Send another message to an existing repo chat. Returns the assistant's reply. If the AI service does not answer this fails with a JSON-RPC error carrying {chat_id, ai_error} rather than returning apology copy as a reply.
| Name | Required | Description | Default |
|---|---|---|---|
| chat_id | Yes | ||
| message | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes well beyond the annotations by disclosing the failure behavior: if the AI service does not answer, it fails with a JSON-RPC error carrying {chat_id, ai_error} instead of returning apology copy. It also states that the tool returns the assistant's reply, which is not derivable from the readOnly or destructive flags.
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 tight sentences that front-load the core action, then add return-value and error-disclosure information. Every sentence adds value and there is no redundant 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?
For a two-parameter tool with no output schema, the description covers what it does, what it returns, and how it fails. It does not explain how to obtain chat_id or explicitly connect to the sibling gluecron_chat_with_repo, but 'existing repo chat' is enough context for invocation in most cases.
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 provides only names and types with zero description coverage, but the description compensates by characterizing chat_id as the identifier of an existing repo chat and message as the content of the follow-up message. Given only two simple string parameters, this is sufficient semantic guidance, though it could explicitly name the 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 opening phrase 'Send another message to an existing repo chat' names a specific verb, resource, and continuation scope, which clearly distinguishes it from starting a new chat such as gluecron_chat_with_repo. It also states the return value, the assistant's reply.
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?
'Another message to an existing repo chat' clearly implies this is the continuation tool, for use after a chat already exists, which separates it from sibling gluecron_chat_with_repo. It does not explicitly name the alternative or state a when-not-to-use condition, so it falls one step short of fully explicit routing.
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.
