Retrieve memories from the local fidelis store using natural-language queries. Uses optional LLM filter or zero-LLM default for verbatim recall.
Retrieve memories from the local fidelis store. Two-stage recall with optional LLM filter; zero-LLM by default.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results | |
| query | Yes | Natural-language query |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral disclosure burden. It does add useful context — 'Two-stage recall with optional LLM filter; zero-LLM by default' — revealing internal mechanics beyond a simple retrieval. However, it omits any clarification of the two stages, possible side effects, or output shape, and the optional LLM filter is not backed by any parameter in the schema.
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 a single front-loaded sentence that states the action and key behavioral constraints with zero waste. Every clause adds information.
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?
The tool has no output schema and no annotations, so the description must carry more weight. It fails to explain how the optional LLM filter is toggled (no schema parameter exists for it), what 'two-stage' entails, or how this differs from fidelis_query. This leaves an agent with a meaningful gap in knowing how to invoke and interpret the tool correctly.
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 100%, with both 'query' and 'limit' already documented meaningfully. The description adds no additional parameter-level meaning, so the baseline of 3 applies.
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 uses a specific verb ('Retrieve') and resource ('memories from the local fidelis store'), clearly indicating a read operation. However, it does not explicitly differentiate itself from the sibling tool 'fidelis_query', so an agent may struggle to choose between them without further inspection.
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 provides no guidance on when to use this tool versus fidelis_query or fidelis_orient. It mentions 'zero-LLM by default' as a behavioral trait, but does not state conditions such as 'use when you need deterministic recall' or 'avoid when...' This leaves selection to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.