Read a single file from a repository at a given ref (branch / tag / commit). Private repos are readable when the authenticated caller has access. Returns the text content (binary files rejected).
| Name | Required | Description | Default |
|---|---|---|---|
| ref | No | Branch / tag / commit. Defaults to the repository's actual default branch (which may not be 'main' — e.g. 'Main'). | |
| path | Yes | File path within the repo | |
| repo | Yes | Repo name | |
| owner | Yes | Repo owner username |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, destructiveHint), the description adds context that private repos require access and binary files are rejected. This helps the agent understand constraints and 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?
Two sentences that are front-loaded and efficient. The first sentence states the core purpose, the second adds necessary constraints. 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?
For a read-only file tool with no output schema, the description explains return type (text content), rejection of binary files, and private repo access. This is sufficient for an agent to understand the tool's capabilities.
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%. The description adds extra detail on the 'ref' parameter (defaults to actual default branch, which may not be 'main'). This enhances the schema's descriptions.
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 'read' and resource 'a single file from a repository at a given ref.' It mentions private repo access and binary file rejection, but does not differentiate from the sibling tool 'gluecron_read_file'.
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 provides no guidance on when to use this tool versus alternatives like gluecron_read_file. No exclusions or context for usage are given.
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.
