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The 44 tools shown in a GitHub MCP connector’s authorization view were not 44 tools available to the Apify Actor, and they did not mean its GitHub token could use all 44. In Ai-Q Labs’ September 18, 2026 case study, the server advertised 44 tools, but the Actor’s input schema declared four, and Apify’s proxy limited that run to those four. The credential, connector, and Actor schema each controlled a different part of the path.
Why 44 discovered tools did not mean 44 usable tools
The apparent mismatch comes from treating tool discovery as permission. An MCP server can advertise a broad inventory of tools; that inventory describes what the server offers, not what a particular Actor run is authorized or configured to call. In Ai-Q Labs’ reported setup, the connector authorization view listed 44 discovered tools, while the Actor run exposed only issue_read, issue_write, list_issues, and search_issues. Those were the four tools named in the Actor’s input schema. These are the author’s reported counts, not a fixed inventory for every connector or account. Ai-Q Labs’ case study attributes this behavior to Apify documentation: “The proxy enforces that an Actor can only call tools it explicitly declared in its input schema.”
What the credential, connector, and schema each control
| Layer | What it controls | What it does not establish |
|---|---|---|
| Upstream GitHub credential | The GitHub permissions the credential carries, and therefore what GitHub will honor. | It does not select which MCP server or tool set the Actor uses. |
| MCP connector | The MCP server associated with the credential and the tools that server advertises or makes available for discovery. | A listed tool is not, by itself, proof the credential can perform that action or the Actor can call it. |
| Actor input schema and Apify proxy | The small set of tools the Actor declares for a run; the case study says the proxy enforces that declaration before dispatch. | Declaring a tool does not expand the upstream GitHub credential’s permissions. |
In the author’s setup, the GitHub credential was a 30-day fine-grained personal access token scoped to one repository, with Issues read/write and Metadata read-only permissions. That is a description of this case study’s configuration, not a platform-wide requirement or current recommendation. A tool must pass through all relevant layers: the credential must allow the GitHub operation, the selected server must provide the tool, and the Actor must declare it for its run.
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How the Actor used the four tools
The workflow was designed to read before writing. The Actor listed open issues, checked stable finding keys against existing issues, and created issues only for findings not already represented. If reading the existing issues failed, it stopped before attempting writes. That ordering makes the decision depend on a successful read rather than risking duplicate issues after a lookup failure.
#1 Best Overall
Ai-Q Labs reports that an identical second run recognized three existing issues. Across four live runs and two audit datasets, the author reports 11 issues. The case study also describes 22 audit Actors and $0.61 in account usage against a $5 monthly credit at the time of the report. These are author-reported observations, not independent benchmarks or guarantees of future usage.
Preserve uncertainty in findings
A finding can contain both an established issue and a check that could not be completed. The case study describes a rate-limited lookup whose missing result was mistakenly treated as proof that a repository was absent. A missing result in that situation means the check is unknown, not that the repository is absent. Keep that uncertainty in the issue text instead of converting it into either a clean result or a false alarm.
Inspect tool replies and make writes recoverable
Do not assume a tool response has the fields your code expects. In the case study, issue creation returned an internal string id and a URL, but no issue-number field. Code that assumed an issue number then failed during the subsequent dataset write—even though GitHub had already received the issue. Capture and inspect initial responses, then map the fields actually returned into later bookkeeping.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchKeep external writes and local bookkeeping as separate stages in error handling. If a dataset update fails after GitHub has created an issue, report that partial outcome accurately; do not describe the whole run as having made no changes. On a retry, the read-before-write check can help the Actor recognize the issue that already exists.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Apify’s two MCP roles are different
Apify’s documentation distinguishes its Apify MCP server, which lets outside AI applications discover and run Actors, from MCP connectors used inside Actors to call third-party MCP servers. The GitHub connector in this example belongs to the latter pattern. The distinction matters when following setup instructions: a feature that exposes Actors to an external client is not the same thing as an Actor using a connector to call GitHub.
For a separate safeguard, GitHub’s official GitHub MCP server repository documents a read-only mode that prevents modifications to repositories, issues, pull requests, and related resources. That server-level mode addresses whether the server can make changes; it is distinct from both the GitHub credential’s permissions and the tools declared in an Actor schema. Connector screens, tool inventories, protocol behavior, and account terms can change, so check the current platform documentation for setup specifics.
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