Meta Ads MCP

by anhnguyen0905

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.2.0
Disambiguation5/5
Each tool targets a distinct resource (ad accounts, campaigns, ads, ad sets, insights, performance summary) with clear boundaries and explicit 'do not use' guidance, eliminating ambiguity.
Naming Consistency4/5
Most tools follow a 'get_<resource>' pattern (get_campaigns, get_ads, get_insights, get_adsets), with one using 'list_' (list_ad_accounts) and another using a noun phrase (performance_summary), which is a minor inconsistency.
Tool Count5/5
The 6 tools are well-scoped for a read-only Meta Ads analytics server, covering account discovery, hierarchical retrieval, insights, and a summary function without being excessive.
Completeness3/5
The surface is read-only and covers listing and insights, but lacks any create, update, or delete capabilities, and misses tools for single-object retrieval or ad account details beyond listing.
Average 4.5/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 21 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds valuable context about the return envelope structure and the fields returned, which goes beyond the annotations. No contradictions.
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 two concise paragraphs plus a usage note. It front-loads the core purpose and includes only essential information. No redundant or unnecessary sentences.
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 presence of an output schema and rich annotations, the description covers the tool's purpose, usage boundaries, and basic parameters. It explains the return envelope and fields. However, it does not mention pagination behavior beyond the limit parameter, which is a minor gap.
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?
The input schema includes descriptions for all three parameters (parent_id, limit, response_format), so the schema already covers parameter semantics. The description summarizes them briefly ('Parent object ID, maximum result count, and JSON or Markdown response format') but adds no additional meaning beyond what is in the schema. Baseline 3 is appropriate.
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 uses a specific verb ('get') and resource ('ads beneath an account, campaign, or ad-set parent'), clearly distinguishing it from sibling tools like get_campaigns and get_insights. It explicitly states the scope ('beneath a parent'), making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly states when to use the tool ('inspect ads for a known hierarchy parent') and when not to ('Do not use it for performance metrics or to create, edit, pause, or delete ads'). However, it does not explicitly name alternative tools for those uses, though siblings like get_insights are implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, making the safety profile clear. The description adds value by specifying the return envelope structure and included fields (identifiers, names, statuses, campaign IDs, daily budgets), and confirming it's for hierarchy inspection.
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?
Description is concisely written with key information front-loaded. It is a single paragraph that efficiently covers purpose, parameters, return format, and usage guidance without extraneous content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given an output schema exists, the description sufficiently explains return envelope and data fields. Combined with usage guidelines and parameter descriptions, it provides complete context for using the tool 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?
Description explains parameters at a high level ('parent account or campaign ID, maximum result count, and JSON or Markdown response format'), adding context about response format options. However, the input schema already provides detailed descriptions, default values, and constraints, so the description's incremental contribution is limited.
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?
Description clearly states the tool retrieves ad sets beneath an account or campaign parent. It uses specific verb 'get' and resource 'ad sets', distinguishing it from sibling tools like get_campaigns and get_ads.
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?
Description explicitly says 'Use this to inspect the ad-set hierarchy for a known parent' and 'Do not use it for performance metrics or to create, edit, pause, or delete ad sets.' This provides clear when-to-use and when-not-to-use guidance, implicitly directing to sibling tools for other purposes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so safety profile is clear. The description adds value by describing the return envelope structure and fields, but does not contradict annotations. A 4 reflects adequate behavioral context beyond annotations.
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?
Two concise paragraphs: the first covers purpose and parameters, the second explains use case and limitations. No redundancy, every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists, the description still usefully summarizes the return envelope and data fields. It covers response format options and default account behavior. Complete for a read-only listing tool.
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 descriptions already cover parameters with examples and defaults, so schema_description_coverage is high (0% context likely erroneous). The description only rephrases parameter types without adding new meaning. Baseline 3 is appropriate.
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 uses a specific verb-resource pair ('Get campaigns') and clarifies scope ('explicit or configured default ad account'). It lists return fields (identifiers, names, statuses, objectives, budgets), distinguishing it from siblings like get_ads or get_insights.
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 states when to use ('account-level campaign inventory') and when not to use ('Do not use it for performance metrics or for campaign creation, edits, or status changes'), implicitly steering to sibling tools like get_insights or other mutation tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, giving the agent confidence it's a safe read operation. The description adds context about the return envelope structure and that it lists accounts available to the configured token. It does not contradict annotations.
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 concise with three short sentences covering purpose, parameters, and return value. No unnecessary words or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity, annotations, and output schema, the description is complete. It explains the use case, input parameters, and return structure (envelope with specific fields), leaving no gaps for an agent to interpret.
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?
The description summarizes the params argument as 'Maximum result count and JSON or Markdown response format,' which adds clarity over the raw schema. However, the schema already includes descriptions for limit and response_format, so the description's contribution is marginal.
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 lists Meta ad accounts and specifies its role in discovering an account ID for subsequent reads. It uses a specific verb ('list') and resource ('ad accounts'), distinguishing it from sibling tools like get_campaigns.
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?
The description explicitly states when to use ('to discover an account ID before account-scoped reads') and when not to use ('do not use it to inspect campaigns or to create, update, or delete ad accounts'). This provides clear guidance and differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint. Description reinforces read-only use and adds details on returned fields (spend, delivery, click, etc.), without contradicting annotations. Adds moderate value beyond annotations.
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 concise with a clear structure: single-sentence summary, then Args, Returns, and usage guidance. No redundant sentences; every part adds value.
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?
Covers purpose, parameters, return format, and usage boundaries. The output schema exists and is described in the Returns section. Sibling tools are listed but not explicitly contrasted, though usage guidelines imply differentiation. Overall complete for a flexible read-only tool.
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?
Despite 0% schema description coverage at the top-level, the description lists all key parameters (Object ID, level, date preset/range, limit, format) and summarizes their roles. The nested schema provides individual descriptions, so the description adds a helpful high-level view.
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 'Get raw Meta performance metrics for an object and reporting period', specifying the verb, resource, and context. It also mentions the available levels (account, campaign, ad-set, ad), which helps distinguish it from sibling list tools like get_campaigns.
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 says 'Use this for flexible read-only metric retrieval' and warns 'Do not use it to modify delivery or to infer causal lift', providing clear when-to-use and when-not-to-use guidance that differentiates from siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds behavioral context: it is for triage, not causal analysis; results have caveats; campaign records include metric values and heuristic flags. No contradiction with annotations.
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 Args/Returns sections and usage guidance, but it is somewhat lengthy. A slightly more concise version could improve readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multiple parameters, heuristic flags, return envelope), the description is complete: it explains the return format, read-only nature, caveats, and provides guidance on when to use. No gaps are evident.
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?
The input schema has detailed descriptions for all sub-parameters, so schema_description_coverage is effectively 100%. The tool description merely lists the parameters without adding meaning beyond the schema, so baseline 3 is appropriate.
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's action: 'Rank campaign performance and flag heuristic symptoms for review.' It specifies the resource (campaigns) and differentiates from sibling tools like list/get operations, as this is a summary/analysis tool.
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?
The description provides explicit guidance: 'Use this for read-only campaign triage, not as proof of causation or a verified diagnosis.' It also warns against using for delivery changes or asserting causal impact, and explains limitations of platform-reported metrics.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![meta-ads-mcp MCP server](https://glama.ai/mcp/servers/anhnguyen0905/meta-ads-mcp/badges/card.svg)](https://glama.ai/mcp/servers/anhnguyen0905/meta-ads-mcp)

Score Badge

Copy to your README.md:

[![meta-ads-mcp MCP server](https://glama.ai/mcp/servers/anhnguyen0905/meta-ads-mcp/badges/score.svg)](https://glama.ai/mcp/servers/anhnguyen0905/meta-ads-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/anhnguyen0905/meta-ads-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server