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Python
Hybrid

meta_ads_conversions_send

Send arbitrary conversion events to Meta's Conversions API for server-side tracking and pixel attribution. Use for custom event names or when dedicated purchase/lead helpers don't apply.

Instructions

Sends a batch of arbitrary conversion events to the Meta Conversions API. Returns Meta's response including events_received and messages (warnings for missing fields). Mutating on Meta's side — events become part of the pixel's attribution stream. For common event types prefer the dedicated meta_ads_conversions_send_purchase or send_lead helpers, which enforce required fields and fewer mistakes. For other event names (AddToCart, InitiateCheckout, CompleteRegistration, custom events) use this generic tool.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
eventsYesEvent payloads to send. Meta batches are typically ≤1000 events per call.
reasonNoWhy this change is being made: one or two sentences naming the evidence and the expected effect. Stored in the journal and on the action_log entry this call produces, for the operator and the next session.
pixel_idYesMeta Pixel ID the event is attributed to. Find via meta_ads_pixels_list. CAPI events flow into the same pixel as browser events; dedupe happens on event_id if one is supplied in user_data / custom_data.
account_idNoMeta Ads account ID in the format 'act_XXXXXXXXXX' (e.g. 'act_1234567890'). Optional — falls back to META_ADS_ACCOUNT_ID from the configured credentials. The leading 'act_' prefix is required.
test_event_codeNoMeta Events Manager test_event_code. When set, the event is routed to the test event stream visible in Events Manager instead of production reporting. Use for validation; drop the field once verified. Get the code from Events Manager → Test Events tab.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.20.0
    • addedInput schema / properties / reason
      Added value: +{
+  "description": "Why this change is being made: one or two sentences naming the evidence and the expected effect. Stored in the journal and on the action_log entry this call produces, for the operator and the next session.",
+  "maxLength": 500,
+  "type": "string"
+}
  2. Changed1 schema field changedv0.10.37
    • addedInput schema / additionalProperties
      Added value: +false
  3. Addedv0.9.12
  4. Removedv0.9.6
  5. Addedv0.9.2
  6. Removedv0.9.1
  7. Addedv1.0.5

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the mutating nature: 'Mutating on Meta's side — events become part of the pixel's attribution stream,' and mentions the response format (`events_received` and `messages`). It does not cover all potential side effects (e.g., deduplication details, permission requirements) but gives a solid baseline of behavioral transparency.

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

Conciseness5/5

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

The description is five sentences with zero fluff. It front-loads the core purpose, then the response, then the mutation warning, then usage guidance. Every sentence earns its place, and the structure leads with the most decision-relevant information.

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?

For a tool with 5 parameters (2 required) and an array-based payload, the description covers the essential context: what the tool does, its mutating nature, the response shape, and when to use it. It does not detail error conditions beyond mentioning 'messages (warnings for missing fields),' but combined with the exhaustive schema descriptions, nothing critical is missing for an agent to invoke it correctly.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents every parameter thoroughly. The description adds no parameter-specific semantics beyond what the schema provides; it only contextualizes the tool's purpose. Per the rubric, baseline 3 is appropriate when the schema does the heavy lifting.

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 opens with a specific verb and resource: 'Sends a batch of arbitrary conversion events to the Meta Conversions API.' It clearly distinguishes itself from the dedicated helpers by stating it handles arbitrary event types, and names the exact sibling tools (meta_ads_conversions_send_purchase, send_lead) it is not.

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?

Explicitly instructs when to use this tool vs alternatives: 'For common event types prefer the dedicated... helpers... For other event names... use this generic tool.' This is direct when/when-not guidance that leaves no ambiguity for an agent.

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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