Start a new chat with a repo: creates a chat row, sends the first user message, streams + persists the assistant reply. Returns {chat_id, reply}. If the AI service does not answer this fails with a JSON-RPC error carrying {chat_id, ai_error} — it never returns apology copy as a reply. Requires authentication.
| Name | Required | Description | Default |
|---|---|---|---|
| repo | Yes | ||
| owner | Yes | ||
| title | No | Chat title (optional) | |
| message | Yes | Initial user message |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description reveals several non-obvious behaviors: it creates and persists a chat row, streams the reply, returns {chat_id, reply}, fails via JSON-RPC error with {chat_id, ai_error} instead of apology copy, and requires authentication. This adds substantial context beyond the annotations (readOnlyHint=false, destructiveHint=false), with no contradiction.
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 three tight sentences: purpose/action first, return value second, failure behavior and auth third. Every sentence earns its place, with no repetition or filler.
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?
Without an output schema, the description explains the return shape and error behavior. It covers authentication and persistence. Missing items are explicit definitions for owner/repo and any rate-limit or streaming specifics, but for a moderately complex chat-start tool this is largely complete.
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 50%, and the description only clarifies 'message' as the first user message. The required parameters 'owner' and 'repo' are not semantically explained beyond the tool name's reference to 'repo'; 'title' is covered in the schema. This partially compensates for the gap but leaves required parameters under-documented.
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 and resource: 'Start a new chat with a repo', then details the actions: creates a chat row, sends the first user message, streams and persists the reply. This clearly distinguishes the tool from siblings like gluecron_chat_continue (new vs. continue) and explain/agent-session tools.
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 context that this is for starting a new chat, implicitly separating it from continuing an existing chat (gluecron_chat_continue). It does not explicitly name alternatives or state 'when not to use', so it falls short of a 5, but the intended usage is unambiguous.
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.
