Change history {who, change, entity, kind, when}, newest first; filter kind/who/query.
| Name | Required | Description | Default |
|---|---|---|---|
| who | No | Author email substring (optional) | |
| kind | No | system | context | milestone | screen (optional) | |
| limit | No | Default 30, max 200 | |
| query | No | Keyword filter (optional) | |
| project_id | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / additionalPropertiesRemoved value: -falseInput schema / properties / kind / descriptionPrevious value: -"Filter by kind: system | context | milestone | screen (optional)"New value: +"system | context | milestone | screen (optional)"Input schema / properties / limit / descriptionPrevious value: -"Max rows (default 30, max 200)"New value: +"Default 30, max 200"Input schema / properties / project_id / descriptionRemoved value: -"Project id (from list_projects) to act on; omit = the connector URL's project."Input schema / properties / query / descriptionPrevious value: -"Keyword to match in the change/entity (optional)"New value: +"Keyword filter (optional)"Input schema / properties / who / descriptionPrevious value: -"Filter by author (email substring, optional)"New value: +"Author email substring (optional)"Input schema / requiredRemoved value: -[]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, and the description adds useful behavioral context: newest-first ordering, returned fields, and filter capabilities. This enriches beyond the annotation without 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?
A single, dense sentence packs essential information: output fields, ordering, and filters. No wasted words; front-loaded and easy to parse.
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?
With no output schema, the description compensates by listing the return fields and sort order. It omits pagination details, but the schema provides limit parameters, so overall context is adequate for a low-complexity list tool.
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 has good coverage (80%) with descriptions for most parameters. The description adds clarity by explicitly identifying kind, who, and query as filters, and implicitly excluding limit and project_id from filter semantics, aiding parameter selection.
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 lists change history with specific fields (who, change, entity, kind, when) and sorts newest first. This specific verb+resource combination with field detail effectively distinguishes it from sibling tools like get_history.
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 implies usage via 'filter kind/who/query' but does not explicitly differentiate when to use this tool over alternatives like get_history. It lacks exclusionary guidance or alternative tool references.
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.
Each tool has a clearly distinct purpose, with explicit distinctions between direct actions and proposals via Inbox. The verbs and object types (system, milestone, screen, element, balance) are unique enough that no two tools appear to do the same thing.
Most tool names follow a consistent verb_noun snake_case pattern (get_system, propose_screen, update_element). Minor deviations like 'dedupe', 'search', 'next_task', and 'reorder' are single words or non-verb but remain readable and stylistically compatible.
With 54 tools, this server vastly exceeds the typical MCP scope, hitting the 'extreme mismatch' threshold. Even for a complex domain, the sheer number will overwhelm agents and degrade selection performance.
The tool surface is remarkably complete, covering full lifecycle operations for all major entities, plus import, design generation, drift detection, status reporting, inbox handling, and rejection workflows. No obvious dead ends or missing operations for the stated purpose.