Fetch a single commit by SHA: metadata plus the list of files it changed (path, status, additions, deletions). Mirrors GET /api/v2/repos/.../commits/:sha. Patch bodies are NOT included — use gluecron_get_diff for those.
| Name | Required | Description | Default |
|---|---|---|---|
| sha | Yes | Commit SHA | |
| repo | Yes | ||
| owner | Yes | ||
| include_files | No | Include the files-changed summary and stats (default true). Set false to skip the diff read on very large commits. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Even with readOnlyHint=true and destructiveHint=false already declared, the description adds meaningful behavioral context: it details the response composition, states that patch bodies are deliberately omitted, and mirrors a specific REST endpoint. This goes beyond the annotations and helps the agent set correct expectations.
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 only two sentences, front-loads the core action, and packs in the endpoint reference, return-value summary, and a clear caveat with an alternative. Every sentence earns its place with no 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 read-only, single-commit fetch with no output schema, the description covers what the response contains, what it omits, and how to get the omitted content. It is sufficient for an agent to call the tool correctly and interpret the result.
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 50%: sha and include_files are described in the schema, while owner and repo are not. The tool description indirectly clarifies owner/repo via the endpoint pattern and clarifies the meaning of the file list, but it does not compensate fully for the undocumented parameters.
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 starts with a specific verb and resource: 'Fetch a single commit by SHA' and lists exactly what is returned (metadata plus file changes with path, status, additions, deletions). It also explicitly contrasts with gluecron_get_diff by stating patch bodies are not included, so an agent can distinguish the two tools immediately.
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 gives an explicit when-not-to-use instruction: 'Patch bodies are NOT included — use gluecron_get_diff for those.' This directly names the alternative and the condition that selects it, which is exactly what a usage guideline should provide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools have near-identical purposes, such as `gluecron_read_file` and `gluecron_repo_read_file` (both read a file from a repo), and `gluecron_explain_repo` and `gluecron_repo_explain_codebase` (both return cached AI explanation). This creates ambiguity despite minor differences in description. While many tools are distinct, the overlapping pairs force an agent to choose between effectively equivalent operations, lowering disambiguation.
All tools use the `gluecron_` prefix followed by a verb_noun pattern (e.g., `acquire_lease`, `create_issue`, `merge_pr`). A few tools like `gluecron_ai_cost_summary` and `gluecron_repo_explain_codebase` deviate slightly but remain readable and predictable. Overall, the naming convention is largely consistent, making it easy to infer tool function from the name.
With 60 tools, the server far exceeds the 25-tool threshold for 'too many' per the guidelines. Although the server covers a broad developer platform (repository management, issues, PRs, workflows, AI features, etc.), the sheer number of tools makes navigation heavy and risks overwhelming both agents and users. A more focused set would improve coherence.
The tool set is remarkably thorough, covering nearly every lifecycle stage for repositories, issues, pull requests, workflows, branches, commits, and AI-assisted features (chat, test generation, release notes, refactoring, voice-to-PR). Essential CRUD operations are present, and advanced operations like leasing, sandbox provisioning, and multi-repo refactoring are included. There are no obvious gaps for the stated purpose of a developer platform.
