Query the per-repo vector index (Voyage embeddings when configured, hash fallback otherwise). Reads the live per-push index (code_embeddings) first, falling back to the manually-reindexed chunk index (code_chunks). Returns {hits, source}.
| Name | Required | Description | Default |
|---|---|---|---|
| repo | Yes | ||
| limit | No | ||
| owner | Yes | ||
| query | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly and non-destructive behavior, and the description adds substantial detail: it reads the live per-push code_embeddings index first, falls back to code_chunks, uses Voyage embeddings when configured with a hash fallback, and returns {hits, source}. This is rich behavioral disclosure beyond the annotations.
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 the main action front-loaded. Every clause adds value: the backend variant, the fallback order, and the return shape, with no wasted 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?
Behavior and return shape are well covered, and annotations handle safety. However, the tool has four parameters with zero schema-level descriptions, and the description does not clarify parameter semantics or when to choose this over sibling search tools, so an agent still has to infer non-trivial call details.
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 0%, so the description needed to compensate, but it does not explain owner, repo, query, or limit semantics. The phrase 'Query the per-repo vector index' lets an agent infer query and repo, but limit and owner are left entirely to the schema's bare names and types.
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 states a specific verb ('Query') and a specific resource ('per-repo vector index'), making the tool's function immediately clear. It also mentions the return shape, which reinforces the purpose. The vector-index emphasis differentiates it from siblings like gluecron_repo_search and gluecron_find_symbol.
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 clear operational context about which index is read and in what order, so an agent can infer when the tool is relevant. However, it does not explicitly say when to prefer this over alternatives such as gluecron_repo_search or gluecron_find_symbol, nor does it state any exclusions.
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.
