debug_tool_traceReturn recent MCP tool-call trace events (tool name, user, notes read/written).
| Name | Required | Description | Default |
|---|---|---|---|
| tool | No | ||
| user | No | ||
| limit | No | ||
| errors_only | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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?
With no annotations, the description must disclose behavioral traits. It implies read-only via 'Return' but does not mention permissions, side effects, or what 'notes read/written' means. The absence of such context makes the tool's behavior opaque.
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 one concise sentence, front-loaded with the verb and resource, and avoids unnecessary detail. Every word contributes to understanding the tool's core function.
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?
An output schema exists, so return values need not be explained, but the description still lacks usage guidance and parameter semantics. It is minimally adequate for a simple debug tool, but not complete enough for an agent to invoke it with confidence in various scenarios.
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 0%, so the description must compensate. It fails to explain any of the four parameters (tool, user, limit, errors_only). It references 'tool name, user' as event fields, not as filters, giving no additional meaning beyond the schema's self-explanatory names.
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's action ('Return') and resource ('recent MCP tool-call trace events'), and specifies event content (tool name, user, notes read/written). This distinguishes it from siblings like debug_recent_requests and debug_log_tail.
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?
No guidance is provided on when to use this tool vs. alternatives such as debug_recent_requests or debug_log_tail. The description only states what it does, not when it should be chosen.
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.
Most tools have clear, distinct purposes, but there are overlapping areas such as search_akashic vs search_notes vs search_and_read_top, and confirm_note/dispute_note/review_note which serve related but different review functions. Descriptions are detailed enough to reduce ambiguity, though some boundary cases require careful reading.
All tool names follow a consistent snake_case verb_noun pattern (e.g., list_notes, create_folder, delete_note, move_note). Even debug tools and compound names like search_and_read_top maintain the convention. The only exception is whoami, which is a common standalone verb and does not break the overall pattern.
With 35 tools, the server has a large surface area that could overwhelm agents. The tool count exceeds the 25+ threshold for 'too many' in the rubric, even though the broad domain (notes, folders, search, reviews, publication workflow, debugging) partially justifies the number. The set feels heavy and could benefit from consolidation.
The toolset provides thorough coverage of the knowledge management lifecycle: note CRUD (upsert/read/delete/move), folder management, multiple search modes, review/confirmation/dispute mechanisms, publication workflow, stale note handling, image upload, and debugging utilities. There are no obvious dead ends; every major operation needed to manage and publish notes is represented.