"Interaction or Feedback Enhancement to Increase Frequency/Attempts" matching MCP tools:

• string_interaction_evidence

  STRING Database MCP Server

  Retrieves direct links to STRING evidence pages for protein–protein interaction pairs. Use this tool only when a STRING evidence page/link is needed. To determine whether an interaction is supported, use `string_interactions_query_set`. It returns URLs linking to STRING’s evidence pages, which display the underlying data sources (experimental results, publications, and curated databases) supporting each predicted interaction. A URL can be generated even for unsupported pairs; the URL is not itself an interaction verdict. Parameters: - **identifier_a**: Query protein identifier (Protein A) - **identifiers_b**: One or more target protein identifiers (Protein B), separated by `%0d` - **species**: NCBI taxonomy ID (e.g. `9606` for human or `10090` for mouse) Typical user questions that should trigger this tool: - "Can you show me the STRING evidence for this interaction?" - "Show me the details supporting this interaction." - "What supports the interaction between TP53 and MDM2?" - "Where can I find the STRING evidence for this pair?"

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• ghostroute_ct_proofs

  TunnelMind Data API

  Returns GhostRoute's per-cert inclusion proofs: each is a cryptographic demonstration that the exact certificate a host serves is included in an append-only CT log whose root TunnelMind signature-verified — upgrading "a monitor said this cert exists" to "proven in a log we witness". Failed attempts are included with a `reason`; a cert that suddenly cannot be proven is itself a signal. Use this tool when: - You want to know whether a specific AI host's live cert is provably logged (pass `domain`), or - You want the corpus-wide proof rollup across watched hosts (omit `domain`). Inputs: - `domain` (query, optional): a hostname to filter to; omit for corpus-wide. - `limit` (query, optional): max recent rows, 1–200, default 50. Returns: - `domain` (echo, null when corpus-wide). - `summary`: `total_attempts`, `proven`, `unproven`, `domains`, `last_observed_at`. - `recent[]`: recent attempts (`log_operator`, `leaf_index`, `tree_size`, `sth_root_hash`, `inclusion_proven`, `reason`, ...). - `by_domain[]`: per-host `attempts` / `proven` rollup. Latency: - Typical <300ms (KV-cached 5m).

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• submit_feedback

  agentView

  Sends the user's product feedback about agentView to an internal review queue. Use this ONLY when the user explicitly wants to share feedback, a feature request, a complaint, or praise about agentView itself (not about the content shown on a display). Always confirm the wording with the user before sending; never invent or embellish feedback on their behalf. Requires authentication with at least content_only scope. The feedback is stored for later review; no automatic reply is sent and this does not open a support ticket. Returns the new feedback id and a status of 'received'.

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• report_issue_create

  DC Member API

  POST /report-issue — Report an issue or feedback Submit a bug report, feedback, or question to the DC team. Optionally include a base64-encoded screenshot (PNG, JPEG, or WebP, up to 4 MB raw). **Privacy note:** Screenshots and report text are sent unredacted to the DC team. Don't include passwords, payment details, or other secrets. ⚠️ WRITE operation: this mutates your DC account data.

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• shippingrates_transit_schedules

  freightgate-mcp-server

  Get detailed transit schedules for a specific carrier — service codes, routing via transhipment ports, transit days, and sailing frequency. Use this when you need routing details beyond just transit time — e.g., which transhipment ports are used, what service string applies, or weekly frequency. For a quick transit time comparison across all carriers, use shippingrates_transit instead. PAID: $0.03/call via x402 (USDC on Base or Solana). Without payment, returns 402 with payment instructions. Returns: Array of { carrier, service_code, origin, destination, transit_days, transhipment_ports[], frequency, direct }.

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• job_alert_subscribe

  FoundRole — AI Job Search & Application Tracker MCP for Claude & ChatGPT

  Subscribes the authenticated user to job alerts for a specific saved job search. **Input:** - `job_search_id`: The job search identifier to subscribe to (required). Accepts either the job search UUID or the composite job ID returned by `jobs_search` / `jobs_details` (format: "seo_id--job_search_id"). - `frequency`: Alert frequency — one of daily, weekly, monthly (optional, defaults to "weekly") **Output:** Returns the created or updated job alert with id, status, and frequency. Idempotent: calling this tool for an already-subscribed search updates the existing alert without creating a duplicate.

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• submit_feedback

  InsideOut (Riley)

  FEEDBACK: Submit feedback, bug reports, or feature requests to Luther Systems Use this tool to forward user feedback directly to the Luther Systems team. This includes bug reports, feature requests, questions, or general feedback about InsideOut. The agent itself can also use this tool to report issues it encounters during operation. REQUIRES: session_id, category, message OPTIONAL: user_email (for follow-up), user_name, source (default: 'mcp'), initiator ('user' or 'agent') Categories: bug_report, feature_request, general_feedback, question, security The 'initiator' field tracks who triggered the report: - 'user' — the user explicitly reported the issue or requested feedback submission - 'agent' — Riley detected an issue and initiated the feedback flow Examples: - User says 'the deploy button is broken' → submit_feedback(category='bug_report', message='...', initiator='user') - User says 'I wish it had dark mode' → submit_feedback(category='feature_request', message='...', initiator='user') - Deployment failed with Terraform error → submit_feedback(category='bug_report', message='Deployment failed: Terraform apply error on aws_alb resource — timeout waiting for ALB provisioning', initiator='agent')

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• list_components

  blueprint-modular

  Liste les composants du design system Blueprint Modular (nom + description en une ligne). À UTILISER pour parcourir le catalogue ou découvrir ce qui existe dans une catégorie donnée. Résultat paginé par curseur (réutiliser 'nextCursor' pour la page suivante). Catégories : Affichage de données, Feedback, Graphiques, IA & Spécialisés, Identification & traçabilité, Interaction, Mise en page, Média, Navigation, Utilitaires.

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• submit_agent_feedback

  Zoning Signal

  Submit feedback to the observatory's operators about the MCP tool surface. The active counterpart to the passive invocation log. Categories: 'gap' (a capability you expected and didn't find), 'error' (an unexpected failure or wrong result), 'praise' (a tool or surface that did exactly what you needed), 'suggestion' (a refinement you'd recommend), 'citation_request' (a claim or fact you want surfaced with a stable @id you can cite). The submission auto-attaches the prior 10 invocations from your MCP-Session-Id, so operators read your feedback annotated with the call sequence that produced it — no need to repeat what you tried. Operators triage every submission and surface notable feedback at /agent-observatory. This is how the observatory evolves toward what agents actually need.

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• shippingrates_transit_schedules

  FreightGate MCP Server

  Get detailed transit schedules for a specific carrier — service codes, routing via transhipment ports, transit days, and sailing frequency. Use this when you need routing details beyond just transit time — e.g., which transhipment ports are used, what service string applies, or weekly frequency. For a quick transit time comparison across all carriers, use shippingrates_transit instead. PAID: $0.03/call via x402 (USDC on Base or Solana). Without payment, returns 402 with payment instructions. Returns: Array of { carrier, service_code, origin, destination, transit_days, transhipment_ports[], frequency, direct }.

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• get_next_cued_message

  Uphealth Signal

  Submit the patient's response to the prior cue and get the next one — federally-sourced, audience-safety-checked, and re-sequenced on that feedback. REFUSES WITHOUT PRIOR-MESSAGE FEEDBACK (the moat): the engine will not advance a stream blind, returning 409 if you skip it. response_action is one of the accepted cue vocabulary (see the signal://catalog resource). Same idempotency_key + same feedback returns the cached cue; conflicting feedback under the same key returns 409.

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• prompt_shield

  Zambo AI Tools

  Zambo Stack — Detect prompt injection, jailbreaks, and policy bypass attempts before they reach your AI model. Two-phase analysis: instant pattern library scan (12 attack vectors) + Groq semantic analysis. Returns injection_risk 0–100, recommendation safe/review/block, and a safe rewritten version when possible. 50 free/day. Best for: 'validate user input before LLM call', 'detect jailbreak attempts', 'is this prompt safe to send to GPT?'.

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• submit_feedback

  delegation-school

  Send the course team feedback FOR the learner — what's confusing, a bug, an idea, or praise. No grade, no admin key. Use it the moment a learner is stuck or reacts to something, AND always offer it when a session wraps ('anything confusing, or that you wish it did?'). Routes to the team's Slack + the feedback log so friction becomes a signal instead of a 1:1 text.

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• pubchem_get_compound_interactions

  pubchem-mcp-server

  Get a compound's interaction data: drug-drug interactions (DrugBank), drug-food interactions, and chemical-target interactions (binding/activity from BindingDB, ChEMBL, and others). Each entry carries its originating source. Richest for approved drugs; many compounds have no deposited interaction records.

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• reject_message

  LinkedIn MCP Server (Salesbot)

  Reject the current drafts on a campaign_contact with textual feedback (e.g. 'too formal, shorten to 2 sentences'). Resets generation_status to 'pending' so a new version is generated based on your feedback, which again waits for approval.

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• shippingrates_transit_schedules

  ShippingRates MCP Server

  Get detailed transit schedules for a specific carrier — service codes, routing via transhipment ports, transit days, and sailing frequency. Use this when you need routing details beyond just transit time — e.g., which transhipment ports are used, what service string applies, or weekly frequency. For a quick transit time comparison across all carriers, use shippingrates_transit instead. PAID: $0.03/call via x402 (USDC on Base or Solana). Without payment, returns 402 with payment instructions. Returns: Array of { carrier, service_code, origin, destination, transit_days, transhipment_ports[], frequency, direct }.

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• roamzy_status

  mcp-server

  Check Roamzy API status, including agent pause flags. Agents MUST call this before purchase attempts and back off if `purchases_paused=true`.

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• collect_feedback

  mcp-server

  Collects user feedback on the provided response. **When to use this tool:** - After providing an analysis, a SQL query, or an important response - When you want to know if the response was helpful - Naturally suggest: "Was this response helpful? 👍 👎" **Ratings:** - 'positive': The response was helpful and accurate - 'negative': The response was not satisfactory - 'neutral': Neither satisfied nor dissatisfied **Categories (optional):** - 'accuracy': Was the response accurate? - 'relevance': Did the response address the question? - 'completeness': Was the response complete? - 'speed': Was the response time acceptable? - 'other': Other feedback **Feedback usage:** Feedback is used to improve future responses (RAG, analytics).

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• get_next_cued_message

  Uphealth Signal

  Submit the patient's response to the prior cue and get the next one — federally-sourced, audience-safety-checked, and re-sequenced on that feedback. REFUSES WITHOUT PRIOR-MESSAGE FEEDBACK (the moat): the engine will not advance a stream blind, returning 409 if you skip it. response_action is one of the accepted cue vocabulary (see the signal://catalog resource). Same idempotency_key + same feedback returns the cached cue; conflicting feedback under the same key returns 409.

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• palette_iterate

  Colour Memory

  Refine an existing palette using natural language feedback. Submit your current palette and feedback such as more melancholic, too corporate add warmth, or better for Gen Z luxury. Returns a refined palette with archive grounding and change rationale.

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