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Cloud Signal Connectors Tool

signal_connectors

Bind external systems (ticketing, alerts, Slack, monitors) to FleetQ so events become signals that trigger rules and agents. Query and extend the knowledge graph for agent reasoning.

Instructions

Inbound signal SOURCES (ticketing, alerts, Slack, HTTP monitors, ClearCue, Telegram bots) plus the team's knowledge graph (KG) read/write surface. Distinct from signal_manage: this tool wires up connector configurations; signal_manage operates on already-ingested signals.

When to use: bind a new external system to FleetQ so its events become signals (which trigger rules → projects → agents), or query/extend the team's knowledge graph that agents reason over. Do NOT use to send outbound messages — use outbound_manage for that.

Connector actions (each accepts a config object specific to that channel; idempotent — re-call to update):

  • ticket (write — upsert) — config (Jira/Linear/Zendesk fields). Polls every 5 min.

  • alert (write — upsert) — config (PagerDuty/Opsgenie webhook). Pushes signals on incident events.

  • slack (write — upsert) — config (workspace_id, channel_ids[], event filters).

  • http_monitor (write — upsert) — config: url, interval_seconds (min 60), expected_status. Emits a signal when status flips.

  • clearcue (write — upsert) — config (ClearCue API key, query).

  • inbound_connector (write) — sub-actions: create, update, delete on generic connectors.

  • subscription (write) — sub-actions: list, create, delete on per-connector subscriptions.

  • telegram (write — upsert) — config: bot_token, allowed_chat_ids[]. Webhook auto-registered.

Knowledge graph actions:

  • kg_search (read — hybrid semantic+symbolic) — query (string), optional types (array). Returns ranked entities with relevance score.

  • kg_facts (read) — entity_id (UUID). All facts attached, paginated.

  • kg_add_fact (write) — entity_id, fact: { predicate (string), object (string), source (string), confidence (0..1) }.

  • intent_score (read — costs ~1-2 LLM credits) — company (string), query (string). Returns intent classification + confidence.

Cloud note: supabase connector not exposed in cloud (TeamProviderCredential handles per-team Supabase access through integration_manage). Side effects: writes update KG and trigger downstream rule evaluation (may dispatch agent runs and consume budget). Errors: 401, 403, 404, 422 (config validation), 429, 503 (upstream connector down).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYesAction to perform: ticket, alert, slack, http_monitor, clearcue, intent_score, kg_search, kg_facts, kg_add_fact, inbound_connector, subscription, telegram
deadline_msNoOptional: max wall-clock time (ms) the tool may spend. If exceeded during the call, returns a DEADLINE_EXCEEDED error. Minimum 100 ms. Leave unset for no deadline.
driverNoConnector driver for setup instructions: github | github_issues | jira | linear
urlNoURL to monitor (required for add)
monitor_typeNoMonitor type: availability | content_change | both (default: availability)availability
nameNoHuman-readable name for the monitor (optional, defaults to hostname)
connector_idNoConnector UUID (required for remove)
expected_statusNoExpected HTTP status codes (default: [200])
ssl_checkNoAlert when SSL certificate expires within 14 days (default: true)
entity_keyNoStable identifier for the entity: LinkedIn URL, company domain, or website URL. Required for get_score and get_signal_history.
entity_typeNoEntity type: company | person
thresholdNoMinimum threshold for list_hot_leads: hot (80+) | warm (50+) | lukewarm (20+)
limitNoMaximum number of results for list_hot_leads (default 20, max 100)
queryYesNatural language search query, e.g. "CEO of Acme Corp" or "latest price of Competitor X"
relation_typeNoFilter by relation type (snake_case), e.g. works_at, has_price, has_status, acquired_by
include_historyNoInclude invalidated historical facts (default: false — only current facts)
entity_nameYesName of the entity to look up, e.g. "Alice Chen" or "Acme Corp"
source_entityYesName of the source entity, e.g. "Alice Chen" or "Acme Corp"
source_typeYesType of the source entity: person | company | organization | location | date | product | technology | event | concept | process | topic
target_entityYesName of the target entity, e.g. "Beta Corp" or "$79/month"
target_typeYesType of the target entity: person | company | organization | location | date | product | technology | event | concept | process | topic
factYesHuman-readable fact statement, e.g. "Alice Chen is VP Engineering at Beta Corp"
valid_atNoISO 8601 datetime when this fact became true (defaults to now if omitted)
subscription_idNoSubscription UUID (required for get/toggle/delete)
integration_idNoIntegration UUID to bind this subscription to (required for create)
filter_configNoDriver-specific filter config. GitHub: {repo, filter_branches, event_types}. Linear: {team_id, resource_types, filter_actions}. Jira: {project_key, webhook_events}.
bot_tokenNoBot token from BotFather (required for register)
routing_modeNoRouting mode: assistant | project | trigger_rules (default: assistant)assistant
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description carries the full burden. It discloses side effects ('writes update KG and trigger downstream rule evaluation'), error codes (401, 403, 404, 422, 429, 503), idempotency for connector actions, polling intervals (5 min), and cloud-specific restrictions. While not exhaustive (e.g., no mention of retry behavior), it is quite thorough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (purpose, when to use, action list, cloud note, side effects, errors). It is front-loaded with essential information. While it is lengthy (due to many actions), it is organized and every sentence adds value, making it efficient rather than verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (28 parameters, no output schema, nested objects), the description covers all major aspects: actions with config details, knowledge graph operations, side effects, error codes, and cloud-specific note. It does not describe return values, but since no output schema exists, this is acceptable. The description provides sufficient context for an agent to understand the tool's capabilities and boundaries.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, but the description adds significant value by grouping actions and explaining the behavior of each connector action (e.g., 'Polls every 5 min' for ticket, 'Pushes signals on incident events' for alert). It provides context for config objects and sub-actions (e.g., subscription: 'list, create, delete'). This goes beyond the schema's field-level descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool handles inbound signal sources and knowledge graph read/write, with a specific verb ('bind' and 'query/extend'). It distinguishes from sibling `signal_manage` by explaining that this tool wires up connector configurations while the sibling operates on already-ingested signals, providing clear differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit guidelines: 'When to use: bind a new external system to FleetQ so its events become signals...or query/extend the team's knowledge graph.' Also explicitly states when NOT to use: 'Do NOT use to send outbound messages — use `outbound_manage` for that.' This provides clear context and alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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