pre_publish_impactCheck what publishing or promoting this version would do to dependent projects: how many are affected, at which severity, and whether the impact has to be acknowledged. Run this before promote_version when the version carries breaking changes. Requires project context.
| Name | Required | Description | Default |
|---|---|---|---|
| specId | Yes | Public ID (GUID) of the API specification | |
| versionId | Yes | Public ID (GUID) of the version to analyse |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already indicates no side effects, and the description reinforces this by saying 'check what ... would do' and 'preview impact.' It clearly frames the tool as non-destructive, though it does not detail any additional behavioral nuances beyond requiring context.
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, front-loaded with the primary purpose, and contains no redundant or vague phrasing. It is concise while still conveying essential usage context.
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 simple read-only preview tool, the description is complete: it explains what is checked, the output concerns, when to run it, and a required precondition. Since no output schema is provided, the conceptual description of results is sufficient.
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 already provides full descriptions for both parameters, specId and versionId, as GUIDs. The description adds no additional parameter-level semantics, so the baseline score applies.
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 tool checks what publishing or promoting a version would do to dependent projects, including affected count, severity, and acknowledgment requirements. It is distinct from sibling tools like get_breaking_changes or compare_versions by focusing on pre-publish impact analysis.
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?
It explicitly instructs to run this tool before promote_version when the version carries breaking changes, and notes that project context is required. This gives clear, actionable guidance on when and under what conditions the tool should be used.
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.
The tools are mostly distinct with clear descriptions. Some pairs like get_header_policies vs get_resolved_headers or get_environment_verification vs get_monitoring_sync_status could be slightly confusing, but the descriptions clarify scope and purpose.
All tools follow a consistent snake_case verb_noun pattern (get_, list_, create_, update_, manage_, etc.). Even the few bare verbs like 'search' and 'set_context' are consistent with the naming scheme.
With 165 tools, the server is extremely heavy. This far exceeds the 'too many' threshold of 25+, making it difficult for an agent to navigate and select the right tool efficiently.
The tool surface covers a very broad API lifecycle domain: specs, environments, test cases, monitors, mock servers, security, governance, documentation, and team management. Read and write operations are present across most areas, with no obvious missing core functionality.