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OpenAkashic

by io.github.szara7678

Search OpenAkashic

search_notes

Search OpenAkashic by note title, tags, summary, and body.

Optional filters:
- kind: restrict to a specific note kind (e.g. "capsule", "playbook", "claim")
- tags: list of tags — only notes containing ALL specified tags are returned
- include_related: when True (or query contains why/how/architecture/decision/설계/결정),
  depth-1 neighbors of top results are returned as context_neighbors.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNoFilter by note kind: 'capsule', 'claim', 'evidence', 'reference', 'playbook', etc.
tagsNoFilter by tags — only notes containing ALL specified tags are returned. Example: ['python', 'benchmark']
limitNoMax number of results to return (default 8)
queryYesSearch terms in plain language. Example: 'Python performance benchmark'
include_relatedNoWhen true, depth-1 neighbors of top results are returned as context_neighbors.

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

A3.9/5.0
Behavior4/5

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

With no annotations, the description relies on text to disclose behavior. It explains the all-tags matching requirement, kind filtering, and the conditional context_neighbors behavior including the query-keyword trigger. It does not explicitly state that the operation is read-only, but 'Search' strongly implies this and the output schema covers return structure.

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 concise and well-structured: a one-sentence purpose followed by a compact bullet list of optional filters. Every sentence adds useful information and there is no redundancy.

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?

For a search tool with an output schema, the description covers all parameter semantics and the key conditional behavior. It is largely complete, though it misses explicit comparison to sibling search tools, which would help in ambiguous contexts.

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

Parameters4/5

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

Schema coverage is 100%, providing a baseline of 3. The description adds value by explaining the kind examples, emphasizing the ALL-tags semantics, and revealing the include_related query-keyword trigger ('why/how/architecture/decision/설계/결정') that is not present in the schema. This improves parameter understanding beyond the structured definitions.

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

Purpose4/5

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

The description clearly states the tool searches OpenAkashic notes by title, tags, summary, and body, using a specific verb and resource. It does not explicitly distinguish from sibling tools like search_akashic or search_and_read_top, so it lacks strong sibling differentiation.

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 optional filters give implied usage guidance for narrowing searches, and include_related behavior is explained. However, there is no explicit guidance on when to use this tool versus alternatives such as search_akashic or list_notes, and no exclusions are mentioned.

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.

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