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Debug Recent OpenAkashic Requests

debug_recent_requests

Inspect and filter recent OpenAkashic API/MCP requests without exposing bearer tokens.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNo
kindNo
limitNo
orderNodesc
methodNo
sort_byNotime
request_idNo
status_minNo
path_prefixNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.7/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden for behavioral disclosure. It does disclose a key safety property (no bearer token exposure), which is valuable. However, it does not mention whether the tool is strictly read-only, or describe any side effects, rate limits, or permission requirements. It adds some transparency but leaves gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that front-loads the action and resource. Every word earns its place, and the token safety note is included without verbosity. It is concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

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 are covered elsewhere. The tool is relatively simple with all optional parameters, and the description covers the core purpose and a key safety aspect. However, it lacks guidance on parameter usage and does not reference sibling tools, so it is not fully complete for a 9-parameter tool, though it is adequate given the output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 9 parameters with 0% description coverage, so the description must compensate. It does not describe any of the parameters or how filtering works, apart from the generic word 'filter'. Since none of the parameter meanings are explained, this is a significant gap, earning a low score.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function with a specific verb+resource ('Inspect and filter recent OpenAkashic API/MCP requests') and adds a critical scoping detail ('without exposing bearer tokens'). This distinguishes it from sibling debug tools like debug_log_tail and debug_tool_trace, which focus on logs and tool traces respectively.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when one needs to inspect or filter recent requests, but it does not explicitly state when to prefer this over alternatives like debug_log_tail or debug_tool_trace. No exclusions or alternative guidance are provided, so it earns a middling score for implied rather than explicit usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.5/5.0
Disambiguation4/5

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.

Naming Consistency5/5

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.

Tool Count2/5

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.

Completeness5/5

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.