List typed data artifacts (images, contact, vectors, documents, datasets, capsules). Filter by blob_type. Readable-column reflects caller_kind + caller_id vs audience.
| Name | Required | Description | Default |
|---|---|---|---|
| blob_type | No | ||
| caller_id | No | ||
| caller_kind | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for disclosing behavior. It mentions that the readable-column depends on caller_kind/caller_id and audience, which hints at access control, but it's cryptic and does not explain side effects, permissions, or return behavior. This is insufficient for a read/list tool with zero annotation support.
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 short (two sentences) and avoids redundant filler. However, the second sentence is cryptic and not optimally structured—'Readable-column reflects...' sounds like a field name but is not explained, making it less effective. Still, it earns points for brevity.
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?
There is no output schema, no annotations, and the input schema is bare. The description provides a list of artifact types and one filter hint, but lacks details on return structure, filtering semantics, pagination, error cases, or how caller parameters interact. The cryptic 'readable-column' further reduces completeness. For a simple listing tool, this is under-specified.
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. The description explains blob_type as a filter, which adds some meaning. However, caller_id and caller_kind are not explained at all beyond the vague 'readable-column' reference. The description fails to give a clear semantic role for two of the three parameters, leaving the agent to guess.
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 a specific verb ('List') and resource ('typed data artifacts') with enumerated types (images, contact, vectors, documents, datasets, capsules), which distinguishes it from sibling list tools like list_collection or list_content. The 'readable-column' phrase adds a unique function but is vague and could confuse rather than clarify.
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 context: use this to list typed data artifacts and filter by blob_type. However, it does not explicitly state when to use this versus other listing tools, nor does it provide exclusions or alternative tool names. The filter instruction gives some guidance, but it's minimal.
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 clearly distinct purposes with detailed descriptions, but a few boundaries are fuzzy: leave_message vs ask_human vs leave_comment, and list_content vs list_collection could confuse an agent at first glance. Overall, the descriptions are thorough enough to disambiguate.
The naming convention is predominantly snake_case with verb_noun structure (list_content, read_blob, upsert_skill). Minor deviations exist such as the mysloodsiewnia_* prefix and British spelling in synthesise_persona_patterns, but the pattern is highly recognizable and readable.
At 41 tools, the surface area is very large for a single MCP server and exceeds the 'heavy' threshold. While each tool serves a distinct purpose, the sheer number makes the server feel bloated and harder to navigate; many tools could be grouped or pruned without losing core functionality.
The server covers a broad domain: content read/list, personas, skills management, vault CRUD (except update/delete intentionally), provenance, licensing, memory, and async question/narada workflows. Minor gaps exist like no tool to cancel a narada job or update a memory, but these are workable and the core workflows are well supported.