Ezoic Setup Assistant
Server Details
Ezoic ads implementation assistant: integration docs, SDKs, and working, copyable setup code.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4/5 across 4 of 4 tools scored. Lowest: 3.2/5.
Each tool has a clear and distinct purpose: ask handles complex queries, help provides orientation, read_response and request_status manage asynchronous job results. No overlap or ambiguity.
All tool names follow a consistent pattern of lowercase verbs with underscores where multi-word (read_response, request_status). The naming is predictable and intuitive.
With 4 tools, the set is appropriately scoped for a setup assistant. It covers the main interaction (ask) plus necessary support functions (help, status, streaming), avoiding bloat.
The tool set covers all essential operations for an AI assistant: querying (ask), obtaining help (help), and managing asynchronous responses (request_status, read_response). No obvious gaps.
Available Tools
4 toolsaskAInspect
Ask the agent a question. Routes through the full agent pipeline: reads schema docs, selects and runs queries, analyzes results, and returns a complete answer. Synchronous when the agent finishes within deadline; returns {status:'pending', job_id:...} otherwise so the caller can poll request_status / read_response. Requires a thread id — generate one (e.g. UUID) at the start of a chat session and reuse it for every subsequent call so the agent keeps conversation context across questions.
| Name | Required | Description | Default |
|---|---|---|---|
| role | No | Optional role override; defaults to admin for MCP callers. | |
| agent | No | Bundle name. Inferred from the Host header when missing. | |
| thread | No | Persistent conversation id. Either `thread` or the legacy `thread_id` alias must be set. | |
| message | No | User prompt for the agent. Either `message` or the legacy `question` alias must be set. | |
| question | No | Legacy apples-compat alias for `message`. Ignored when `message` is set. | |
| thread_id | No | Legacy apples-compat alias for `thread`. Ignored when `thread` is set. | |
| deadline_seconds | No | Synchronous wait budget. Default 240. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds rich behavioral context beyond annotations: explains the full pipeline, synchronous vs asynchronous execution, return types, and thread requirement. No annotation contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Four concise sentences front-loaded with purpose, each adding necessary information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Comprehensive: explains return values (complete answer or pending status), async behavior, thread management, and prerequisites, sufficient given no output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (baseline 3). Description adds significant value by explaining thread id lifecycle and deadline_seconds as sync budget, but does not detail every parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool asks a question to the agent and routes through the full pipeline, distinguishing it from sibling tools 'read_response' and 'request_status' which only handle async results.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly describes when to use (ask questions, get answers), mentions asynchronous behavior and the need to poll with siblings, and advises to reuse thread id for conversation context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
helpBRead-onlyIdempotentInspect
Short orientation text for apples MCP callers.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds no additional behavioral traits beyond 'short orientation text,' which is already implied by the title 'Usage help.' It does not disclose any further side effects, required permissions, or return behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence that immediately conveys the tool's purpose. It is front-loaded and contains no unnecessary words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no parameters and no output schema, the description is adequate but could be more informative about what the 'orientation text' covers (e.g., list of tools or general usage). Given the simplicity, it meets the minimum requirement but lacks depth.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters in the input schema, and schema coverage is 100% (empty). The description does not need to elaborate on parameter semantics, and it adds no irrelevant information. Baseline 4 is appropriate as the schema fully covers the parameter aspect.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it provides 'short orientation text' for 'apples MCP callers,' indicating it is a help tool. It differentiates from sibling tools like ask, read_response, and request_status, which have distinct purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly state when to use this tool versus alternatives. It merely describes its function without offering usage context, prerequisites, or exclusions. Sibling tools are listed but not differentiated in terms of usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
read_responseARead-onlyIdempotentInspect
Stream a chunk of a pending ask() job's output.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | Job id returned by a prior ask() call. | |
| offset | No | Byte offset to start reading from. 0 for the beginning. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds context about streaming a chunk of pending output but does not contradict annotations. It could mention what happens if the job is not pending or offset is invalid.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that directly conveys the tool's function with no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lacks information about the output format, chunk size, or behavior when the job is done or output is unavailable. Since no output schema is provided, more context would be helpful for a complete understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 both parameters. The description does not add significant meaning beyond 'stream a chunk', which is implied by the offset parameter. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'stream a chunk' and the resource 'pending ask() job's output', distinguishing it from siblings like 'ask' (create job) and 'request_status' (check status).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions 'pending ask() job's output', which implies usage after an ask() call and while the job is pending. However, it does not explicitly state when not to use it or suggest alternatives like checking status first.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
request_statusARead-onlyIdempotentInspect
Check the status of a pending ask() job.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | Job id returned by a prior ask() call that went async. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, destructiveHint. The description adds 'pending' context but does not disclose return format, error handling, or behavior for non-pending jobs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence of 8 words with no wasted content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, but the description does not specify what the return value contains (e.g., status string). This is a significant omission for a status-check tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and already describes job_id. The description adds no additional parameter meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Check' and the resource 'status of a pending ask() job', distinguishing it from siblings like 'ask' (create job) and 'read_response' (read results).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use after an async ask call, but does not explicitly state when not to use it (e.g., after job completion) or mention alternatives like 'read_response' for results.
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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