generate_changelogGenerate a structured changelog for a specification version by comparing it with the previous version. Returns a Markdown changelog. 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 generate a changelog for |
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?
Annotations already declare readOnlyHint=true, so the description only needs to add context. It notes the return format (Markdown) and a prerequisite (project context), but does not disclose behavior when no previous version exists or how failures are handled. Some added value beyond annotations, but not rich.
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?
Two sentences with no wasted words. The core purpose is stated first, and the prerequisite is appended efficiently. Excellent structure.
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 tool with only two parameters and no output schema, the description covers the main purpose, return type, and a prerequisite. It does not detail the changelog structure or edge cases, but given the simplicity, it is reasonably 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 description coverage is 100% for both parameters, so the description adds little beyond what the schema already provides. It does not explain how the parameters affect the changelog generation or any additional constraints. Baseline 3 is appropriate.
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?
States the verb 'Generate' and resource 'changelog' with the specific mechanism of comparing against the previous version. It is clear and distinct from many siblings, though it does not explicitly differentiate from compare_versions or get_breaking_changes.
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?
Mentions 'Requires project context' as a prerequisite, but does not explain when to use this tool versus alternatives like compare_versions or check_schema_drift. No guidance on exclusions or preferred scenarios.
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.