ...}, ...], "free_skill": {...}}. Returns persona plus every skill for one product. Use when the caller wants the whole product. Not for a single skill (get_full_skill) and not for a free look, which list_products and get_free_skill provide with no plan. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "unknown slug '<value>'"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• get_full_productA

  Moltline Research DeskA

  Load one product in full: its persona plus every paid skill. PREMIUM (license). Typical input {"slug": "thesis-advisor"} returns {"slug": ..., "name": ..., "persona": "<persona text>", "skills": [{"name": ..., "instructions": ...}, ...], "free_skill": {...}}. Returns persona plus every skill for one product. Use when the caller wants the whole product. Not for a single skill (get_full_skill) and not for a free look, which list_products and get_free_skill provide with no plan. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "unknown slug '<value>'"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• get_auditor_personaA

  Moltline Agent GovernanceA

  Load the Governance Auditor persona for consistent fleet audits. PREMIUM (license). The persona is methodical, evidence-driven, and allergic to 'it's probably fine'. Takes no arguments. Returns {"persona": ..., "identity": ..., "rules": ["...", ...], "opening_move": "..."} ready to adopt as a system prompt. Use to keep repeated audits consistent in voice and rigor. Not for running an audit - the audit tools do that. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• campaignstack_delete_personaA

  campaignstackA

  Permanently delete a persona; no restore tool exists. Call this only when the user explicitly asks to remove one, not merely edit it (campaignstack_update_persona instead). Use campaignstack_list_personas to confirm the ID first.

  Connector

  Destructive

  API key

• ethora-agents-create-v2A

  ethora-mcp-serverA

  Create a reusable AI agent (POST /v2/apps/:appId/agents). Works in user auth mode (the normal hosted mode) or B2B mode; app-token mode is not accepted by the backend. Each agent is a persona — name, avatar, system prompt, LLM config, plus response-gate settings (responseMode, cooldownSec) that control when it speaks in a room. For multi-agent scenarios (two or more personas conversing in one chat) create each one separately, then `ethora-agent-invite-to-chat` them into the same room. See the `ethora-agents-quickstart` prompt for the end-to-end recipe. Requires: a selected app (`ethora-app-select`) or an explicit `appId`; the agent is owned by that app.

  ConnectorNo auth

• list_open_jobsA

  Sable — an AI companion for AI agentsA

  Browse jobs currently open for AI agents to apply to. No token needed — this is a read-only, public listing, separate from the visit/reception system chat_with_companion uses (browsing and applying never talk to the companion's own model, so there's no visit budget, queue, or daily cap here). Call introduce_yourself first if you want to apply to one with apply_to_job.

  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

• departi_search_accommodationA

  Departi – Travel Compliance & Curated BookingA

  Searches bookable accommodation in a destination for given dates and returns a ranked list of stays, each with: name, property type, nightly price (in the requested currency), star rating, guest rating, key facilities (e.g. wifi, kitchen), a persona/traveler-type fit score, and a booking deep-link. Curated and scored for the traveller's persona and trip type — e.g. long-stay nomad apartments with kitchen + fast wifi vs weekend hotels. Use when the user needs a place to stay; for getting there use departi_search_transport, for things to do use departi_search_experiences. Returns an empty list if no inventory matches.

  ConnectorNo auth

• designesy_skill_mdA

  DesignesyA

  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.

  ConnectorNo auth

• get_full_productA

  Moltline Catalog — 138 Agent Skills & PersonasA

  Load one product in full: persona plus every skill's instructions. PREMIUM (paid plan). Typical input {"slug": "inbox-zero-assistant"} returns {"slug": ..., "name": ..., "persona": ..., "skills": [{"name": ..., "instructions": ...}], "free_skill": {...}}. Returns persona plus every skill for one product. Use when the caller wants the whole product. Not for a single skill (get_full_skill) and not for a bundle of products (get_full_kit). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "unknown slug '<value>'"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• get_full_productA

  Moltline Educator ToolkitA

  Load one product in full: its persona plus every paid skill. PREMIUM (license). Typical input {"slug": "curriculum-architect-hs"} returns {"slug": ..., "name": ..., "persona": "<persona text>", "skills": [{"name": ..., "instructions": ...}, ...], "free_skill": {...}}. Returns persona plus every skill for one product. Use when the caller wants the whole product. Not for a single skill (get_full_skill) and not for a free look, which list_products and get_free_skill provide with no plan. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "unknown slug '<value>'"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• get_full_productA

  Moltline Educator ToolkitA

  Load one product in full: its persona plus every paid skill. PREMIUM (license). Typical input {"slug": "curriculum-architect-hs"} returns {"slug": ..., "name": ..., "persona": "<persona text>", "skills": [{"name": ..., "instructions": ...}, ...], "free_skill": {...}}. Returns persona plus every skill for one product. Use when the caller wants the whole product. Not for a single skill (get_full_skill) and not for a free look, which list_products and get_free_skill provide with no plan. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "unknown slug '<value>'"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth