Full-text search of public repositories by name/description, case-insensitively. Mirrors GET /api/v2/search/repos. total is the full match count (not the page size) and nextOffset pages through it.
| Name | Required | Description | Default |
|---|---|---|---|
| sort | No | stars | updated | name (default: stars) | |
| limit | No | Max rows per page (default 30, max 100) | |
| query | Yes | Search keyword | |
| offset | No | Rows to skip. Use `nextOffset` from the previous response. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral details beyond the schema: case-insensitive matching, mirroring a specific endpoint, the meaning of total as the full match count, and how nextOffset controls pagination.
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 two sentences with no filler. The core operation is front-loaded, and the pagination semantics are concise but complete.
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 search tool with well-documented parameters and annotations, the description is largely sufficient. It explains the key return-field semantics (total and nextOffset) even though there is no output schema, but it could go slightly further in describing the shape of the returned repository items.
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?
The schema has 100% description coverage for all four parameters, so the description does not need to re-document them. It does add some helpful context by explaining that total is the full count and nextOffset pages through results, but this is more about response pagination than the parameter definitions themselves.
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 identifies the tool as a full-text search over public repositories by name or description, case-insensitively, and it mirrors a specific API endpoint. This is a precise verb+resource+scope statement, though it does not explicitly contrast itself with the sibling gluecron_repo_search tool.
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 useful context, such as searching public repositories by name/description rather than code or issues, so the intended use is implied. However, it does not explicitly say when to prefer this tool over nearby alternatives like gluecron_repo_search, gluecron_semantic_search, or gluecron_find_symbol.
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.
